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Register a new userA Unified Span-Based Approach for Opinion Mining with Syntactic Constituents
نهج تم توزيعه
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حقق تعدين الرأي الرأي الدقيق (OM) جاذبية متزايدة في مجتمع معالجة اللغات الطبيعية (NLP)، والتي تهدف إلى إيجاد هياكل الرأي التي عبرت عن آرائها تجاه ما "في جملة واحدة. في هذا العمل، بدافع من تمثيلها المستندة إلى تعبيرات الرأي والأدوار، نقترح نهجا موحدا قائما على إعداد OM في نهاية إلى نهاية. علاوة على ذلك، مستوحاة من الشكليات الموحدة المستندة المستندة إلى OM و EM و Constitioning، نستكشف طريقتين مختلفتين (التعلم متعدد المهام والشبكة العصبية التنافيلية) لإدماج مكونات النحوية في النموذج المقترح للمساعدة OM. نحن نقوم بإجراء تجارب على DataSet MPQA 2.0 شائع الاستخدام. تظهر النتائج التجريبية أن نهجنا المقرر أن يحقق النهج المستند إليه التحسينات المهمة على الأعمال السابقة في درجة F1 الدقيقة ويقلل من عدد تعبيرات الرأي والأدوار المتوقعة بشكل خاطئ، يظهر فعالية طريقتنا. بالإضافة إلى ذلك، فإن دمج الناخبين النحوي يحقق تحسينات واعدة على أساس الأساس القوي المعزز من خلال تمثيلات الكلمات السياقية.
Fine-grained opinion mining (OM) has achieved increasing attraction in the natural language processing (NLP) community, which aims to find the opinion structures of Who expressed what opinions towards what'' in one sentence. In this work, motivated by its span-based representations of opinion expressions and roles, we propose a unified span-based approach for the end-to-end OM setting. Furthermore, inspired by the unified span-based formalism of OM and constituent parsing, we explore two different methods (multi-task learning and graph convolutional neural network) to integrate syntactic constituents into the proposed model to help OM. We conduct experiments on the commonly used MPQA 2.0 dataset. The experimental results show that our proposed unified span-based approach achieves significant improvements over previous works in the exact F1 score and reduces the number of wrongly-predicted opinion expressions and roles, showing the effectiveness of our method. In addition, incorporating the syntactic constituents achieves promising improvements over the strong baseline enhanced by contextualized word representations.
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