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Register a new userImproving Synonym Recommendation Using Sentence Context
تحسين توصية مرادف باستخدام سياق الجملة
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غالبا ما تشمل توصيات المرادف التقليدية اقتراحات غير مناسبة للسياقات المحددة للكاتب.نقترح نهج بسيط لتوصية مرادف السياق من خلال الجمع بين الرسوم البيانية القائمة على الإنسان، على سبيل المثالWordnet، مع نماذج اللغة المدربة مسبقا.نقوم بتقييم تقنيةنا عن طريق برعاية مجموعة من أزواج الجملة بكلمة الكلمة متوازنة عبر كوربورا وأجزاء الكلام، ثم قم بتسليم كل زوج جملة الكلمة مع مجموعة من المرادفات المناسبة للسياق.وجدنا أن نهج نموذج اللغة الأساسية لها دقة أعلى.الأساليب الاستفادة من سياق الجملة لها استدعاء أعلى.بشكل عام، كانت النهج السياقي الأخير لديه أعلى درجة F.
Traditional synonym recommendations often include ill-suited suggestions for writer's specific contexts. We propose a simple approach for contextual synonym recommendation by combining existing human-curated thesauri, e.g. WordNet, with pre-trained language models. We evaluate our technique by curating a set of word-sentence pairs balanced across corpora and parts of speech, then annotating each word-sentence pair with the contextually appropriate set of synonyms. We found that basic language model approaches have higher precision. Approaches leveraging sentence context have higher recall. Overall, the latter contextual approach had the highest F-score.
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