Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
16504 papers
Transition-based dependency parsers generally use heuristic decoding algorithms but can accommodate arbitrarily rich feature representations. In this paper, we show that we can improve the accuracy of such parsers by considering even richer feature sets than those employed in previous systems. In the standard Penn Treebank setup, our novel features improve attachment score form 91.4 % to 92.9%, giving the best results so far for transitionbased parsing and rivaling the best results overall. For the Chinese Treebank, they give a signficant improvement of the state of the art. An open source release of our parser is freely available.
BACKGROUND: Major depressive disorder (MDD) is associated with deficits in recalling specific autobiographical memories (AMs). Extensive research has examined the functional anatomical correlates of AM in healthy humans, but no studies have examined the neurophysiological underpinnings of AM deficits in MDD. The goal of the present study was to examine the differences in the hemodynamic response between patients with MDD and controls while they engage in AM recall. METHOD: Participants (12 unmedicated MDD patients; 14 controls) underwent functional magnetic resonance imaging (fMRI) scanning while recalling AMs in response to positive, negative and neutral cue words. The hemodynamic response during memory recall versus performing subtraction problems was compared between MDD patients and controls. Additionally, a parametric linear analysis examined which regions correlated with increasing arousal ratings. RESULTS: Behavioral results showed that relative to controls, the patients with MDD had fewer specific (p=0.013), positive (p=0.030), highly arousing (p=0.036) and recent (p=0.020) AMs, and more categorical (p<0.001) AMs. The blood oxygen level-dependent (BOLD) response in the parahippocampus and hippocampus was higher for memory recall versus subtraction in controls and lower in those with MDD. Activity in the anterior insula was lower for specific AM recall versus subtraction, with the magnitude of the decrement greater in MDD patients. Activity in the anterior cingulate cortex was positively correlated with arousal ratings in controls but not in patients with MDD. CONCLUSIONS: We replicated previous findings of fewer specific and more categorical AMs in patients with MDD versus controls. We found differential activity in medial temporal and prefrontal lobe structures involved in AM retrieval between MDD patients and controls as they engaged in AM recall. These neurophysiological deficits may underlie AM recall impairments seen in MDD.
There has been a rapid increase in the volume of research on data-driven dependency parsers in the past five years. This increase has been driven by the availability of treebanks in a wide variety of languages—due in large part to the CoNLL shared tasks—as well as the straightforward mechanisms by which dependency theories of syntax can encode complex phenomena in free word order languages. In this article, our aim is to take a step back and analyze the progress that has been made through an analysis of the two predominant paradigms for data-driven dependency parsing, which are often called graph-based and transition-based dependency parsing. Our analysis covers both theoretical and empirical aspects and sheds light on the kinds of errors each type of parser makes and how they relate to theoretical expectations. Using these observations, we present an integrated system based on a stacking learning framework and show that such a system can learn to overcome the shortcomings of each non-integrated system.
This paper presents a simple yet effective semi-supervised method to improve Chinese word segmentation and POS tagging. We introduce novel features derived from large auto-analyzed data to enhance a simple pipelined system. The auto-analyzed data are generated from unlabeled data by using a baseline system. We evaluate the usefulness of our approach in a series of experiments on Penn Chinese Treebanks and show that the new features provide substantial performance gains in all experiments. Furthermore, the results of our proposed method are superior to the best reported results in the literature. 1
Dependency parsers are critical components within many NLP systems. However, currently available dependency parsers each exhibit at least one of several weaknesses, including high running time, limited accuracy, vague dependency labels, and lack of nonprojectivity support. Furthermore, no commonly used parser provides additional shallow semantic interpretation, such as preposition sense disambiguation and noun compound interpretation. In this paper, we present a new dependency-tree conversion of the Penn Treebank along with its associated fine-grain dependency labels and a fast, accurate parser trained on it. We explain how a non-projective extension to shift-reduce parsing can be incorporated into non-directional easy-first parsing. The parser performs well when evaluated on the standard test section of the Penn Treebank, outperforming several popular open source dependency parsers; it is, to the best of our knowledge, the first dependency parser capable of parsing more than 75 sentences per second at over 93 % accuracy. 1
People show autonomic responses when they empathize with the suffering of another person. However, little is known about how these autonomic changes are related to prosocial behavior. We measured skin conductance responses (SCRs) and affect ratings in participants while either receiving painful stimulation themselves, or observing pain being inflicted on another person. In a later session, they could prevent the infliction of pain in the other by choosing to endure pain themselves. Our results show that the strength of empathy-related vicarious skin conductance responses predicts later costly helping. Moreover, the higher the match between SCR magnitudes during the observation of pain in others and SCR magnitude during self pain, the more likely a person is to engage in costly helping. We conclude that prosocial motivation is fostered by the strength of the vicarious autonomic response as well as its match with first-hand autonomic experience.
This paper proposes a direct parsing of non-local dependencies in English. To this end, we use probabilistic linear context-free rewriting systems for data-driven parsing, following recent work on parsing German. In order to do so, we first perform a transformation of the Penn Treebank annotation of non-local dependencies into an annotation using crossing branches. The resulting treebank can be used for PLCFRS-based parsing. Our evaluation shows that, compared to PCFG parsing with the same techniques, PLCFRS parsing yields slightly better results. In particular when evaluating only the parsing results concerning long-distance dependencies, the PLCFRS approach with discontinuous constituents is able to recognize about 88% of the dependencies of type *T* and *T*-PRN encoded in the Penn Treebank. Even the evaluation results concerning local dependencies, which can in principle be captured by a PCFG-based model, are better with our PLCFRS model. This demonstrates that by discarding information on non-local dependencies the PCFG model loses important information on syntactic dependencies in general.
Abstract This article presents an approach to automatic language classification by means of linguistic networks. Networks of 11 languages were constructed from dependency treebanks, and the topology of these networks serves as input to the classification algorithm. The results match the genealogical similarities of these languages. In addition, we test two alternative approaches to automatic language classification – one based on n-grams and the other on quantitative typological indices. All three methods show good results in identifying genealogical groups. Beyond genetic similarities, network features (and feature combinations) offer a new source of typological information about languages. This information can contribute to a better understanding of the interplay of single linguistic phenomena observed in language.
We examined how individual differences in mood and anxiety in the early postpartum period are related to brain response to infant stimuli during fMRI, with particular focus on regions implicated in both maternal behavior and mood/anxiety, that is, the subgenual anterior cingulate cortex (sgACC) and the amygdala. At approximately 3 months postpartum, 22 mothers completed an affect-rating task (ART) during fMRI, where their affective response to infant stimuli was explicitly probed. Mothers viewed/rated four infant face conditions: own positive (OP), own negative (ON), unfamiliar positive (UP), and unfamiliar negative (UN). Mood and anxiety were measured by the Edinburgh Postnatal Depression Scale (EDPS) and the State-Trait Anxiety Inventory-Trait Version (STAI-T); maternal factors related to parental stress and attachment were also assessed. Brain-imaging data underwent a random-effects analysis, and cluster-based statistical thresholding was applied to the following contrasts: OP-UP, ON-UN, OP-ON, and UP-UN. Our main finding was that poorer quality of maternal experience was significantly related to reduced amygdala response to OP compared to UP infant faces. Our results suggest that, in human mothers, infant-related amygdala function may be an important factor in maternal anxiety/mood, in quality of mothering, and in individual differences in the motivation to mother. We are very grateful to the staff at the Imaging Research Center of the Brain-Body Institute for their contributions to this project. This work was supported by an Ontario Mental Health Foundation operating grant awarded to Alison Fleming and a postdoctoral fellowship awarded to Jennifer Barrett.
We describe a method for disambiguating Chi-nese commas that is central to Chinese sen-tence segmentation. Chinese sentence seg-mentation is viewed as the detection of loosely coordinated clauses separated by commas. Trained and tested on data derived from the Chinese Treebank, our model achieves a clas-sification accuracy of close to 90 % overall, which translates to an F1 score of 70 % for detecting commas that signal sentence bound-aries. 1
The large combined search space of joint word segmentation and Part-of-Speech (POS) tagging makes efficient decoding very hard. As a result, effective high order features representing rich contexts are inconvenient to use. In this work, we propose a novel stacked subword model for this task, concerning both efficiency and effectiveness. Our solution is a two step process. First, one word-based segmenter, one character-based segmenter and one local character classifier are trained to produce coarse segmentation and POS information. Second, the outputs of the three predictors are merged into sub-word sequences, which are further bracketed and labeled with POS tags by a fine-grained sub-word tagger. The coarse-to-fine search scheme is efficient, while in the sub-word tagging step rich contextual features can be approximately derived. Evaluation on the Penn Chinese Treebank shows that our model yields improvements over the best system reported in the literature. 1
Abstract In this paper, I investigate the phonological similarity of different elements of the phonological pole of multi-word units. I discuss two case studies on slightly different levels of abstractness. The first case study investigates lexically fully-specified V-NP DirObj idioms such as kick the bucket and lose one's cool; the idioms investigated are taken from the Collins Cobuild Dictionary of Idioms (Harper Collins, 2002). The second case study investigates the lexically less specified way -construction, which is exemplified by He fought his way through the crowd (cf. Goldberg, Constructions: A Construction Grammar approach to argument structure, The University of Chicago Press, 1995: Ch. 9), on the basis of data from the British National Corpus 1.0. I show that both patterns exhibit a strong phonological within-pole relation, namely a strong preference for having their slots filled with phonologically similar elements, where phonological similarity is manifested in alliteration patterns. These preferences are statistically significant when compared to chance-level expectations derived both from the corpora and from the CELEX database (Baayen et al., The CELEX Lexical Database (CD-ROM), University of Pennsylvania, 1995) and are explained on the basis of Langacker's concept of syntactic and phonological constituents as well as current exemplar-/usage-based approaches.
In this paper, we investigated the impact of extracting different types of multiword expressions (MWEs) in improving the accuracy of a data-driven dependency parser for a morphologically rich language (Turkish). We showed that in the training stage, the unification of MWEs of a certain type, namely compound verb and noun formations, has a negative effect on parsing accuracy by increasing the lexical sparsity. Our results gave a statistically significant improvement by using a variant of the treebank excluding this MWE type in the training stage. Our extrinsic evaluation of an ideal MWE recognizer (for only extracting MWEs of type named entities, duplications, numbers, dates and some predefined list of compound prepositions) showed that the preprocessing of the test data would improve the labeled parsing accuracy by 1.5%.
This chapter discusses the inspection of the relevance of language to power and social diversity. It explores the elastic impact of power on the social life of living languages. Sociolinguists and historical linguists have demonstrated that linguistic evolution has been shaped by many forces, including political circumstances that are not egalitarian. Einar Haugen promoted the study of language within its ecological context. Some critics of quantitative variationist sociolinguistics have noted that scholars who classify speakers based on pre-ordained social categories, like race, may miss important nuances in linguistic behavior that defy easy circumstantial classification. Hymes affirmed that communicative events demand a high degree of communicative competence as related to language usage throughout the world. William Labov's study of the social stratification of English speakers in New York City is illustrative of urban linguistic stratification. The majority of speech communities throughout the world set the indigenous standard linguistic norms.
Emotion recognition algorithms for spoken dialogue applications typically employ lexical models that are trained on labeled in-domain data. In this paper, we propose a domainindependent approach to affective text modeling that is based on the creation of an affective lexicon. Starting from a small set of manually annotated seed words, continuous valence ratings for new words are estimated using semantic similarity scores and a kernel model. The parameters of the model are trained using least mean squares estimation. Word level scores are combined to produce sentence-level scores via simple linear and non-linear fusion. The proposed method is evaluated on the SemEval news headline polarity task and on the ChIMP politeness and frustration detection dialogue task, achieving state-of-theart results on both. For politeness detection, best results are obtained when the affective model is adapted using in domain data. For frustration detection, the domain-independent model and non-linear fusion achieve the best performance. Index Terms: language understanding, emotion, affect, affective lexicon
This paper presents the introduction of WordNet semantic classes in a dependency parser, obtaining improvements on the full Penn Treebank for the first time. We tried different combinations of some basic semantic classes and word sense disambiguation algorithms. Our experiments show that selecting the adequate combination of semantic features on development data is key for success. Given the basic nature of the semantic classes and word sense disambiguation algorithms used, we think there is ample room for future improvements.
Attachment-related strategies are thought to be critical for regulation and processing of emotional information. This study examined biases in selective attention to emotional stimuli as a function of insecure attachment. Participants searched for a single target image preceded by to-be-ignored distracters depicting emotional images varying in valence and arousal. Results revealed that, in general, negative distracters affected accuracy levels, and that the anxious attached participants showed a clear interference of the emotional distracters. In contrast, the avoidant group evinced a higher control on such interference. In addition, arousal ratings to distracter images indicated superior emotional activation only for anxious attached participants. Consistent with the evolutionary-based attachment theory threat-related stimuli prompted priority attentional responses. Present findings are in line with evidence showing the deployment of distinct strategies in insecurely attached individuals for the regulation of attention to emotional information.
We show how punctuation can be used to improve unsupervised dependency parsing. Our linguistic analysis confirms the strong connection between English punctuation and phrase boundaries in the Penn Treebank. However, approaches that naively include punctuation marks in the grammar (as if they were words) do not perform well with Klein and Manning’s Dependency Model with Valence (DMV). Instead, we split a sentence at punctuation and impose parsing restrictions over its fragments. Our grammar inducer is trained on the Wall Street Journal (WSJ) and achieves 59.5 % accuracy out-of-domain (Brown sentences with 100 or fewer words), more than 6 % higher than the previous best results. Further evaluation, using the 2006/7 CoNLL sets, reveals that punctuation aids grammar induction in 17 of 18 languages, for an overall average net gain of 1.3%. Some of this improvement is from training, but more than half is from parsing with induced constraints, in inference. Punctuation-aware decoding works with existing (even already-trained) parsing models and always increased accuracy in our experiments. 1
Resolving coordination ambiguity is a classic hard problem. This paper looks at coordination disambiguation in complex noun phrases (NPs). Parsers trained on the Penn Treebank are reporting impressive numbers these days, but they don’t do very well on this problem (79%). We explore systems trained using three types of corpora: (1) annotated (e.g. the Penn Treebank), (2) bitexts (e.g. Europarl), and (3) unannotated monolingual (e.g. Google N-grams). Size matters: (1) is a million words, (2) is potentially billions of words and (3) is potentially trillions of words. The unannotated monolingual data is helpful when the ambiguity can be resolved through associations among the lexical items. The bilingual data is helpful when the ambiguity can be resolved by the order of words in the translation. We train separate classifiers with monolingual and bilingual features and iteratively improve them via co-training. The co-trained classifier achieves close to 96 % accuracy on Treebank data and makes 20 % fewer errors than a supervised system trained with Treebank annotations. 1
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) can induce nonmotor side effects such as behavioral and mood disturbances or body weight gain in Parkinson's disease (PD) patients. We hypothesized that some of these problems could be related to an altered attribution of incentive salience (ie, emotional relevance) to rewarding and aversive stimuli. Twenty PD patients (all men; mean age ± SD, 58.3 ± 6 years) in bilateral STN DBS switched ON and OFF conditions and 18 matched controls rated pictures selected from the International Affective Picture System according to emotional valence (unpleasantness/pleasantness) and arousal on 2 independent visual scales ranging from 1 to 9. Eighty-four pictures depicting primary rewarding (erotica and food) and aversive fearful (victims and threat) and neutral stimuli were selected for this study. In the STN DBS ON condition, the PD patients attributed lower valence scores to the aversive pictures compared with the OFF condition (P <.01) and compared with controls (P <.01). The difference between the OFF condition and controls was less pronounced (P <.05). Furthermore, postoperative weight gain correlated with arousal ratings from the food pictures in the STN DBS ON condition (P <.05 compensated for OFF condition). Our results suggest that STN DBS increases activation of the aversive motivational system so that more relevance is attributed to aversive fearful stimuli. In addition, STN DBS-related sensitivity to food reward stimuli cues might drive DBS-treated patients to higher food intake and subsequent weight gain.
m, 3abid_khan1961@y ahoo.com Abstract-- This paper is about the development of Pashto Treebank in the form of Extensible Markup Language (XML) code. A Chart Parser has been developed that uses Chart Parsing Algorithm (1) for building parse trees for Pashto sentences. The output of the parser is the parsed text which can be obtained in one of its three forms such as reduced graph, parse tree and XML code. For parsing, the parser needs Context Free Grammar (CFG) of Pashto language and Tagged Input Text as input. The system has been tested on real world text taken from Pashto novels and web sites and tagged manually. Eighty seven (87) sentences were parsed by the parser in which fifty four (54) were correctly parsed with a single parse tree and the rest 33 were parsed with multiple trees and thus the accuracy obtained is 62.06%.
Categories of stimulus parameters such as word frequency, and familiarity, have been investigated in treatment of word retrieval deficit in aphasia, however none has been without disagreement as to the presence or magnitude of effect on performance. Recently, embodied semantics has also been considered. The present project reports an item analysis of stimulus parameters used in a naming study with one participant. Three parameters were examined for their contribution to successful naming performance: word class (noun vs. verb), word familiarity (high vs low) and embodiment (hand, foot or mouth). Results show equivocal support for all categories.
This paper introduces Chart Inference (CI), an algorithm for deriving a CCG category for an unknown word from a partial parse chart. It is shown to be faster and more precise than a baseline brute-force method, and to achieve wider coverage than a rule-based system. In addition, we show the application of CI to a domain adaptation task for question words, which are largely missing in the Penn Treebank. When used in combination with self-training, CI increases the precision of the baseline StatCCG parser over subjectextraction questions by 50%. An error analysis shows that CI contributes to the increase by expanding the number of category types available to the parser, while self-training adjusts the counts. 1
The Internet has rarely been used in auditory perception studies due to concerns about standardisation and calibration across different systems and settings. However, not all auditory research is based on the investigation of fine-grained differences in auditory thresholds. Where meaningful ‘real-world’ listening, for instance the perception of speech, is concerned, the Internet may be a more appropriate and ecologically valid setting to collect data. This study compared affective ratings of low-pass-filtered infant-, foreigner- and British adult-directed speech obtained with traditional methods in the laboratory, with those obtained from an Internet sample. Dropout rates and demographic distribution of participants in the Internet condition were also assessed. The results show that affective ratings were similar for both the Internet and laboratory samples. These findings indicate the viability of Internet-based research into affective speech perception and suggest that precise acoustic environmental control may not always be necessary.
In this paper, we describe and compare two statistical parsing approaches for the hybrid dependency-constituency syntactic representation used in the Quranic Arabic Treebank (Dukes and Buckwalter, 2010). In our first approach, we apply a multi-step process in which we use a shift-reduce algorithm trained on a pure dependency preprocessed version of the treebank. After parsing, the dependency output is converted into the hybrid representation. This is compared to a novel one-step parser that is able to learn the hybrid representation without preprocessing. We define an extended labelled attachment score (ELAS) as our performance metric for hybrid parsing, and report 87.47 % (F1 score) for the multi-step approach, and 89.03 % (F1 score) for the onestep integrated algorithm. We also consider the effect of using different sets of morphological features for parsing the Quran, comparing our results to recent work on Modern Standard Arabic.
This chapter presents a meta-search approach, meant to deliver bibliography from the internet, according to trainees’ results obtained at an e-assessment task. The bibliography consists of web pages related to the knowledge gaps of the trainees. The meta-search engine is part of an education recommender system, attached to an e-assessment application for project management knowledge. Meta-search means that, for a specific query (or mistake made by the trainee), several search mechanisms for suitable bibliography (further reading) could be applied. The lists of results delivered by the standard search mechanisms are used to build thematically homogenous groups using an ontology-based clustering algorithm. The clustering process uses an educational ontology and WordNet lexical database to create its categories. The research is presented in the context of recommender systems and their various applications to the education domain.
Abstract. FrameNet frames have been used to develop lexical databases and annotated corpora for different languages. This paper analyses the use of FrameNet frames to build a legal ontology for the Brazilian Law. In order to discuss the problems of such approach to ontology development, the lexical units evoking the Criminal_process frame were contrasted in English and Portuguese. Frame divergence between languages has consequences not only for legal ontology development but also for the development of legal lexical resources, such as lexical databases and corpora annotation. 1.
We present an approach of identifying the most prominent text/sentences using various shallow linguistic features, taking degree of connectiveness among the text units into consideration so as to minimize the poorly linked sentences in the resulting summary. As per the limitations of the current summarizing systems, the summary generated by those systems contains poorly linked sentences and are not topically salient. Thus, the paper aims at highlighting the effect of lexical chain scoring after the nouns and compound nouns are chained by searching for lexical cohesive relationships between words in the text using WordNet and using lexicographical relationships such as synonymy and hyponyms. In this paper, our algorithm ranks sentences based on the sum of the scores of the words in each sentence involving approaches like term frequencies, location of sentence in the text, cue words and phrases, word occurrences, and measuring lexical similarity(measuring chain score, word score and finally sentence score) for ranking the text units. We then identified and extracted high scored sentences and then the Vector Space approach is used to measure the relatedness/similarity between the extracted sentence and the topic words involving again the WordNet lexical database relationships to prioritise the topically related sentences. A threshold angle between the two vectors is predefined experimentally to which the ranked/scored sentences to be dropped and which the significant sentences with ranking/scores higher than threshold to be extracted. Note that the value of threshold is predetermined based on the percentage of output summary required to be generated.
Therapist self-disclosure has been theorized and found to have both positive and negative effects. These effects depend, in part, on the nature of the disclosure. This study sought to examine the differential effects of therapist disclosures of more and less resolved countertransference issues on perceptions of therapists and therapy sessions. Using an analogue method, undergraduate participants (N = 116) were randomly assigned to watch one of two videos in which a therapist disclosed personal issues that were relatively resolved or relatively unresolved. As hypothesized, therapist disclosure of issues that were more resolved caused the therapist to be rated as more attractive and trustworthy and instilled greater hope than therapist disclosure of less resolved issues. The type of therapist disclosure, however, did not affect ratings of the expertness of the therapist, the depth or smoothness of the session, or the perceived universality between client and therapist. Implications of the results for the judicious use of self-disclosure are discussed.
This paper considers how implemented grammars can enhance descriptive ones. An implemented grammar encodes the analyses in machine readable form, facilitating automatic annotation of morphological, syntactic and semantic structures. A grammar augmented with a collection of such structures would enable, for example, a reader to search for items in which a PP argument fills the third most prominent semantic role.
Recent advances in parsing technology have made treebank parsing with discontinuous constituents possible, with parser output of competitive quality (Kallmeyer and Maier, 2010). We apply Data-Oriented Parsing (DOP) to a grammar formalism that allows for discontinuous trees (LCFRS). Decisions during parsing are conditioned on all possible fragments, resulting in improved performance. Despite the fact that both DOP and discontinuity present formidable challenges in terms of computational complexity, the model is reasonably efficient, and surpasses the state of the art in discontinuous parsing. 1
This article provides recent empirical evidence to support the argument that the everyday politics of race and fear of the non-desired Other still persist in Australia more than half a century after the official demise of the White Australia Policy. The article sheds so me insight into how Australian immigration policies are now deliberately designed to normalise and assimilate new migrants into narrow Anglo-Saxon cultural and linguistic norms, thereby inadvertently excluding people from culturally and linguistically diverse backgrounds who need Australian citizenship the most. The argument of the article is based on outcomes of a study on personal stories of African refugee background Australian citizens regarding their experiences with the Australian citizenship test; their opinions about the literacy-for-citizenship requirement; and their ideas about being and becoming Australian. The participants to the study expressed strong reservations with the idea of having to undertake a formal citizenship test that neither improves their understanding of the everyday way of life in Australia nor opens avenues for greater opportunities for socio-economic participation and recognition of the linguistic and cultural identities they bring to Australia.
OBJECTIVE: The purpose of this article is to evaluate the use of the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) technique for artifact reduction and overall image quality improvement for intermediate-weighted and T2-weighted MRI of the shoulder. SUBJECTS AND METHODS: One hundred eleven patients undergoing MR arthrography of the shoulder were included. A coronal oblique intermediate-weighted turbo spin-echo (TSE) sequence with fat suppression and a sagittal oblique T2-weighted TSE sequence with fat suppression were obtained without (standard) and with the PROPELLER technique. Scanning time increased from 3 minutes 17 seconds to 4 minutes 17 seconds (coronal oblique plane) and from 2 minutes 52 seconds to 4 minutes 10 seconds (sagittal oblique) using PROPELLER. Two radiologists graded image artifacts, overall image quality, and delineation of several anatomic structures on a 5-point scale (5, no artifact, optimal diagnostic quality; and 1, severe artifacts, diagnostically not usable). The Wilcoxon signed rank test was used to compare the data of the standard and PROPELLER images. RESULTS: Motion artifacts were significantly reduced in PROPELLER images (p < 0.001). Observer 1 rated motion artifacts with diagnostic impairment in one patient on coronal oblique PROPELLER images compared with 33 patients on standard images. Ratings for the sequences with PROPELLER were significantly better for overall image quality (p < 0.001). Observer 1 noted an overall image quality with diagnostic impairment in nine patients on sagittal oblique PROPELLER images compared with 23 patients on standard MRI. CONCLUSION: The PROPELLER technique for MRI of the shoulder reduces the number of sequences with diagnostic impairment as a result of motion artifacts and increases image quality compared with standard TSE sequences. PROPELLER sequences increase the acquisition time.
Abstract The language of a speech community can only act as an identity marker for all of its speakers if linguistic norms are widely shared and if a minimal number of language varieties are spoken. This article examines briefly how a linguistic norm came to serve the whole of Iceland and how a situation of relative linguistic homogeneity was maintained for centuries. Sociolinguistic theory tells us that the speech community that we can reconstruct for early Iceland should lead to the establishment and maintenance of local norms. However, Iceland, arguably monodialectal, was certainly characterized by long-term linguistic homogeneity and remained a society where nucleated settlements barely formed over a thousand-year period. Scholars have argued that a mixture of dialects leveled shortly after the settlement of Iceland in the ninth century (Settlement). Studies show that dialect leveling requires dialect mixing, the convergence of people on one place, and sustained linguistic contact between the speakers. The settlement pattern of Iceland is indicative of population divergence (not convergence) and there is limited evidence of sustained contact. It is therefore proposed that the dialect leveling might be linked instead with significant population movements and social upheaval in mainland Scandinavia in the immediate pre-Viking period. The variety of Norse that was taken westward across the Atlantic might itself already have been the result of several earlier stages of mixing and koineization. It is only by combining linguistic, historical, and archaeological knowledge that this problem of how one linguistic norm came to serve the whole of Iceland can be understood.
This paper introduces, an XML format developed to serialise the object model defined by the ISO Syntactic Annotation Framework SynAF. Basing on widespread best practices we adapt a popular XML format for syntactic annotations, TigerXML, with additional features to support a variety of syntactic phenomena including constituent and dependency structures, binding, and different node types such as compounds or empty elements. We also define interfaces to other formats and standards including the Morpho-syntactic Annotation Framework MAF and the ISOCat Data Category Registry. Finally a case study of the German Treebank TueBa-D/Z is presented, showcasing the handling of constituent structures, topological fields and coreference annotation.
Psycholinguistic studies on whether classifiers facilitate processing object-extracted relative clauses (RC) in Mandarin have often made use of a classifier mismatch-match configuration, wherein a preceding classifier mismatches the following RC-subject but matches the modified head noun. However, an examination of the Chinese Treebank corpus 5.0 shows this configuration rarely occurs. None of the 10 tokens of pre-RC classifiers conforms to the mismatch-match configuration in a real sense. Instead, either a dropped RC-subject or some intervening item successfully avoids anticipated lexical disruption effects induced by a mismatching classifier. The results of analysis suggest that the constructed examples used in previous psycholinguistic studies may not realistically test natural language processing procedures.