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16504 papers
Despite all the impressive advances of recurrent neural networks, sequential data is still in need of better modelling. Truncated backpropagation through time (TBPTT), the learning algorithm most widely used in practice, suffers from the truncation bias, which drastically limits its ability to learn long-term dependencies.The Real-Time Recurrent Learning algorithm (RTRL) addresses this issue, but its high computational requirements make it infeasible in practice. The Unbiased Online Recurrent Optimization algorithm (UORO) approximates RTRL with a smaller runtime and memory cost, but with the disadvantage of obtaining noisy gradients that also limit its practical applicability. In this paper we propose the Kronecker Factored RTRL (KF-RTRL) algorithm that uses a Kronecker product decomposition to approximate the gradients for a large class of RNNs. We show that KF-RTRL is an unbiased and memory efficient online learning algorithm. Our theoretical analysis shows that, under reasonable assumptions, the noise introduced by our algorithm is not only stable over time but also asymptotically much smaller than the one of the UORO algorithm. We also confirm these theoretical results experimentally. Further, we show empirically that the KF-RTRL algorithm captures long-term dependencies and almost matches the performance of TBPTT on real world tasks by training Recurrent Highway Networks on a synthetic string memorization task and on the Penn TreeBank task, respectively. These results indicate that RTRL based approaches might be a promising future alternative to TBPTT.
We propose a novel way to create categorized discourse lexicons for multiple languages. We combine information from the Penn Discourse Treebank with statistical machine translation techniques on the Europarl corpus. Using gender profiling as an application, we evaluate our approach by comparing it with an approach using features from a knowledge-based lexicon and with an Rhetorical structure theory (RST) discourse parser. Our experiments are performed on corpora for three languages (English, Dutch, and German) in two genres (news and blogs). We include a feature analysis in which we look for (in)consistencies of discourse features related to male and female authors between the different experimental settings.
Answering questions formulated in natural language is a long standing quest in Artificial Intelligence. However, even formulating the problem in precise terms has proven to be too challenging, which lead many researchers to focus on Multiple-Choice Question Answering problems. One particularly interesting type of the latter problem is solving standardized tests such as university entrance exams. The Exame Nacional do Ensino Médio (ENEM) is a High School level exam widely used by Brazilian universities as entrance exam, and the world's second biggest university entrance examination in number of registered candidates. In this work we tackle the problem of answering purely textual multiple-choice questions from the ENEM. We build on a previous solution that formulated the problem as a text information retrieval problem. In particular, we investigate how to enhance these methods by text augmentation using Word Embedding and WordNet, a structured lexical database where words are connected according to some relations like synonymy and hypernymy. We also investigate how to boost performance by building ensembles of weakly correlated solvers. Our approaches obtain accuracies ranging from 26% to 29.3%, outperforming the previous approach.
Let me begin by thanking the Association for Computational Linguistics and its Executive Committee for conferring on me the great honor of their Lifetime Achievement Award for 2018, which of course I share with all the wonderful students and colleagues that have made many essential contributions to this work over many years.At the heart of the work that I have been pursuing over my research lifetime so far, whether in parsing and sentence processing, spoken language understanding, semantics, or even in musical understanding by machine, there lies a theory of natural language grammar that brings parsing, compositional semantics, statistical modeling, and logical inference into the closest possible relation. This theory of grammar is combinatory, in the sense that its operations are type-dependent and restricted to strictly string-adjacent phonologically or graphologically-realized inputs, and categorial, in the sense that those operands pair a syntactic type with a type-transparent semantic representation or logical form.I'd like to use this opportunity to briefly address three questions that revolve around the theory of grammar, both combinatory and otherwise. The first question concerns the way that Combinatory Categorial Grammar (CCG) was developed with a number of colleagues, over a number of stages and in slightly different forms. The second is an essentially evolutionary question of why natural language grammar should take a combinatory form. The third question is that of what the future holds for CCG and other structural theories of grammar in computational linguistics and NLP in the age of deep learning.I have called this talk "The Lost Combinator" in homage to the Victorian era poem "The Lost Chord," in the hope of suggesting that the theoretical development of CCG has always been empirical, rather than axiomatic, in search of the simplest explanation of the facts of language, rather than for confirmation of linguistic received opinion, however intuitively salient.In the late 1960s (when I was a psychology undergraduate at the University of Sussex under Stuart Sutherland, and then started as a graduate student in artificial intelligence at Edinburgh under Christopher Longuet-Higgins), a broad community of theoretical linguists, psychologists, and computational linguists saw themselves as all working on the same problem, under the definition provided by the "transformational" theory of grammar proposed by Chomsky (1957, 1965), using theories of psycholinguistic processing, language acquisition, and language evolution proposed by Lashley (1951), Miller, Galanter, and Pribram (1960), Miller (1967), and Lenneberg (1967), theories of natural language semantics proposed by Carnap (1956), Montague (1970), and Lewis (1970), and computational models of parsing such as those proposed by Thorne, Bratley, and Dewar (1968) and Woods (1970). (I myself was so convinced that this program would succeed that I believed it was time to apply the same methods to other cognitive faculties, taking as my research project for Ph.D. their application to the interpretation of music by machine, following the lead of Max Clowes [1971] in machine vision.)Almost immediately, this consensus fell apart. First, Chomsky himself was among the first (1965) to recognize that transformational rules, though descriptively revealing, were so expressive as to have little explanatory force, and required many apparently arbitrary constraints (Ross 1967). Second, psychologists realized that psycholinguistic measures of processing difficulty of sentences bore almost no relation to their transformational derivational complexity (Marslen-Wilson 1973; Fodor, Bever, and Garrett 1974). Finally, computational linguists attempting to implement transformational grammars as parsers realized that they were spending all their time implementing even more constraints on rules, in order to limit search arising from overgeneration (Friedman 1971; Gross 1978). (Meanwhile, I realized that the problem had not in fact been solved, and returned to natural language processing, thanks to a postdoc at Sussex with Philip Johnson-Laird.)This disillusion wasn't just a case of internal academic squabbling. There were also a couple of influential reports commissioned by the U.S. and UK governments that ended funding for machine translation (MT) and artificial intelligence (AI) (Pierce et al. 1966; Lighthill 1973). As a result of the second of these reports, which determined that AI was never going to work, PhDs in artificial intelligence like my classmate Geoff Hinton and myself spent ten years or so after graduation in psychology departments (in my case, at the Universities of Sussex and Warwick), until yet another report said AI was working after all and that Britain and the U.S. were falling behind Japan in this vital area. As a result, I could get hired again in computer science, first briefly back at Edinburgh, and then at the University of Pennsylvania (I learned a lesson from this odyssey that I have tried to remember whenever I have been appointed to a committee to report on anything, which is that while reports very rarely do any good, they can very easily do a great deal of harm.)Meanwhile, as a result of these conflicts, the scientific study of language fragmented. The linguists swiftly abjured any responsibility for their grammars ("Competence") bearing any relation to processing ("Performance"). Because the psychologists could hardly abandon Performance, they in turn became agnostic about grammar, retreating to context-free surface grammar (which they tended to refer to as "parsing strategies"), or a touchingly optimistic belief in its emergence from neural models. Meanwhile, the computational linguists (whose machines were growing exponentially in size and speed from the 16K byte core of the machine that supported the whole group when I started my graduate studies, on to levels that would soon permit parsing the entire contents of the then embrionic Web) similarly found that very little of what the linguists and psychologists cared about was usable at scale, and that none of it significantly improved overall performance over very much simpler context-free or even finite-state methods that the linguists had shown to be incomplete. The reason of course was Zipf's law, which means that the events with respect to which the low-level methods are incomplete are off in the long tail.It also became apparent to a few computationalists working on speech, MT, and information retrieval that the real problem was not grammar but ambiguity and its resolution by world-knowledge, and that the solution lay in probabilistic models (Bar-Hillel 1960/1964; Spärck Jones 1964/1986; Wilks 1975; Jelinek and Lafferty 1991) (although it was not immediately apparent how to combine statistical models with grammar-based systems without making obviously false independence assumptions).Nevertheless, as any red-blooded psychologist had always insisted, the divorce between competence and performance that everyone else had accepted did not make any sense. The grammar and the processor had to have evolved in lock-step, as a package deal, for what could be the evolutionary selective advantage of a grammar that you cannot process, or a parser without a grammar?It seemed equally obvious that surface syntax and the underlying semantic or conceptual representation must also be closely related, since the only reasonable basis for child language acquisition that has ever been on offer is that the child attaches language-specific grammar to a universal conceptual relation or "language of mind" (Miller 1967; Bowerman 1973; Wexler and Culicover 1980). It seemed to follow that radically new theories of grammar were needed.Theoretical linguists agree that the central problem for the theory of grammar is discontinuity or non-adjacent dependency between predicates and their arguments:Chomsky described discontinuity in terms of movement, which was known to be formally very unconstrained. By contrast, the ATN parser used in the LUNAR project (Woods, Kaplan, and Nash-Webber 1972) reduced all discontinuity to local operations on registers (Thorne, Bratley, and Dewar 1968; Bobrow and Fraser 1969; Woods 1970).In particular, unbounded wh-dependencies like the above were handled by: (a) putting a pointer into a * or HOLD register as soon as the "which" was encountered without regard to where it would end up; and (b) retrieving the pointer from HOLD when the verb needing an object "had" was encountered without regard to where it had started out. (It also included an ingenious mechanism for coordination called SYSCONJ, which one finds even now being reinvented on an almost yearly basis—cf. Woods [2010].) A * register was also used for wh-constructions within a systemic grammar framework by Winograd (1972, pages 52–53) in his inspiring conversational program SHRDLU.However, it was unclear how to generalize the HOLD register to handle the multiple long-range dependencies, including crossing dependencies, that are found in many other languages. In particular, if the HOLD register were assumed to be a stack, then the ATN becomes a two-stack machine (since we are already implicitly using one stack as a PDA to parse the context-free core grammar).On the computational side at least, the reaction to this impass took two distinct forms. Both reactions took the form of trying to reduce the two major operators of the transformation theory, substitution of immediate constituents, or what is nowadays called "Merge," and "Move," or displacement of non-immediate constituents, to one. On the one hand, Lexical Functional Grammar (Bresnan and Kaplan 1982) and Head-driven Phrase Structure Grammar (Pollard and Sag 1994) followed Kay (1979) in making unification the basis of movement and merger. Because unification can pass information across unbounded structures, this can be thought of as reducing Merge to Move.On the other hand, Generalized Phrase Structure Grammar (Gazdar 1981), Tree Adjoining Grammar (TAG; Joshi and Levy 1982), and Combinatory Categorial Grammar (CCG, Ades and Steedman, 1982) sought to reduce Move to various forms of local merger. In particular, the latter authors suggested that the same stack could be used to capture both long-range dependency and recursion in CCG.1Natural language grammar exhibits discontinuity because semantically language is an applicative system. Applicative systems (such as programming languages) support the twin notions of: (a) Application of a function/concept to an argument/entity; and (b) Abstraction, or the definition of a new function/concept in terms of existing ones.Language is in that sense inherently computational. It seems to follow that linguistics is (or should be) inherently computational as well. (Of course, it does not follow that computationalists have nothing to learn from linguistics.)There are two ways of modeling abstraction in applicative systems: Taking abstraction itself as a primitive operation (λ-calculus, LISP):(2)a.fatherEsau⇒Isaacb.grandfather=λx.father(fatherx)c.grandfatherEsau⇒Abrahamor Defining abstraction in terms of a collection of operators on strictly adjacent terms aka Combinators, such as function composition (Combinatory Calculus, MIRANDA).(3)b′.grandfather=BfatherfatherThe latter does the work of the λ-calculus without using any variables.Despite the resemblance of the "traces" (or copies) and "operators" (or complementizer positions) of the transformational theory to the λ-operators and variables of applicative systems of the first kind, natural language actually seems to be a system of the second, combinatory kind. The evidence stems from the fact that natural language deals with all sorts of fragments that linguists do not normally think of as semantically typable constituents, without the use of any phonologically realized equivalent of variables, such as pronouns:(4)a.Give[Anna books]?and[Manny records]?b.(Mother to child): There's adoggie![Youlike]?#the doggie.c.Food that you must[washVP/NP[before eating](VP∖VP)/NP]?.d.ik denk dat ik1Henk2Cecilia3[zag1leren2zingen3]?These fragments are diagnostic of a Combinatory Calculus based on Bn, T, and the "duplicator" Sn, plus application (Steedman 1987; Szabolcsi 1989; Steedman and Baldridge 2011).2CCG lexicalizes all bounded dependencies, such as passive, raising, control, exceptional case-marking, and so forth, via lexical logical form. All syntactic rules are Combinatory—that is, binary operators over contiguous phonologically realized categories and their logical forms. These rules are restricted by a Combinatory Projection Principle, which in essence says they cannot override the decisions already taken in the language-specific lexicon, but must be consistent with and project unchanged the directionality specified there. All such language-specific information is specified in the lexicon: The combinatory rules like composition are free and universal. All arguments, such as subjects and objects, are lexically type-raised to be functions over the predicate, as if they were morphologically cased as in Latin, exchanging the roles of predicate and argument.All long-range dependencies are established by contiguous reduction of a wh-element, such as (N∖N)/(S/NP), with an adjacent non-standard constituent with category S/NP, formed by rules of function composition.The combinatory rules synchronize composition of the syntactic types shown here with corresponding composition of logical forms (suppressed in the derivations above), to yield the logical forms shown as λ-terms for the resulting nouns N.To capture the construction in Example (4c), whose syntactic derivation we pass over here, we also need rules based on the duplicator S:(7)a."wash X before eating X″b.VP/NP:λx.before(eatx)(washx)≡S(Bbeforeeat)washTo capture constructions like Example (4d) (whose syntactic derivation is similarly suppressed), we also need rules based on second-order composition B2:(8)a."Y saw X teach W to sing.″b.((S∖NP)∖NP)∖NP:λwλxλy.help(teach(singw)wx)xy≡B2sees(Bteachsing)CCG thus reduces the operator move of transformational theory to applications of purely adjacent operators—that is, to recursive combinatory merge.Interestingly, the latest "minimalist" form of the transformational theory has also proposed that Move should be relabeled as an "internal" form of standard or "external" Merge (Chomsky 2001/2004, page 110), though without providing any formal basis for the reduction other than identifying internal Merge as "a grammatical transformation." (If anything deserved the soubriquet "the lost combinator," it would be this notional unitary combination of application and abstraction in a single perfect operator, linking or merging all types, as in the epigraph to this article.)B2 rules allow us to "grow" categories of arbitrarily high valency, such as ((S∖NPy)∖NPx)∖NPw. As we saw earlier, in some Germanic languages like Dutch, Swiss German, and West-Flemish, serial verbs are linearized using such rules to require crossing discontinuous dependencies. Thus, B2 rules give CCG slightly greater than context-free power.Nevertheless, CCG is still not as expressive as movement. In particular, we can only capture permutations that are what is called "separable," where separability is related to the idea of obtaining the permutations by rebracketing and rotating sister nodes (Steedman 2018).For example, for the categories of the form A|B, B|C, C|D, and D, it is obvious by inspection that we cannot recognize the following permutations:(9)i.*B|CDA|BC|Dii.*C|DA|BDB|CThis generalizatiion appears likely to be true cross-linguistically for the components of this form for the NP "These five young boys":(10)i.*Five boys these youngii.*Young these boys fiveTwenty-one of the 22 separable permutations of "These five young boys" are attested (Cinque 2005; Nchare 2012). The two forbidden orders are among the unattested three.3The probability of this happening by chance is the probability of the is the of the number of ways of two of three unattested by the is, about one in a (If the order that unattested so were to be this chance would to about one in number of separable permutations much more in than the number of all example, for around of the permutations are There are obvious for the problem of in machine translation and neural semantic parsing, to which we I to and at first as a still under least, that was what Joshi and then as a of where much of the development of CCG was out. students in a of and Joshi 1987; and that the of Ades and Steedman was by that both CCG and were equivalent to Grammar (Gazdar a new of the by the and of languages by these fell within the of what Joshi called which proposed as a for what could as a theory of natural that they are and and some limit on crossing dependencies. the is much much than the including the multiple free languages and even the languages of so it seems to and CCG as with to the its CCG was assumed to be as a grammar for parsing, because of the derivational ambiguity by and the combinatory rules, these also under and as any grammar with the same as CCG the same of in the parser it is there in the In this is just another in the of derivational ambiguity that all natural language and can be handled by the same statistical models as other particular, the dependency models by and are and Steedman and CCG is also to parsing with which can be using and long and Steedman and is now used in those that for between semantic and syntactic processing, such as machine translation and and machine and parsing and Steedman 1987; and et al. et al. et al. and semantic parser and 2005; et al. et al. of my work with the same has returned to their application in musical and and Steedman and shown that CCG grammars of the same and parsing models of the same statistical are required there as It is only in the of their compositional semantics that music and language very than on this of work in like to by two more The first is an evolutionary should natural language be a combinatory in the first The second is a question about the future development of CCG and other grammar-based theories to be to NLP in the age of deep and recursive neural take these questions in order in the two language like a combinatory applicative system because T, and evolved in to support of before there was any language (Steedman like need and are for of need to to make can form the that you need to before is to that they allow and some other can with like B2 are to make with arbitrary of including and including other whose is yet to be to be to do the the and problem, using like to in order to that are of to of and so such as of and can apply to a and much work that the and other have to be there in the already for the to be to with was to that from the even if had the problem of can be as the problem of search for a of in a or of possible such search has the same recursive as parser example, there are both and for the The latter a more in evolutionary as a mechanism that is to both semantic interpretation and parsing, rather than the evolution of like the the for linguistic as as the operators for competence grammar, the two to as what was to above as an evolutionary hope to have convinced you that CCG grammars are both and as as semantic parsers that are to parse the like other CCG parsers are by the of parsing models based on only a of no how we using and the constructions they are dependencies and so as we have and off in the long As a parsers are to on performance overall by using models et al. says more about the of grammars and parsers than about the of deep in the of semantic parser for arbitrary such as and Steedman I would that CCG and other grammar-based parsers have already been by of deep neural and and the question of whether models for applications in are because we have to the universal semantic that allow the child to CCG for natural languages and that we to be using both in semantic parser and in Because the language is any of linguistic logical like the universal language of it be more with and to semantic parsers for by deep neural force, rather than by CCG semantic parser is it possible that the problem of parsing could be by neural by stack et al. et al. actually learn as has been seems likely that semantic parsers and neural machine translation to have difficulty with long-range because the evidence for their is so example, both and as a case, verb categories or a complementizer I is actually by which says that if you are an language with like and then you not in be to in languages like or languages like and German, you be to both subjects and objects, or at the time of a translation system no of learned these syntactic from a we get an sentence whose to translation means is the that the us the a is the that the us to the if we with we (which back into the a is the that the us holds the a we with using a the translation again an that is is the that they said had the is the they said they the contrast, CCG parsers do rather on and Steedman Steedman, and which is a construction that to be rather determined by the parsing methods are similarly when with long-range even when the sentence is in this think that the the and that it is et think that the the and it is in at least, these constructions could be learned by grammar-based semantic parser from using the methods of et al. and et al. make it likely that there be a need for in like where long-range dependencies like deep and are here to The future in parsing for such lies with systems using neural for and grammars for problem in NLP the fact that natural language understanding inference as as semantics, and we have no idea of the representation question is the almost it many it is almost equally to the information in a form that is not immediately with the form of the example, sentences like the following a different a rather than a an an a a and at a representation language that is we are using CCG parsers to the for between in order to consistent of between over of the same types, using over then an it and it under such as et al. of in the then that can be to a single relation and Steedman can be across from multiple languages and Steedman can then the semantics for relation with the and the entire using this now both and semantic an with the as and the as questions the in this we parse questions into the same semantics which is now the language of the the we use the and the and the following anything that in the then the the of anything is in the in the this to work, we need to be nodes in their in both and then be to like in of a semantic in the language of the need to learn between semantic and the language of the this project is the the function of a of the semantic in semantic like those of and and Steedman and while the form a similarly of the of Carnap and Fodor, Fodor, and Garrett semantic are essentially but with the advantage that they can be with logical operators such as and for the of semantic underlying natural language semantics in to another different to semantics that to use reduced of to using operations such as and and in of compositional It is an question whether can be with to of a and et al. It is likely that some of be here to the of the of the long like and work in do they work in In particular, can they learn all the syntactic in the long like and crossing in a way that support semantic they are not actually but are a finite-state or a then by on as for natural language processing, we are in of of the computational linguistic project of also providing computational of language and if we that like is a real and that learn their first language by of the sentences of their language the of the universal language of we still the of what that universal semantic language not get an to that question we can above using and and such as as for the language of to use machine for what it is such variables and their for use in a natural language work was supported in by a Award and a a University of Edinburgh and my and the and all my and students over many
BACKGROUND: Neuroplastic underpinnings of meditation-induced changes in affective processing are largely unclear. METHODS: We included healthy older participants in an active-controlled experiment. They were involved a meditation training or a control relaxation training of eight weeks. Associations between behavioral and neural morphometric changes induced by the training were examined. RESULTS: The meditation group demonstrated a change in valence perception indexed by more neutral valence ratings of positive and negative affective images. These behavioral changes were associated with synchronous structural enlargements in a prefrontal network involving the ventromedial prefrontal cortex and the inferior frontal sulcus. In addition, these neuroplastic effects were modulated by the enlargement in the inferior frontal junction. In contrast, these prefrontal enlargements were absent in the active control group, which completed a relaxation training. Supported by a path analysis, we propose a model that describes how meditation may induce a series of prefrontal neuroplastic changes related to valence perception. These brain areas showing meditation-induced structural enlargements are reduced in older people with affective dysregulations. CONCLUSION: We demonstrated that a prefrontal network was enlarged after eight weeks of meditation training. Our findings yield translational insights in the endeavor to promote healthy aging by means of meditation.
We present ongoing work on data-driven parsing of German and French with Lexicalized Tree Adjoining Grammars. We use a supertagging approach combined with deep learning. We show the challenges of extracting LTAG supertags from the French Treebank, introduce the use of leftand right-sister-adjunction, present a neural architecture for the supertagger, and report experiments of n-best supertagging for French and German.
We propose a novel approach to Vietnamese word segmentation. Our approach is based on the Single Classification Ripple Down Rules methodology (Compton and Jansen, 1990), where rules are stored in an exception structure and new rules are only added to correct segmentation errors given by existing rules. Experimental results on the benchmark Vietnamese treebank show that our approach outperforms previous state-of-the-art approaches JVnSegmenter, vnTokenizer, DongDu and UETsegmenter in terms of both accuracy and performance speed. Our code is open-source and available at: https://github.com/datquocnguyen/RDRsegmenter.
A dependency parser generates both a syntactic structure and a shallow semantic structure of a sentence. It is a fundamental component of natural language processing (NLP) based pipelines, which are critical to facilitate research using the Electronic Health Records (EHR). However, current works mainly apply parsers developed in the general English domain to clinical text. There are no formal evaluations and comparisons of deep learning based dependency parsers in the medical domain. No state-of-the-art dependency parsing performance has been established on clinical text, either. In this study, we investigated the performance of four state-ofthe-art deep learning based dependency parsers, Stanford parser, Bist-parser, dependency_tf parser and jPTDP parser, respectively. Experiments for evaluation are conducted on two datasets: (1) The MiPACQ Treebank and (2) A Treebank of progress notes. Our results showed that the original parsers achieved lower performance in clinical text compared to general English text. After retraining on the clinical Treebank, all parsers obtained better performance. Besides, using word embeddings from Gigaword and MIMICIII yielded comparable performance. Interestingly, the transition-based parsers demonstrated stronger generalizability on different treebanks than the graph-based parsers. Overall, Bist-parser achieved the best performance on MiPACQ (88.95% UAS, 92.69% LS, 86.10% LAS). Stanford parser achieved the best performance on progress notes (84.01% UAS, 89/97% LS, 80.72% LAS).
This paper describes our approach to developing the Turkish PropBank by adopting the semantic role-labeling guidelines of the original PropBank and using the translation of the English Penn-TreeBank as a resource. We discuss the semantic annotation process of the PropBank and language-specific cases for Turkish, the tools we have developed for annotation, and quality control for multiuser annotation. In the current phase of the project, more than 9500 sentences are semantically analyzed and predicate-argument information is extracted for 1330 verbs and 1914 verb senses. Our plan is to annotate 17,000 sentences by the end of 2017.
The syntax of newspaper headlines in English displays features which, on a superficial level, set it apart from the norm of Standard English. This, however, is not to say that headlinese stands outside the notion of a linguistic norm: drawing from a corpus of various headlines, and looking at the specific fields of determiners and verb tenses, this article explores how the English of newspaper headlines constitutes a norm in itself which actually builds on the very potentialities of Standard English. Such an extension of the syntactic possibilities of English is designed to fit the pragmatic purpose of headlines, i.e. heighten the relevance of the newspaper article to the reader.
The contents and structure of semantic memory have been the focus of much recent research, with major advances in the development of distributional models, which use word co-occurrence information as a window into the semantics of language. In parallel, connectionist modeling has extended our knowledge of the processes engaged in semantic activation. However, these two lines of investigation have rarely been brought together. Here, we describe a processing model based on distributional semantics in which activation spreads throughout a semantic network, as dictated by the patterns of semantic similarity between words. We show that the activation profile of the network, measured at various time points, can successfully account for response times in lexical and semantic decision tasks, as well as for subjective concreteness and imageability ratings. We also show that the dynamics of the network is predictive of performance in relational semantic tasks, such as similarity/relatedness rating. Our results indicate that bringing together distributional semantic networks and spreading of activation provides a good fit to both automatic lexical processing (as indexed by lexical and semantic decisions) as well as more deliberate processing (as indexed by ratings), above and beyond what has been reported for previous models that take into account only similarity resulting from network structure.
Despite all the impressive advances of recurrent neural networks, sequential data is still in need of better modelling. Truncated backpropagation through time (TBPTT), the learning algorithm most widely used in practice, suffers from the truncation bias, which drastically limits its ability to learn long-term dependencies.The Real Time Recurrent Learning algorithm (RTRL) addresses this issue, but its high computational requirements make it infeasible in practice. The Unbiased Online Recurrent Optimization algorithm (UORO) approximates RTRL with a smaller runtime and memory cost, but with the disadvantage of obtaining noisy gradients that also limit its practical applicability. In this paper we propose the Kronecker Factored RTRL (KF-RTRL) algorithm that uses a Kronecker product decomposition to approximate the gradients for a large class of RNNs. We show that KF-RTRL is an unbiased and memory efficient online learning algorithm. Our theoretical analysis shows that, under reasonable assumptions, the noise introduced by our algorithm is not only stable over time but also asymptotically much smaller than the one of the UORO algorithm. We also confirm these theoretical results experimentally. Further, we show empirically that the KF-RTRL algorithm captures long-term dependencies and almost matches the performance of TBPTT on real world tasks by training Recurrent Highway Networks on a synthetic string memorization task and on the Penn TreeBank task, respectively. These results indicate that RTRL based approaches might be a promising future alternative to TBPTT.
This paper presents initial experiments in data-driven morphological analysis for Finnish using deep learning methods. Our system uses a character based bidirectional LSTM and pretrained word embeddings to predict a set of morphological analyses for an input word form. We present experiments on morphological analysis for Finnish. We learn to mimic the output of the OMorFi analyzer on the Finnish portion of the Universal Dependency treebank collection. The results of the experiments are encouraging and show that the current approach has potential to serve as an extension to existing rule-based analyzers.
An important research field in the area of text mining is text categorization. Most of the real world documents are multi-label in nature. In this paper we have proposed a novel method for automated and effective categorization of multi-label text documents. The proposed method is based on lexical and semantics concepts. Tokens are identified in the text documents using standard IEEE taxonomy. To analyze the semantic relationships between tokens, standard lexical database WordNet is used. The proposed method is tested on a dataset of 150 research articles of computer science domain from IEEE Xplore digital library. It has shown a significantly good performance with an accuracy of 75%.
We present a complete, automated, and efficient approach for utilizing valency analysis in making dependency parsing decisions. It includes extraction of valency patterns, a probabilistic model for tagging these patterns, and a joint decoding process that explicitly considers the number and types of each token's syntactic dependents. On 53 treebanks representing 41 languages in the Universal Dependencies data, we find that incorporating valency information yields higher precision and F1 scores on the core arguments (subjects and complements) and functional relations (e.g., auxiliaries) that we employ for valency analysis. Precision on core arguments improves from 80.87 to 85.43. We further show that our approach can be applied to an ostensibly different formalism and dataset, Tree Adjoining Grammar as extracted from the Penn Treebank; there, we outperform the previous stateof-the-art labeled attachment score by 0.7. Finally, we explore the potential of extending valency patterns beyond their traditional domain by confirming their helpfulness in improving PP attachment decisions. 1
Standard neural network architectures are non-linear only by virtue of a simple element-wise activation function, making them both brittle and excessively large. In this paper, we consider methods for making the feed-forward layer more flexible while preserving its basic structure. We develop simple drop-in replacements that learn to adapt their parameterization conditional on the input, thereby increasing statistical efficiency significantly. We present an adaptive LSTM that advances the state of the art for the Penn Treebank and WikiText-2 word-modeling tasks while using fewer parameters and converging in less than half as many iterations.
Wikipedia has a strong norm of writing in a 'neutral point of view' (NPOV). Articles that violate this norm are tagged, and editors are encouraged to make corrections. But the impact of this tagging system has not been quantitatively measured. Does NPOV tagging help articles to converge to the desired style? Do NPOV corrections encourage editors to adopt this style? We study these questions using a corpus of NPOV-tagged articles and a set of lexicons associated with biased language. An interrupted time series analysis shows that after an article is tagged for NPOV, there is a significant decrease in biased language in the article, as measured by several lexicons. However, for individual editors, NPOV corrections and talk page discussions yield no significant change in the usage of words in most of these lexicons, including Wikipedia's own list of 'words to watch.' This suggests that NPOV tagging and discussion does improve content, but has less success enculturating editors to the site's linguistic norms.
Abstract This chapter addresses interlingual relations between constructions. The perspective is contrastive rather than typological, with an aim towards multilingual constructicon development. Building on previous work on the alignment of frame-based multilingual lexical databases, we explore possibilities and problems for multilingual constructicography. By comparing the dataset of Berkeley’s English constructicon to Brazilian Portuguese and Swedish, we discuss the alignment of constructicons vis á vis the existence of more or less equivalent constructions or the deployment of different linguistic strategies in different languages.
Emotion mobilizes and demobilizes a social movement while the movement itself impacts people's emotions. An evidence base is needed for developing timely emotion-focused counseling services for affected populations worldwide. This experience sampling study investigated the impact of a social movement on everyday emotional reactivity, variability, instability, and persistence, and whether and how these predicted subsequent psychological distress and well-being among 108 community-dwelling adults. Fifty-four participants reported momentary events and emotions 5 times daily over 7 days during the Umbrella Movement in Hong Kong. An age-matched, sex-matched comparison group (n = 54) participated during a period without major social/political movement. Psychological distress and well-being were assessed at baseline (Time 1 [T1]) and 3-month follow-up (Time 2 [T2]). Hierarchical linear modeling revealed lower valence ratings of positive events during the Umbrella Movement. Lower positive emotional reactivity and higher negative emotional reactivity in positive events were reported during the movement. Controlling for demographics and T1 scores, the positive association between negative emotional reactivity in positive events and T2 depressive symptoms, and the inverse association between positive emotional instability and T2 positive emotions, were significant only among the comparison group. The positive association between positive emotions in positive events and T2 life satisfaction was significant only among the Umbrella Movement group. People could experience significant changes in their emotions in everyday life during a social movement, even if it is short and relatively nonviolent like the Umbrella Movement. Maximizing positive emotions in positive events during a social movement can uniquely contribute to higher subsequent psychological well-being. (PsycINFO Database Record
Deep neural networks have advanced the state of the art in numerous fields, but they generally suffer from low computational efficiency and the level of improvement compared to more efficient machine learning models is not always significant. We perform a thorough PoS tagging evaluation on the Universal Dependencies treebanks, pitting a state-of-the-art neural network approach against UDPipe and our sparse structured perceptron-based tagger, efselab. In terms of computational efficiency, efselab is three orders of magnitude faster than the neural network model, while being more accurate than either of the other systems on 47 of 65 treebanks.
Dependency distance minimization (DDM) is found as a universal quantitative property of natural languages. To investigate whether second language learners develop their interlanguage system under the pressure of DDM, we selected 367 Chinese EFL learners of nine consecutive grades, built one second language dependency treebank and two corresponding random treebanks and fitted different probability distribution models to dependency distances. It was found that: (1) The mean dependency distance (MDD) of interlanguage increases significantly across nine grades and the MDD of high-level learners doesn't reach the level of English native speakers. (2) The MDDs of interlanguage at different learning phases are significantly lower than their corresponding random languages (RL1 and RL2), indicating that learners develop their English proficiency under the pressure of DDM. (3) The distribution of dependency distances of RL1 cannot fit the Zipf-Alekseev distribution, but that of RL2 can. The parameters in the Zipf-Alekseev distribution of RL2 have no correlation with learners' language proficiency.
Temporal lobe epilepsy with amygdala enlargement (TLE-AE) is increasingly recognized as a distinct adult electroclinical syndrome. However, functional consequences of morphological alterations of the amygdala in TLE-AE are poorly understood. Here, two emotional stimulation designs were employed to investigate subjective emotional rating and skin conductance responses in a sample of treatment-naïve patients with suspected or confirmed autoimmune TLE-AE (n = 12) in comparison to a healthy control group (n = 16). A subgroup of patients completed follow-up measurements after treatment. As compared to healthy controls, patients with suspected or confirmed autoimmune TLE-AE showed markedly attenuated skin conductance responses and arousal ratings, especially pronounced for anxiety-inducing stimuli. The degree of right amygdala enlargement was significantly correlated with the degree of autonomic arousal attenuation. Furthermore, a decline of amygdala enlargement following prompt aggressive immunotherapy in one patient suffering from severe confirmed autoimmune TLE-AE with a very recent clinical onset was accompanied by a significant improvement of autonomic responses. Findings suggest dual impairments of autonomic and cognitive discrimination of stimulus arousal as hallmarks of emotional processing in TLE-AE. Emotional responses might, at least partially, recover after successful treatment, as implied by first single case data.
OBJECTIVES: Sufficient prefrontal top-down control of limbic affective areas, especially the amygdala, is essential for successful effortful emotion regulation (ER). Difficulties in effortful ER have been seen in patients with bipolar disorder (BD), which could be suggestive of a disturbed prefrontal-amygdala regulation circuit. The aim of this study was to investigate whether BD patients show abnormal effective connectivity from the prefrontal areas to the amygdala during effortful ER (reappraisal). METHODS: Forty participants (23 BD patients and 17 healthy controls [HC]) performed an ER task during functional magnetic resonance imaging. Using dynamic causal modeling, we investigated effective connectivity from the dorsolateral prefrontal cortex (DLPFC) and ventrolateral prefrontal cortex (VLPFC) to the amygdala, as well as connectivity between the DLPFC and VLPFC during reappraisal. RESULTS: Both BD patients and HC showed decreased negative affect ratings following reappraisal compared to attending negative pictures (P <.001). There were no group differences (P =.10). There was a differential modulatory effect of reappraisal on the connectivity from the DLPFC to amygdala between BD patients and HC (P =.04), with BD patients showing a weaker modulatory effect on this connectivity compared to HC. There were no other group differences. CONCLUSION: The disturbance in BD patients in effective connectivity from the DLPFC to the amygdala while reappraising is indicative of insufficient prefrontal control. This impairment should be studied further in relation to cycling frequency and polarity of switches in BD patients.
Abstract This paper presents a treebank-based study of the effect the text form (prose vs. verse) has on the course of two grammatical changes in Medieval French: the loss of null subjects and the loss of OV word order. By means of statistical analysis, we demonstrate that naive estimates of the spread of overt subjects and VO orders give the impression that there is a significant difference between the rates of development in prose vs. verse. By contrast, estimates based on an abstract grammar competition model which distinguishes between grammar-ambiguous surface forms (overt personal subjects, null subjects in coordination contexts) and grammar-unambiguous surface forms (overt expletive subjects, null subjects in non-coordination contexts) show prose-verse parallelism, prose having an earlier change onset, in line with traditional intuitions. At a more general level, these results suggest that the product of the interaction of a particular grammar with universal pragmatic laws is constant, which can be observed if the factors responsible for variation in grammatical choices are controlled for.
In this paper, we explore the linguistic factors that influence an author's choice of discourse connectives in the production of a coherent text. We focus on the competition between so-called primary connectives (grammaticalized and mostly one-word expressions such as therefore) and secondary connectives (not yet fully grammaticalized compositional discourse phrases such as for this reason). We attempt to describe the linguistic constraints on and preferences in connective selection. The analysis is based on manually annotated data from the Prague Discourse Treebank 2.0 (PDiT), which contains almost 50000 sentences from Czech newspaper texts. We demonstrate that discourse connectives are used in accordance with the economy principle in language, i.e. authors aim to achieve the maximal result with minimal effort. They most frequently choose short and semantically more generalized primary connectives. However, in cases where the discourse relations can be misunderstood, authors prefer more complex and specific structures.
Nous nous intéressons à la performance du langage et aux contraintes causées par la capacité cognitive. Miller (1956) a montré que la mémoire à court-terme est limitée à 7±2 éléments, mais aujourd’hui, cette limite est actualisée à 4 selon Cowan (2001). La première question que nous nous posons est: comment mesurer la complexité syntaxique à partir des données observables considérant ce contrainte de la mémoire à court-terme? Deux autres questions que nous voudrions résoudre sont: quelles sont les relations syntaxiques qui créent vraiment de la complexité? Lorsqu’il s’agit d’une structure syntaxique plus (ou moins) complexe, quels sont les phénomènes qu’on pourrait observer dans nos données?Pour répondre à la première question, je me concentre sur des différents mesures du flux de dépendances (Kahane et al., 2017; Yan & Kahane, 2018). Notre hypothèse est que la mesure du flux à partir de treebanks annotés en syntaxe de dépendance permettraient de vérifier les hypothèses sur la mémoire à court-terme. Les deux autres questions alimentent mon actuel travail de thèse sur les configurations du flux de dépendance, ainsi que les liens entre flux et structure prosodique.
Wikipedia has a strong norm of writing in a "neutral point of view" (NPOV). Articles that violate this norm are tagged, and editors are encouraged to make corrections. But the impact of this tagging system has not been quantitatively measured. Does NPOV tagging help articles to converge to the desired style? Do NPOV corrections encourage editors to adopt this style? We study these questions using a corpus of NPOV-tagged articles and a set of lexicons associated with biased language. An interrupted time series analysis shows that after an article is tagged for NPOV, there is a significant decrease in biased language in the article, as measured by several lexicons. However, for individual editors, NPOV corrections and talk page discussions yield no significant change in the usage of words in most of these lexicons, including Wikipedia's own list of "words to watch." This suggests that NPOV tagging and discussion does improve content, but has less success enculturating editors to the site's linguistic norms.
In this paper, we describe the annotation and development of Telugu treebank following the Universal Dependencies framework. We manually annotated 1328 sentences from a Telugu grammar textbook and the treebank is freely available from Universal Dependencies version 2.1.1 In this paper, we discuss some language specific annotation issues and decisions; and report preliminary experiments with POS tagging and dependency parsing. To the best of our knowledge, this is the first freely accessible and open dependency treebank for Telugu.
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Frequency distribution of words, syntax and semantics in many languages abides by certain laws. However, because of the shortage of discourse corpora, few studies have examined whether the frequency of discourse relations follows some distributional patterns. Although there is some research based on the Rhetorical Structure Theory discourse treebank (RST-DT), each of these studies is limited to a single language. Otherwise to the RST-DT, the Penn Discourse Treebank (PDTB), adopting another annotation system, has had an enormous influence on the study of discourse structure and discourse annotation. Discourse corpora in other languages, such as Chinese, Hindi, Turkish, Czech and Arabic have been annotated following PDTB style. With the data from these discourse treebanks, we find that the rank-frequency of discourse relations follow the same pattern and that these languages share significant similarities in using semantic relations to organize the discourse. It is evidenced in our research that humans assume the relationship between two consecutive sentences is a causal connection or expansion link for fewer connectives used, but the relation of contrast is the most marked by connectives. This research will be of significance for understanding the homogeneity of discourse structure across languages.
The dorsolateral prefrontal cortex (DLPFC) plays a key role in the modulation of affective processing. However, its specific role in the regulation of neurocognitive processes underlying the interplay of affective perception and visual awareness has remained largely unclear. Using a mixed factorial design, this study investigated effects of inhibitory continuous theta-burst stimulation (cTBS) of the right DLPFC (rDLPFC) compared to an Active Control condition on behavioral (N=48) and electroencephalographic (N=38) correlates of affective processing in healthy Chinese participants. Event-related potentials (ERPs) in response to passively viewed subliminal and supraliminal negative and neutral natural scenes were recorded before and after cTBS application. We applied minimum-norm approaches to estimate the corresponding neuronal sources. On a behavioral level, we found evidence for reduced emotional interference by, and less negative and arousing ratings of negative supraliminal stimuli following rDLPFC inhibition. We found no evidence for stimulation effects on self-reported mood or the behavioral discrimination of subliminal stimuli. On a neurophysiological level, rDLPFC inhibition relatively enhanced occipito-parietal brain activity for both subliminal and supraliminal negative compared to neutral images (112-268ms; 320-380ms). The early onset and localization of these effects suggests that rDLPFC inhibition boosts automatic processes of ‘emotional attention’ independently of visual awareness. Further, our study reveals the first available evidence for a differential influence of rDLPFC inhibition on subliminal versus supraliminal neural emotion processing. Explicitly, our findings indicate that rDLPFC inhibition selectively enhances rather late (292-360ms) activity in response to supraliminal negative images. We tentatively suggest that this differential frontal activity likely reflects enhanced awareness-dependent down-regulation of negative scene processing eventually leading to facilitated disengagement from and less negative and arousing evaluations of negative supraliminal stimuli.
Automatic syntactic parsing for question constructions is a challenging task due to the paucity of training examples in most treebanks. The near absence of question constructions is due to the dominance of the news domain in treebanking efforts. In this paper, we compare two synthetic low-cost question treebank creation methods with a conventional manual high-cost annotation method in the context of three domains (news questions, political talk shows, and chatbots) for Modern Standard Arabic, a language with relatively low resources and rich morphology. Our results show that synthetic methods can be effective at significantly reducing parsing errors for a target domain without having to invest large resources on manual annotation; and the combination of manual and synthetic methods is our best domain-independent performer.
Prestige and dominance are thought to be two evolutionarily distinct routes to gaining status and influence in human social hierarchies. Prestige is attained by having specialist knowledge or skills that others wish to learn, whereas dominant individuals use threat or fear to gain influence over others. Previous studies with groups of unacquainted students have found prestige and dominance to be two independent avenues of gaining influence within groups. We tested whether this result extends to naturally-occurring social groups. We ran an experiment with 30 groups of 5 people from Cornwall, UK (n=150). Participants answered general knowledge questions individually and as a group, and subsequently nominated a team representative to answer bonus questions to win money on behalf of the team. Participants then rated all other team-mates anonymously on scales of prestige, dominance, likeability and influence on the task. Using a model comparison approach with Bayesian multi-level models, we found that prestige and dominance ratings were predicted by influence ratings on the task, replicating previous studies. However, prestige and dominance ratings did not predict who was nominated as group representative. Instead, participants nominated team members with the highest individual quiz scores, despite this information being unavailable to them. Interestingly, team members who were initially rated as being high status in the group, such as a team captain or group administrator, had higher ratings of both dominance and prestige than other group members. In contrast, those who were initially rated as someone from whom group members would like to learn had higher prestige ratings, but not higher dominance ratings, supporting the claim that prestige reflects social learning opportunities. Our results suggest that prestige and dominance hierarchies do become established in naturally occurring human social groups, but that these hierarchies may be more domain-specific and less flexible than we anticipated.
Analyzing public opinions towards products, services and social events is an important but challenging task. Despite the remarkable successes of deep neural networks in sentiment analysis, these approaches do not make full use of the prior sentiment knowledge (e.g., sentiment lexicon, negation words, intensity words). In this paper, we propose a Sentiment-Aware Attention Network (SAAN) to boost the performance of sentiment analysis, which adopts a three-step strategy to learn the sentiment-specific sentence representation. First, we employ a word-level mutual attention mechanism to model word-level correlation. Next, a phrase-level convolutional attention is designed to obtain phrase-level correlation. Finally, a sentence-level multi-head attention mechanism is proposed to capture various sentimental information from different subspaces. The experiments on Movie Review (MR) and Stanford Sentiment Treebank (SST) show that our model consistently outperform the previous methods for sentiment analysis.
Abstract This paper develops and evaluates a theory of status belief transfer, the process by which gender status beliefs differentially affect the evaluations of products made by men and women. We conduct three online experiments to evaluate this theory. In Study 1, we gathered 50 product categories from a large online retailer and had participants rate each product’s association with femininity and masculinity. We find evidence of the pervasiveness of gender-typing in product markets. In Studies 2 and 3, we simulate male-typed and female-typed product markets (craft beer and cupcakes, respectively). In the male-typed product market, a craft beer described as produced by a woman is evaluated more negatively than the same product described as produced by a man. Consistent with our predictions, we further find that if the beer is conferred external status via an award, the evaluation of the beer made by a woman improves by a greater magnitude than the same beer made by a man. In the female-typed product market of cupcakes, the producer’s gender does not affect ratings. Together, the two studies provide evidence of an asymmetric negative bias: products made by women are disadvantaged in male-typed markets, but products made by men are not disadvantaged in female-typed markets. These studies also provide compelling evidence of status belief transfer from producers to their products. We draw out the implications of these findings and suggest ways that gender biases in product markets can be reduced.
We propose a novel neural network model for joint part-of-speech (POS) tagging and dependency parsing. Our model extends the well-known BIST graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating a BiLSTM-based tagging component to produce automatically predicted POS tags for the parser. On the benchmark English Penn treebank, our model obtains strong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+% absolute improvements to the BIST graph-based parser, and also obtaining a state-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimental results on parsing 61 "big" Universal Dependencies treebanks from raw texts show that our model outperforms the baseline UDPipe (Straka and Strakov, 2017) with 0.8% higher average POS tagging score and 3.6% higher average LAS score. In addition, with our model, we also obtain state-of-the-art downstream task scores for biomedical event extraction and opinion analysis applications.
There is a sizable literature on the causes and effects of candidate positioning in elections. An implication of this research is that candidates present clear issue positions to the electorate and citizens then make voting decisions based on this information. However, if candidates are ambiguous in the positions they take, this may impair voters’ decision-making and prompt voters to punish them for inconsistency. Although there is a growing literature on the effects of candidate and party ambiguity, consensus on the implications of ambiguity for candidates and voters is yet to be achieved. Using data from the 2010 House elections, we find that candidate ambiguity undermines voters’ ability to vote consistent with the spatial logic just as Downs speculated. We also find, in contrast to Downs, that voters punish rather than reward candidate ambiguity. We suggest that a possible mechanism is in voters’ valence ratings of candidates.
Sequence-to-sequence constituency parsing casts the tree structured prediction problem as a general sequential problem by top-down tree linearization,and thus it is very easy to train in parallel with distributed facilities. Despite its success, it relies on a probabilistic attention mechanism for a general purpose, which can not guarantee the selected context to be informative in the specific parsing scenario. Previous work introduced a deterministic attention to select the informative context for sequence-to-sequence parsing, but it is based on the bottom-up linearization even if it was observed that top-down linearization is better than bottom-up linearization for standard sequence-to-sequence constituency parsing. In this paper, we thereby extend the deterministic attention to directly conduct on the top-down tree linearization. Intensive experiments show that our parser delivers substantial improvements over the bottom-up linearization in accuracy, and it achieves 92.3 Fscore on the Penn English Treebank section 23 and 85.4 Fscore on the Penn Chinese Treebank test dataset, without reranking or semi-supervised training.
We propose a novel neural network model for joint part-of-speech (POS) tagging and dependency parsing. Our model extends the well-known BIST graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating a BiLSTM-based tagging component to produce automatically predicted POS tags for the parser. On the benchmark English Penn treebank, our model obtains strong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+% absolute improvements to the BIST graph-based parser, and also obtaining a state-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimental results on parsing 61 "big" Universal Dependencies treebanks from raw texts show that our model outperforms the baseline UDPipe (Straka and Straková, 2017) with 0.8% higher average POS tagging score and 3.6% higher average LAS score. In addition, with our model, we also obtain state-of-the-art downstream task scores for biomedical event extraction and opinion analysis applications. Our code is available together with all pre-trained models at: https://github.com/datquocnguyen/jPTDP
Failing to recognize one's mirror image can signal an abnormality in one's sense of self. In dissociative identity disorder (DID), individuals often report that their mirror image can feel unfamiliar or distorted. They also experience some of their own thoughts, emotions, and bodily sensations as if they are nonautobiographical and sometimes as if instead, they belong to someone else. To assess these experiences, we designed a novel backwards masking paradigm in which participants were covertly shown their own face, masked by a stranger's face. Participants rated feelings of familiarity associated with the strangers' faces. 21 control participants without trauma-generated dissociation rated masks, which were covertly preceded by their own face, as more familiar compared to masks preceded by a stranger's face. In contrast, across two samples, 28 individuals with DID and similar clinical presentations (DSM-IV Dissociative Disorder Not Otherwise Specified type 1) did not show increased familiarity ratings to their own masked face. However, their familiarity ratings interacted with self-reported identity state integration. Individuals with higher levels of identity state integration had response patterns similar to control participants. These data provide empirical evidence of aberrant self-referential processing in DID/DDNOS and suggest this is restored with identity state integration.
Are gender differences in emotion culturally universal? To answer this question, the current study compared gender differences in emotional arousal (intensity) ratings for negative and positive pictures from the International Affective Picture System (IAPS) across cultures (Chinese vs. German culture) and age (younger vs. older adults). The raters were 53 younger Germans (24 women), 53 older Germans (28 women), 300 younger Chinese (176 women), and 126 older Chinese (86 women). The results showed that gender differences in arousal ratings were moderated by culture and age: Chinese women reported higher arousal for both negative and positive pictures compared with Chinese men; German women reported higher arousal for negative pictures, but lower arousal for positive pictures compared with German men. Moreover, the gender differences were larger for older than younger adults in the Chinese sample but smaller for older than younger adults in the German sample. The results indicated that gender differences in self-report emotional intensity induced by pictorial stimuli were more consistent with gender norms and stereotypes (i.e., women being more emotional than men) in the Chinese sample, compared with the German sample, and that gender differences were not constant across age groups. The study revealed that gender differences in emotion are neither constant nor universal, and it highlighted the importance of taking culture and age into account.