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Register a new userRelation Extraction with Word Graphs from N-grams
استخراج العلاقة مع الرسوم البيانية Word من N-Grams
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أحدث دراسات لاستخراج العلاقات (إعادة) الاستفادة من شجرة التبعية من جملة الإدخال لإدماج المعلومات السياقية التي يحركها بناء الجملة لتحسين الأداء النموذجي، مع القليل من الاهتمام المدفوع للقيود حيث محلل التبعية عالية الجودة في معظم الحالات غير متوفرة، خاصة في سيناريوهات البناء. لمعالجة هذا القيد، في هذه الورقة، نقترح شبكات اتصال بياني اختصاصية (A-GCN) لتحسين الأساليب العصبية بطريقة غير مرئية لبناء الرسم البياني للسياق، دون الاعتماد على وجود محلل التبعية. على وجه التحديد، نقوم بإنشاء الرسم البياني من N-Grams المستخرجة من معجم مبني من المعلومات المتبادلة غير التاريخية (PMI) وتطبيق الانتباه عبر الرسم البياني. لذلك، يتم مرجح أزواج كلمة مختلفة من السياقات داخل وعبر N-Grams في النموذج وتسهيل إعادة استخدامها وفقا لذلك. النتائج التجريبية مع المزيد من التحليلات على مجموعات بيانات قياسية باللغة الإنجليزية لإظهار فعالية نهجنا، حيث يلاحظ أداء حديثة على كلا البيانات.
Most recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to incorporate syntax-driven contextual information to improve model performance, with little attention paid to the limitation where high-quality dependency parsers in most cases unavailable, especially for in-domain scenarios. To address this limitation, in this paper, we propose attentive graph convolutional networks (A-GCN) to improve neural RE methods with an unsupervised manner to build the context graph, without relying on the existence of a dependency parser. Specifically, we construct the graph from n-grams extracted from a lexicon built from pointwise mutual information (PMI) and apply attention over the graph. Therefore, different word pairs from the contexts within and across n-grams are weighted in the model and facilitate RE accordingly. Experimental results with further analyses on two English benchmark datasets for RE demonstrate the effectiveness of our approach, where state-of-the-art performance is observed on both datasets.
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