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الاعتراف الدلالي المعجمي
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في دلالات معجمية، يتم علاج تجزئة الجملة الكاملة ووضع القطاع من الظواهر المختلفة بشكل عام بشكل منفصل، على الرغم من الترابط.نحن نفترض أن مهمة الاعتراف الدلالية المعتمدة الموحدة هي وسيلة فعالة لتغليف الأساليب التوضيحية سابقا من التوضيحية، بما في ذلك التعبير / التصنيف التعبير المتعدد الكلمات والعلامات الفائقة.باستخدام Streusle Corpus، نربط تسلسل CRF العصبي Tagger وتقييم أدائه على طول محاور مختلفة من التوضيحية.نظرا لأن مجموعة العلامات تعميم تعميم المهام السابقة (PARSEME، DIMSUM)، فإننا نتقييم بالإضافة إلى ذلك مدى جودة تعميم النموذج إلى مجموعات الاختبار هذه، وإيجاد أنها تقترب أو تجاوز النماذج الحالية على الرغم من التدريب فقط على Streusle.ينشئ عملنا أيضا نماذج أساسية ومقاييس التقييم لنمذجة متكاملة ودقيقة للدلالات المعجمية، مما يسهل العمل في المستقبل في هذا المجال.
In lexical semantics, full-sentence segmentation and segment labeling of various phenomena are generally treated separately, despite their interdependence. We hypothesize that a unified lexical semantic recognition task is an effective way to encapsulate previously disparate styles of annotation, including multiword expression identification / classification and supersense tagging. Using the STREUSLE corpus, we train a neural CRF sequence tagger and evaluate its performance along various axes of annotation. As the label set generalizes that of previous tasks (PARSEME, DiMSUM), we additionally evaluate how well the model generalizes to those test sets, finding that it approaches or surpasses existing models despite training only on STREUSLE. Our work also establishes baseline models and evaluation metrics for integrated and accurate modeling of lexical semantics, facilitating future work in this area.
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