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Register a new userIncreasing Sentence-Level Comprehension Through Text Classification of Epistemic Functions
زيادة فهم مستوى الجملة من خلال تصنيف النص للوظائف المعرفية
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كلمة embeddings تلتقط المعنى الدلالي للكلمات الفردية.كيفية سد المعرفة اللغوية على مستوى Word مع تمثيل لغة مستوى الجملة هو مشكلة مفتوحة.تفحص هذه الورقة ما إذا كان يمكن تحقيق تمثيلات مستوى الجملة من خلال بناء قاعدة بيانات جملة مخصصة تركز على جانب واحد من معنى الجملة.إن الجوانب الدلالية الثلاثة المنفصلة الخاصة بنا هي ما إذا كانت الجملة: (1) تقوم (1) بإجراء علاقات سببية، (2) تشير إلى أن شيئين مرتبطين ببعضهما البعض، و (3) يعبر عن معلومات أو معرفة.توفر المصنفات الثلاثة معلومات معرفية حول محتوى الجملة.
Word embeddings capture semantic meaning of individual words. How to bridge word-level linguistic knowledge with sentence-level language representation is an open problem. This paper examines whether sentence-level representations can be achieved by building a custom sentence database focusing on one aspect of a sentence's meaning. Our three separate semantic aspects are whether the sentence: (1) communicates a causal relationship, (2) indicates that two things are correlated with each other, and (3) expresses information or knowledge. The three classifiers provide epistemic information about a sentence's content.
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