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Register a new userLooking for a Role for Word Embeddings in Eye-Tracking Features Prediction: Does Semantic Similarity Help?
تبحث عن دور ل Adgeddings في توقعات تتبع العين التنبؤ: هل تساعد التشابه الدلالي؟
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اقترحت الدراسات النفسية تتبع العين أن التماسك الدلالي في السياق والتنبؤية تؤثر على معالجة اللغة خلال نشاط القراءة.في هذه الدراسة، يمكننا التحقيق في الارتباط بين أوجه التشابه الجيبيني المحسوب مع نماذج تضمين كلمة (كلا من البيانات الثابتة والسياقية) وبيانات تتبع العين من اثنين من القراءة الطبيعية.درسنا أيضا ارتباطات الدرجات المفاجئة المحسوبة بثلاث نماذج لغة حديثة.تظهر نتائجنا ارتباطا قويا للدرجات المحسوبة مع بيرت والقفازات، مما يشير إلى أن التشابه يمكن أن تلعب دورا مهما في أوقات القراءة النمذجة.
Eye-tracking psycholinguistic studies have suggested that context-word semantic coherence and predictability influence language processing during the reading activity. In this study, we investigate the correlation between the cosine similarities computed with word embedding models (both static and contextualized) and eye-tracking data from two naturalistic reading corpora. We also studied the correlations of surprisal scores computed with three state-of-the-art language models. Our results show strong correlation for the scores computed with BERT and GloVe, suggesting that similarity can play an important role in modeling reading times.
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Semantic textual similarity (STS) systems estimate the degree of the meaning similarity between two sentences. Cross-lingual STS systems estimate the degree of the meaning similarity between two sentences, each in a different language. State-of-the-a
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Sarcasm detection is important for several NLP tasks such as sentiment identification in product reviews, user feedback, and online forums. It is a challenging task requiring a deep understanding of language, context, and world knowledge. In this pap
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