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Register a new userTarget-specified Sequence Labeling with Multi-head Self-attention for Target-oriented Opinion Words Extraction
تسلسل تسلسل محدد الهدف مع اهتمام الذات متعدد الرأس لاستخراج كلمات الرأي الموجهة نحو الهدف
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استخراج الرأي المستهدف واستخراج الأجل رأي هما مهمتان أساسيتان في تحليل المعرفات القائم على الجانب (ABASA). تركز العديد من الأعمال الأخيرة على ABSA على استخراج كلمات الرأي الموجهة نحو الهدف (TOWE) (Towe)، والتي تهدف إلى استخراج كلمات الرأي المقابلة للحصول على هدف رأي معين. يمكن تطبيق Towe أكثر استخداما على استخراج زوج الرأي في الجانب (AOPE) الذي يهدف إلى استخراج الجوانب (أي أهداف الرأي) وشروط الرأي في أزواج. في هذه الورقة، نقترح تسلسل تسلسل محدد بالهدف مع اهتمام الذات متعدد الرأس (TSMSA) ل Towe، حيث يمكن دمج أي نموذج لغة مدرب مسبقا مع اهتمام ذاتي متعدد الرأس بشكل مريح. كدراسة حالة، نقوم أيضا بتطوير هيكل متعدد المهام يدعى MT-TSMSA من أجل الجمع بين TSMSA لدينا مع وحدة استخراج الجانب والرأي. تشير النتائج التجريبية إلى أن TSMSA تتفوق على الأساليب القياسية على Towe بشكل ملحوظ؛ وفي الوقت نفسه، فإن أداء MT-TSMSA متشابه أو حتى أفضل من نماذج خط الأساس الحديثة.
Opinion target extraction and opinion term extraction are two fundamental tasks in Aspect Based Sentiment Analysis (ABSA). Many recent works on ABSA focus on Target-oriented Opinion Words (or Terms) Extraction (TOWE), which aims at extracting the corresponding opinion words for a given opinion target. TOWE can be further applied to Aspect-Opinion Pair Extraction (AOPE) which aims at extracting aspects (i.e., opinion targets) and opinion terms in pairs. In this paper, we propose Target-Specified sequence labeling with Multi-head Self-Attention (TSMSA) for TOWE, in which any pre-trained language model with multi-head self-attention can be integrated conveniently. As a case study, we also develop a Multi-Task structure named MT-TSMSA for AOPE by combining our TSMSA with an aspect and opinion term extraction module. Experimental results indicate that TSMSA outperforms the benchmark methods on TOWE significantly; meanwhile, the performance of MT-TSMSA is similar or even better than state-of-the-art AOPE baseline models.
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