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Register a new userSentence Structure and Word Relationship Modeling for Emphasis Selection
هيكل الجملة ونمذجة علاقة الكلمة للتأكيد
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التحديد التركيز هو مهمة مقترحة حديثا تركز على اختيار الكلمات للتأكيد في جمل قصيرة.الطريقة التقليدية تنظر فقط في معلومات التسلسل من الجملة مع تجاهل هيكل الجملة الغنية ومعلومات علاقة الكلمة.في هذه الورقة، نقترح إطارا جديدا يعتبر هيكل الجملة عبر رسم بياني هيكل الجملة وعلاقة كلمة عبر الرسم البياني للكلمة التشابه.يتم اشتقاق الرسم البياني هيكل الجملة من شجرة التحليل من الجملة.يسمح الرسم البياني للكلمة التشابه العقد بمشاركة المعلومات مع جيرانها لأننا نقول أنه في التركيز على التحديد، من المرجح أن يتم التأكيد على كلمات مماثلة معا.يتم استخدام الشبكات العصبية الرسم البياني لتعلم تمثيل كل عقدة لهذين الرسوم البيانية.تظهر النتائج التجريبية أن إطارنا يمكن أن يحقق أداء متفوقا.
Emphasis Selection is a newly proposed task which focuses on choosing words for emphasis in short sentences. Traditional methods only consider the sequence information of a sentence while ignoring the rich sentence structure and word relationship information. In this paper, we propose a new framework that considers sentence structure via a sentence structure graph and word relationship via a word similarity graph. The sentence structure graph is derived from the parse tree of a sentence. The word similarity graph allows nodes to share information with their neighbors since we argue that in emphasis selection, similar words are more likely to be emphasized together. Graph neural networks are employed to learn the representation of each node of these two graphs. Experimental results demonstrate that our framework can achieve superior performance.
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