Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
16504 papers
Dietary interventions with a household support component show promise for improving household social support and may impact magnitude of dietary change.
Large-scale phrase structure treebank and dependency structure treebank are developed and interconverted for the purpose of syntactic analysis on true corpus. The head percolation table is constructed on modern Chinese dependency grammar by discussing the relationship between phrase structure and dependency structure based on Penn Chinese Treebank (CTB),and CTB from phrase structure is converted to dependency structure treebank using the head percolation table. 200 sentences are chosen from CTB randomly to evaluate the conversion performance. Precision of the conversion has attained 99.50%. The achieved dependency structure treebank can be used to analyze Chinese dependency relation.
In this paper, we offer broad insight into the underperformance of Arabic constituency parsing by analyzing the interplay of linguistic phenomena, annotation choices, and model design. First, we identify sources of syntactic ambiguity understudied in the existing parsing literature. Second, we show that although the Penn Arabic Treebank is similar to other treebanks in gross statistical terms, annotation consistency remains problematic. Third, we develop a human interpretable grammar that is competitive with a latent variable PCFG. Fourth, we show how to build better models for three different parsers. Finally, we show that in application settings, the absence of gold segmentation lowers parsing performance by 2–5 % F1. 1
Nouns are generally easier to learn than verbs (e.g., Bornstein, 2005; Bornstein et al., 2004; Gentner, 1982; Maguire, Hirsh-Pasek, & Golinkoff, 2006). Yet, verbs appear in children's earliest vocabularies, creating a seeming paradox. This paper examines one hypothesis about the difference between noun and verb acquisition. Perhaps the advantage nouns have is not a function of grammatical form class but rather related to a word's imageability. Here, word imageability ratings and form class (nouns and verbs) were correlated with age of acquisition according to the MacArthur-Bates Communicative Development Inventory (CDI) (Fenson et al., 1994). CDI age of acquisition was negatively correlated with words' imageability ratings. Further, a word's imageability contributes to the variance of the word's age of acquisition above and beyond form class, suggesting that at the beginning of word learning, imageability might be a driving factor.
BACKGROUND: In this controlled postdiagnosis study, the authors examined various aspects of body image of breast cancer survivors in cross-sectional and longitudinal designs. METHODS: In 2004 and 2007 the Body Image Scale (BIS) was completed by the same 248 disease-free women who had been treated for stage II and III breast cancer between 1998 and 2002. "Poorer" body image was defined as greater than the 70th percentile (N=76 women) of the BIS scores in contrast to "better" body image (N=172 women). Breast cancer survivors were examined clinically in 2004, and their BIS scores were compared with the scores from an age-matched group of women from the general population. RESULTS: In this cross-sectional study, poorer body image in 2004 was associated significantly with modified radical mastectomy, undergoing or planning to undergo breast-reconstructive surgery, a change in clothing, poor physical and mental health, chronic fatigue, and reduced quality of life (QoL). In univariate analyses, most of these factors and manually planned radiotherapy were significant predictors of poorer body image in 2007. In multivariate analyses, manually planned radiotherapy, poor physical QoL and high BIS score in 2004 remained independent predictors of a poorer body image in 2007. Body image ratings were relatively stable from 2004 to 2007. Twenty-one percent of breast cancer survivors reported body image dissatisfaction, similar to the proportion of dissatisfaction in controls. CONCLUSIONS: In this cross-sectional analysis, body image in breast cancer survivors was associated with the types of surgery and radiotherapy and with mental distress, reduced health, and impaired QoL. Body image ratings were relatively stable over time, and the antecedent body image score was a strong predictor of body image at follow-up. Body image in breast cancer survivors differed very little from that in controls.
The late positive potential (LPP) depicts brain electrical activity during both automatic and controlled sustained attentional processing of emotional stimuli. We investigated in a sample of 18 healthy women how the LPP is modulated by facial expression during an explicit valence rating task and an implicit sex classification task. Midline LPP amplitudes were significantly larger for valence rating than for sex classification. During valence rating, faces with a positive valence resulted in larger LPP amplitudes at centrofrontal electrodes than faces with a negative valence. During sex classification, a similar valence effect was observed at midline parietal electrodes. This implicit LPP valence effect appears to depend on higher visual processing, as during an additional sex classification task with blurred faces no such implicit valence effect was found.
In this paper, we propose a novel selftraining strategy for parsing which is based on Treebank conversion (SSPTC). In SSPTC, we make full use of the strong points of Treebank conversion and self-training, and offset their weaknesses with each other. To provide good parse selection strategies which are needed in self-training, we score the automatically generated parse trees with parse trees in source Treebank as a reference. To maintain the constituency between source Treebank and conversion Treebank which is needed in Treebank conversion, we get the conversion trees with the help of self-training. In our experiments, SSPTC strategy is utilized to parse Tsinghua Chinese Treebank with the help of Penn Chinese Treebank. The results significantly outperform the baseline parser. 1
Studies of discourse relations have not, in the past, attempted to characterize what serves as evidence for them, beyond lists of frozen expressions, or markers, drawn from a few well-defined syntactic classes. In this paper, we describe how the lexicalized discourse relation annotations of the Penn Discourse Treebank (PDTB) led to the discovery of a wide range of additional expressions, annotated as AltLex (alternative lexicalizations) in the PDTB 2.0. Further analysis of AltLex annotation suggests that the set of markers is open-ended, and drawn from a wider variety of syntactic types than currently assumed. As a first attempt towards automatically identifying discourse relation markers, we propose the use of syntactic paraphrase methods.
We investigate a number of approaches to generating Stanford Dependencies, a widely used semantically-oriented dependency representation. We examine algorithms specifically designed for dependency parsing (Nivre, Nivre Eager, Covington, Eisner, and RelEx) as well as dependencies extracted from constituent parse trees created by phrase structure parsers (Charniak, Charniak-Johnson, Bikel, Berkeley and Stanford). We found that phrase structure parsers systematically outperform algorithms designed specifically for dependency parsing. The most accurate method for generating dependencies is the Charniak-Johnson reranking parser, with 89 % (labeled) attachment F1 score. The fastest methods are Nivre, Nivre Eager, and Covington. When used with a linear classifier to make local parsing decisions, these methods can parse the entire Penn Treebank development set (section 22) in less than 10 seconds on an Intel Xeon E5520. However, this speed comes with a substantial drop in F1 score (about 76 % for labeled attachment) compared to competing methods. By tuning how much of the search space is explored by the Charniak-Johnson parser, we are able to arrive at a balanced configuration that is both fast and nearly as good as the most accurate approaches. 1.
This article gives a survey of the main issues confronting the compilers of monolingual dictionaries in the age of the Internet. Among others, it discusses the relationship between a lexical database and a monolingual dictionary, the role of corpus evidence, historical principles in lexicography vs. synchronic principles, the instability of word meaning, the need for full vocabulary coverage, principles of definition writing, the role of dictionaries in society, and the need for dictionaries to give guidance on matters of disputed word usage. It concludes with some questions about the future of dictionary publishing. Keywords: Monolingual Dictionaries, Lexical Database, Dictionary Structure, Word Meaning, Meaning Change, Usage, Usage Notes, Historical Principles Of Lexicography, Synchronic Principles Of Lexicography, Register, Slang, Standard English, Vocabulary Coverage, Consistency Of Sets, Phraseology, Syntagmatic Patterns, Problems Of Compositionality, Linguistic Prescriptivism, Lexical Evidence **This article is an edited version of a plenary address delivered at the conference on 'Dictionaries, More Than Words', which took place at the Faculty of Social Sciences, University of Ljubljana, Ljubljana, Slovenia, 6 February 2009.
Abstract This paper aims to contribute to the current debate on ‘interculturality’ (IC) by investigating the process of language socialization whereby different generations of diasporic families negotiate, construct, and renew their sociocultural values and identities through interaction. Focusing on the use of address terms and ‘talk about social, cultural, and linguistic practice,’ the paper argues that IC is not only a dynamic process through which participants make aspects of their multiple and shifting identities relevant, but also a process of developing new social and cultural identities. In effect, it serves as a direct means of language socialization for the younger generation who are developing their sociocultural roles and learning about the social and cultural appropriateness of behavior in a diasporic context, where there are potentially substantial differences in social and cultural values between the wider local community and the diasporic community. Language socialization is regarded in this study as not simply about passing social and cultural values from one generation to another, but about bringing about changes in social and cultural values. Through language socialization, the younger generations of diasporic communities not only internalize the social, cultural and linguistic norms of their community, but also play an active role in constructing and creating their own social and cultural identities as well as bringing about changes to the existing community and family norms.
We compare self-training with and without reranking for parser domain adaptation, and examine the impact of syntactic parser adaptation on a semantic role labeling system. Although self-training without reranking has been found not to improve in-domain accuracy for parsers trained on the WSJ Penn Treebank, we show that it is surprisingly effective for parser domain adaptation. We also show that simple self-training of a syntactic parser improves out-of-domain accuracy of a semantic role labeler. 1
Discontinuities occur especially frequently in languages with a relatively free word order, such as German. Generally, due to the longdistance dependencies they induce, they lie beyond the expressivity of Probabilistic CFG, i.e., they cannot be directly reconstructed by a PCFG parser. In this paper, we use a parser for Probabilistic Linear Context-Free Rewriting Systems (PLCFRS), a formalism with high expressivity, to directly parse the German NeGra and TIGER treebanks. In both treebanks, discontinuities are annotated with crossing branches. Based on an evaluation using different metrics, we show that an output quality can be achieved which is comparable to the output quality of PCFG-based systems. In most constituency treebanks, sentence annotation is restricted to having the shape of trees without crossing branches, and the non-local dependencies induced by the discontinuities are modeled by an additional mechanism. In the Penn Treebank (PTB) (Marcus et al., 1994), e.g., this mechanism is a combination of special labels and empty nodes, establishing implicit additional edges. In the German TüBa-D/Z (Telljohann et al., 2006), additional edges are established by a combination of topological field annotation and special edge labels. As an example, Fig. 1 shows a tree from TüBa-D/Z with the annotation of (1). Note here the edge label ON-MOD on the relative clause which indicates that the subject of the sentence (alle Attribute) is modified. 1
Habituation is a fundamental form of learning manifested by a decrement of neuronal responses to repeated sensory stimulation. In addition, habituation is also known to occur on the behavioral level, manifested by reduced emotional reactions to repeatedly presented affective stimuli. It is, however, not clear which brain areas show a decline in activity during repeated sensory stimulation on the same time scale as reduced valence and arousal experience and whether these areas can be delineated from other brain areas with habituation effects on faster or slower time scales. These questions were addressed using functional magnetic resonance imaging acquired during repeated stimulation with piano melodies. The magnitude of functional responses in the laterobasal amygdala and in related cortical areas and that of valence and arousal ratings, given after each music presentation, declined in parallel over the experiment. In contrast to this long-term habituation (43 min), short-term decreases occurring within seconds were found in the primary auditory cortex. Sustained responses that remained throughout the whole investigated time period were detected in the ventrolateral prefrontal cortex extending to the dorsal part of the anterior insular cortex. These findings identify an amygdalocortical network that forms the potential basis of affective habituation in humans.
This paper proposes a unified framework for zero anaphora resolution, which can be divided into three sub-tasks: zero anaphor detection, anaphoricity determination and antecedent identification. In particular, all the three sub-tasks are addressed using tree kernel-based methods with appropriate syntactic parse tree structures. Experimental results on a Chinese zero anaphora corpus show that the proposed tree kernel-based methods significantly outperform the feature-based ones. This indicates the critical role of the structural information in zero anaphora resolution and the necessity of tree kernel-based methods in modeling such structural information. To our best knowledge, this is the first systematic work dealing with all the three sub-tasks in Chinese zero anaphora resolution via a unified framework. Moreover, we release a Chinese zero anaphora corpus of 100 documents, which adds a layer of annotation to the manually-parsed sentences in the Chinese Treebank (CTB) 6.0. 1
Previous investigations of somatic hypersensitivity in IBS patients have typically involved only a single stimulus modality, and little information exists regarding whether patterns of somatic pain perception vary across stimulus modalities within a group of patients with IBS. Therefore, the current study was designed to characterize differences in perceptual responses to a battery of noxious somatic stimuli in IBS patients compared to controls. A total of 78 diarrhea-predominant and 57 controls participated in the study. We evaluated pain threshold and tolerance and sensory and affective ratings of contact thermal, mechanical pressure, ischemic stimuli, and cold pressor stimuli. In addition to assessing perceptual responses, we also evaluated differences in neuroendocrine and cardiovascular responses to these experimental somatic pain stimuli. A subset of IBS patients demonstrated the presence of somatic hypersensitivity to thermal, ischemic, and cold pressor nociceptive stimuli. The somatic hypersensitivity in IBS patients was somatotopically organized in that the lower extremities that share viscerosomatic convergence with the colon demonstrate the greatest hypersensitivity. There were also changes in ACTH, cortisol, and systolic blood pressure in response to the ischemic pain testing in IBS patients when compared to controls. The results of this study suggest that a more widespread alteration in central pain processing in a subset of IBS patients may be present as they display hypersensitivity to heat, ischemic, and cold pressor stimuli.
Inducing a grammar from text has proven to be a notoriously challenging learning task despite decades of research. The primary reason for its difficulty is that in order to induce plausible grammars, the underlying model must be capable of representing the intricacies of language while also ensuring that it can be readily learned from data. The majority of existing work on grammar induction has favoured model simplicity (and thus learnability) over representational capacity by using context free grammars and first order dependency grammars, which are not sufficiently expressive to model many common linguistic constructions. We propose a novel compromise by inferring a probabilistic tree substitution grammar, a formalism which allows for arbitrarily large tree fragments and thereby better represent complex linguistic structures. To limit the model’s complexity we employ a Bayesian non-parametric prior which biases the model towards a sparse grammar with shallow productions. We demonstrate the model’s efficacy on supervised phrase-structure parsing, where we induce a latent segmentation of the training treebank, and on unsupervised dependency grammar induction. In both cases the model uncovers interesting latent linguistic structures while producing competitive results. Keywords: grammar induction, tree substitution grammar, Bayesian non-parametrics, Pitman-Yor process, Chinese restaurant process 1.
Respondents can vary strongly in the way they use rating scales. Specifically, respondents can exhibit a variety of response styles, which threatens the validity of the responses. The purpose of this article is to investigate how response style and content of the items affect rating scale responses. The authors develop a novel model that accounts for different types of response styles, content of items, and background characteristics of respondents. By imposing a bilinear parameter structure on a multinomial logit model, the authors graphically distinguish the effects on the response behavior of the characteristics of a respondent and the content of an item. The authors combine this approach with finite mixture modeling, yielding two segmentations of the respondents: one for response style and one for item content. They apply this latent-class bilinear multinomial logit model to the well-known List of Values in a cross-national context. The results show large differences in the opinions and the response styles of respondents and reveal previously unknown response styles. Some response styles appear to be valid communication styles, whereas other response styles often concur with inconsistent opinions of the items and seem to be response bias.
Computer-asisted language learning needs better lexical databases in order to produce better software for vocabulary learning. This paper attempts to give some guidelines for the construction of a dedicated lexical database for vocabulary learning purposes.
Obesity prevalence in the U.S. has increased during the last three decades with major impact on public health. Screening for obesity in a population with unknown weight status can be time- and resource-consuming, but the information is valuable for prioritizing and allocating scarce resources. The challenge remains to properly assess obesity with the available methods. Body Image Rating Scales (BIRS) have initially been developed to assess body image disturbances, but also seem useful as an alternative method in assessing obesity prevalence. Several different BIRS exists. In this project I reviewed the literature that exists regarding the use of BIRS, and its advantages and limitations for the assessment of obesity status with regards to BMI. The result yielded nine publications that examined eight different scales and their correlation with BMI, ranging from r=.59 for self-reported BMI to r=.94 for measured BMI. One concern is the lack of standardization of this method to assess obesity, given the range of different scales. While many methods for obesity assessment are available, the simplicity, ease of use and cost-effectiveness of BIRS make it very appealing. BIRS remain a potentially attractive option to assess the weight status of a large population with minimal requirements in assets and time, especially in situations where measuring instruments are not available, or when height or weight could not be recalled.
We investigate the performance of an easyfirst, non-directional dependency parser on the Hebrew Dependency treebank. We show that with a basic feature set the greedy parser’s accuracy is on a par with that of a first-order globally optimized MST parser. The addition of morphological-agreement feature improves the parsing accuracy, making it on-par with a second-order globally optimized MST parser. The improvement due to the morphological agreement information is persistent both when gold-standard and automatically-induced morphological information is used. 1
Parser disambiguation with precision grammars generally takes place via statistical ranking of the parse yield of the grammar using a supervised parse selection model. In the standard process, the parse selection model is trained over a hand-disambiguated treebank, meaning that without a significant investment of effort to produce the treebank, parse selection is not possible. Furthermore, as treebanking is generally streamlined with parse selection models, creating the initial treebank without a model requires more resources than subsequent treebanks. In this work, we show that, by taking advantage of the constrained nature of these HPSG grammars, we can learn a discriminative parse selection model from raw text in a purely unsupervised fashion. This allows us to bootstrap the treebanking process and provide better parsers faster, and with less resources. 1
We present an approach to automatically identifying the arguments of discourse connectives based on data from the Penn Discourse Treebank. Of the two arguments of connectives, called Arg1 and Arg2, we focus on Arg1, which has proven more challenging to identify. Our approach employs a sentence-based representation of arguments, and distinguishes intra-sentential connectives, which take both their arguments in the same sentence, from inter-sentential connectives, whose arguments are found in different sentences. The latter are further distinguished by paragraph position into ParaInit connectives, which appear in a paragraph-initial sentence, and ParaNonInit connectives, which appear elsewhere. The paper focusses on predicting Arg1 of Inter-sentential ParaNonInit connectives, presenting a set of scope-based filters that reduce the search space for Arg1 from all the previous sentences in the paragraph to a subset of them. For cases where these filters do not uniquely identify Arg1, coreference-based heuristics are employed. Our analysis shows an absolute 3 % performance improvement over the high baseline of 83.3 % for identifying Arg1 of Inter-sentential ParaNonInit connectives. 1.
Abstract The aim of the article is to introduce a new approach to verb valency analysis. This approach – full valency – observes properties of verbs which occur solely in actual language usage. The term “full valency” means that all arguments, without distinguishing complements (obligatory arguments governed by the verb) and adjuncts (optional arguments directly dependent on the predicate verb), are taken into account. Because of an expectation that full valency reflects some mechanism which governs verb behaviour in a language, hypotheses concerning (1) the distribution of full valency frames, (2) the relationship between the number of valency frames and the frequency of the verb, and (3) the relationship between the number of valency frames and verb length were tested empirically. To test the hypotheses, a Czech syntactically annotated corpus – the Prague Dependency Treebank – was used.
In this paper, we present a novel approach to enhance hierarchical phrase-based machine translation systems with linguistically motivated syntactic features. Rather than directly using treebank categories as in previous studies, we learn a set of linguistically-guided latent syntactic categories automatically from a source-side parsed, word-aligned parallel corpus, based on the hierarchical structure among phrase pairs as well as the syntactic structure of the source side. In our model, each X nonterminal in a SCFG rule is decorated with a real-valued feature vector computed based on its distribution of latent syntactic categories. These feature vectors are utilized at decoding time to measure the similarity between the syntactic analysis of the source side and the syntax of the SCFG rules that are applied to derive translations. Our approach maintains the advantages of hierarchical phrase-based translation systems while at the same time naturally incorporates soft syntactic constraints.
Proceedings of the Ninth International Workshop \non Treebanks and Linguistic Theories. \nEditors: Markus Dickinson, Kaili Müürisep and Marco Passarotti. \nNEALT Proceedings Series, Vol. 9 (2010), 55-66. \n© 2010 The editors and contributors. \nPublished by \nNorthern European Association for Language \nTechnology (NEALT) \nhttp://omilia.uio.no/nealt. \nElectronically published at \nTartu University Library (Estonia) \nhttp://hdl.handle.net/10062/15891.
In this paper we explore two strategies to incorporate local morphosyntactic features in Hindi dependency parsing. These features are obtained using a shallow parser. We first explore which information provided by the shallow parser is most beneficial and show that local morphosyntactic features in the form of chunk type, head/non-head information, chunk boundary information, distance to the end of the chunk and suffix concatenation are very crucial in Hindi dependency parsing. We then investigate the best way to incorporate this information during dependency parsing. Further, we compare the results of various experiments based on various criterions and do some error analysis. All the experiments were done with two data-driven parsers, MaltParser and MSTParser, on a part of multi-layered and multi-representational Hindi Treebank which is under development. This paper is also the first attempt at complete sentence level parsing for Hindi.
We describe a method for the automatic extraction of a Stochastic Lexicalized Tree Insertion Grammar from a linguistically rich HPSG Treebank. The extraction method is strongly guided by HPSG–based head and argument decomposition rules. The tree anchors correspond to lexical labels encoding fine–grained information. The approach has been tested with a German corpus achieving a labeled recall of 77.33% and labeled precision of 78.27%, which is competitive to recent results reported for German parsing using the Negra Treebank.
OBJECTIVES: It is well recognised that medical training can be extremely stressful and that high stress is a risk factor for a wide range of psychological and health-related consequences. The primary aims of this study were to introduce the Medical Student Stress Profile (MSSP) and to demonstrate its psychometric quality as a specific device for auditing medical student stress. Secondary aims were to establish the reliability, construct and criterion validity of this instrument and to explore the relationships between stress, coping, personality, motivation and emotional intelligence in medical students. METHODS: A battery of self-report measures including the MSSP was administered to a sample of 239 undergraduate and graduate-entry medical students. The battery included indices of stress, coping with and proneness to stress, as well as measures of emotional intelligence, motivation style, personality traits, educational environment perception and self-reported symptomatology. Psychometric evaluation of the MSSP was conducted along with a correlation analysis of stress concomitants. RESULTS: The MSSP revealed good psychometric properties and showed a substantial stress load in the participant sample. The pattern of correlations with concomitant measures conformed generally to expectations. Strong cohort effects were observed, which suggest the importance of future investigation into the role of the group in stress amelioration. Stress adversely affects ratings of the educational environment as measured by the Dundee Ready Education Environment Measure. CONCLUSIONS: The MSSP was specifically developed for the medical training context and may have utility for individual and group stress audits of medical students and as a device to inform remedial programmes in stress management in medical education.
In the field of natural language processing (NLP), there often exist multiple corpora with different annotation standards for the same task. In this paper, we take syntactic parsing as a case study and propose a reranking method which is able to make direct use of disparate treebanks simultaneously without using techniques such as treebank conversion. The method proceeds in three steps: 1) build parsers on individual treebanks; 2) use parsers independently to generate n-best lists for each sentence in test set; 3) rerank individual n-best lists which correspond to the same sentence by using consensus information exchanged among these n-best lists. Experimental results on two open Chinese treebanks show that our method significantly outperforms the baseline system by 0.84% and 0.53% respectively.
We present the first evaluation of the utility of automatic evaluation metrics on surface realizations of Penn Treebank data. Using outputs of the OpenCCG and XLE realizers, along with ranked WordNet synonym substitutions, we collected a corpus of generated surface realizations. These outputs were then rated and post-edited by human annotators. We evaluated the realizations using seven automatic metrics, and analyzed correlations obtained between the human judgments and the automatic scores. In contrast to previous NLG meta-evaluations, we find that several of the metrics correlate moderately well with human judgments of both adequacy and fluency, with the TER family performing best overall. We also find that all of the metrics correctly predict more than half of the significant systemlevel differences, though none are correct in all cases. We conclude with a discussion of the implications for the utility of such metrics in evaluating generation in the presence of variation. A further result of our research is a corpus of post-edited realizations, which will be made available to the research community. 1
This paper proposes a dependency parsing method that uses bilingual constraints to improve the accuracy of parsing bilingual texts (bitexts). In our method, a targetside tree fragment that corresponds to a source-side tree fragment is identified via word alignment and mapping rules that are automatically learned. Then it is verified by checking the subtree list that is collected from large scale automatically parsed data on the target side. Our method, thus, requires gold standard trees only on the source side of a bilingual corpus in the training phase, unlike the joint parsing model, which requires gold standard trees on the both sides. Compared to the reordering constraint model, which requires the same training data as ours, our method achieved higher accuracy because of richer bilingual constraints. Experiments on the translated portion of the Chinese Treebank show that our system outperforms monolingual parsers by 2.93 points for Chinese and 1.64 points for English. 1
RATIONALE: Acute tryptophan depletion (ATD) decreases levels of central serotonin. ATD thus enables the cognitive effects of serotonin to be studied, with implications for the understanding of psychiatric conditions, including depression. OBJECTIVE: To determine the role of serotonin in conscious (explicit) and unconscious/incidental processing of emotional information. MATERIALS AND METHODS: A randomized, double-blind, cross-over design was used with 15 healthy female participants. Subjective mood was recorded at baseline and after 4 h, when participants performed an explicit emotional face processing task, and a task eliciting unconscious processing of emotionally aversive and neutral images presented subliminally using backward masking. RESULTS: ATD was associated with a robust reduction in plasma tryptophan at 4 h but had no effect on mood or autonomic physiology. ATD was associated with significantly lower attractiveness ratings for happy faces and attenuation of intensity/arousal ratings of angry faces. ATD also reduced overall reaction times on the unconscious perception task, but there was no interaction with emotional content of masked stimuli. ATD did not affect breakthrough perception (accuracy in identification) of masked images. CONCLUSIONS: ATD attenuates the attractiveness of positive faces and the negative intensity of threatening faces, suggesting that serotonin contributes specifically to the appraisal of the social salience of both positive and negative salient social emotional cues. We found no evidence that serotonin affects unconscious processing of negative emotional stimuli. These novel findings implicate serotonin in conscious aspects of active social and behavioural engagement and extend knowledge regarding the effects of ATD on emotional perception.
Tree-based translation models, which exploit the linguistic syntax of source language, usually separate decoding into two steps: parsing and translation. Although this separation makes tree-based decoding simple and efficient, its translation performance is usually limited by the number of parse trees offered by parser. Alternatively, we propose to parse and translate jointly by casting tree-based translation as parsing. Given a source-language sentence, our joint decoder produces a parse tree on the source side and a translation on the target side simultaneously. By combining translation and parsing models in a discriminative framework, our approach significantly outperforms a forestbased tree-to-string system by 1.1 absolute BLEU points on the NIST 2005 Chinese-English test set. As a parser, our joint decoder achieves an F1 score of 80.6 % on the Penn Chinese Treebank. 1
This paper presents an efficient approach to use small syntactic category for constructing large tree with the assist of linguistic knowledge for parsing Thai language. VB-EM algorithm is exploited to adjust parameters of any trees for selecting the best tree. Our best result is 70.62% accuracy of bracketing recovery.