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Register a new userA Novel Framework for Detecting Important Subevents from Crisis Events via Dynamic Semantic Graphs
إطار جديد للكشف عن الفرعية الهامة من أحداث الأزمات عبر الرسوم البيانية الدلالية الديناميكية
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الوسائط الاجتماعية هي أداة أساسية لتبادل المعلومات حول أحداث الأزمات، مثل الكوارث الطبيعية. يهدف الكشف عن الحدث إلى استخراج المعلومات في شكل حدث، ولكنه يعتبر كل حدث بمعزل، دون الجمع بين المعلومات عبر الجمل أو الأحداث. تحتوي العديد من الوظائف في أزمة NLP على معلومات متكررة أو تكميلية تحتاج إلى تجميعها (على سبيل المثال، عدد الأشخاص المحاصرين وموقعهم) للاستجابة للكوارث. على الرغم من أن النهج السابقة في أزمة NLP إجمالية المعلومات عبر المشاركات، إلا أنها تستخدم فقط التمثيلات الضحلة للمحتوى (على سبيل المثال، الكلمات الرئيسية)، والتي لا يمكن أن تمثل دلالة لحدث الأزمات وأسفرها بشكل كاف. في هذا العمل، نقترح إطارا جديدا لاستخراج الأحداث الفرعية النقدية من حدث أزمة واسعة النطاق من خلال الجمع بين المعلومات الهامة عبر التغريدات ذات الصلة. يقوم إطارنا أولا بتحويل جميع التغريدات من حدث الأزمات إلى مجموعة من الرسوم البيانية المرتبة الزمنية. ثم تستخرج الرسوم البيانية الفرعية التي تمثل العلاقات الدلالية التي تربط الأفعال والأسماء في 3 إلى 6 رسوم بيانية فرعية عقدة. يفعل ذلك من خلال تعلم الأوزان الحافة عبر الشبكات التنافسية الرسمية الرسمية (DGCNS) واستخراج الرسوم البيانية الفرعية الصغيرة ذات الصلة. تظهر تجاربنا أن هياكلنا المستخرجة (1) هي أحداث فرعية ذات مغزى دلالة و (2) تحتوي على معلومات مهمة لحدث الأزمة الكبيرة. علاوة على ذلك، نظير على أن نهجنا يتفوق بشكل كبير على خطوط الأساس للكشف عن الحدث، وتسليط الضوء على أهمية المعلومات التجميعية عبر تغريدات مهمتنا.
Social media is an essential tool to share information about crisis events, such as natural disasters. Event Detection aims at extracting information in the form of an event, but considers each event in isolation, without combining information across sentences or events. Many posts in Crisis NLP contain repetitive or complementary information which needs to be aggregated (e.g., the number of trapped people and their location) for disaster response. Although previous approaches in Crisis NLP aggregate information across posts, they only use shallow representations of the content (e.g., keywords), which cannot adequately represent the semantics of a crisis event and its sub-events. In this work, we propose a novel framework to extract critical sub-events from a large-scale crisis event by combining important information across relevant tweets. Our framework first converts all the tweets from a crisis event into a temporally-ordered set of graphs. Then it extracts sub-graphs that represent semantic relationships connecting verbs and nouns in 3 to 6 node sub-graphs. It does this by learning edge weights via Dynamic Graph Convolutional Networks (DGCNs) and extracting smaller, relevant sub-graphs. Our experiments show that our extracted structures (1) are semantically meaningful sub-events and (2) contain information important for the large crisis-event. Furthermore, we show that our approach significantly outperforms event detection baselines, highlighting the importance of aggregating information across tweets for our task.
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