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Register a new userPredicate Representations and Polysemy in VerbNet Semantic Parsing
تمثيلات المسند و Polysemy في تحليل Verbnet الدلالي
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على الرغم من التطورات الحديثة في الدور الدوالي الذي يدفعه ترميز النص المدرب مسبقا مثل بيرت، فإن الأداء يتخلف عند تطبيقه على المسندات لاحظ بشكل غير منتظم أثناء التدريب أو إلى الجمل في مجالات جديدة. في هذا العمل، يمكننا التحقيق في كيفية تحسين أداء وضع العلامات على الدوران المنخفض التردد والبيانات خارج نطاق البيانات باستخدام Verbnet، معجم فعل يضم الأفعال إلى فئات هرمية تستند إلى سلوك النحوية والدلية المشتركة وتحدد التمثيل الدلالي وصف العلاقات بين الحجج. نجد أن فئات Verbnet توفر مستوى فعال من التجريد، وتحسين التعميم على المساكن المنخفض التردد من خلال السماح لهم بالتعلم من الأمثلة التدريبية للندوات الأخرى المنتمدة إلى نفس الفصل. نجد أيضا أن التدريب المشترك لعلامات الدور الحرفية والأزهار المسند للفئات الحرفية للأفعال البسيطة يؤدي إلى تحسينات في كلا المهام، مما يدعم بشكل طبيعي استخراج التمثيلات الدلالية في فيربيت.
Despite recent advances in semantic role labeling propelled by pre-trained text encoders like BERT, performance lags behind when applied to predicates observed infrequently during training or to sentences in new domains. In this work, we investigate how role labeling performance on low-frequency predicates and out-of-domain data can be further improved by using VerbNet, a verb lexicon that groups verbs into hierarchical classes based on shared syntactic and semantic behavior and defines semantic representations describing relations between arguments. We find that VerbNet classes provide an effective level of abstraction, improving generalization on low-frequency predicates by allowing them to learn from the training examples of other predicates belonging to the same class. We also find that joint training of VerbNet role labeling and predicate disambiguation of VerbNet classes for polysemous verbs leads to improvements in both tasks, naturally supporting the extraction of VerbNet's semantic representations.
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Tracking entity states is a natural language processing task assumed to require human annotation. In order to reduce the time and expenses associated with annotation, we introduce a new method to automatically extract entity states, including locatio
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One of the central aspects of contextualised language models is that they should be able to distinguish the meaning of lexically ambiguous words by their contexts. In this paper we investigate the extent to which the contextualised embeddings of word
forms that display multiplicity of sense reflect traditional distinctions of polysemy and homonymy. To this end, we introduce an extended, human-annotated dataset of graded word sense similarity and co-predication acceptability, and evaluate how well the similarity of embeddings predicts similarity in meaning. Both types of human judgements indicate that the similarity of polysemic interpretations falls in a continuum between identity of meaning and homonymy. However, we also observe significant differences within the similarity ratings of polysemes, forming consistent patterns for different types of polysemic sense alternation. Our dataset thus appears to capture a substantial part of the complexity of lexical ambiguity, and can provide a realistic test bed for contextualised embeddings. Among the tested models, BERT Large shows the strongest correlation with the collected word sense similarity ratings, but struggles to consistently replicate the observed similarity patterns. When clustering ambiguous word forms based on their embeddings, the model displays high confidence in discerning homonyms and some types of polysemic alternations, but consistently fails for others.
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