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Register a new userEDTC: A Corpus for Discourse-Level Topic Chain Parsing
EDTC: Corpus لتخليص سلسلة موضع مستوى الخطاب
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من المعروف أن تحليل الخطاب أمرا أساسيا في معالجة اللغة الطبيعية.في هذا البحث، نقدم نظرة ثاقبة حول تحليل سلسلة موضوعات مستوى الخطاب (DTC) التي تهدف إلى اكتشاف مواضيع جديدة والتحقيق في كيفية تطور هذه الموضوعات بمرور الوقت داخل مقال.لمعالجة عدم وجود بيانات، نساهم في كوربس خطاب جديد مع الرسوم البيانية التبعية على غرار DTC المشروح عند المقالات الإخبارية.على وجه الخصوص، نضمن الموثوقية العالية للدور من خلال الاستفادة من استراتيجية توضيحية من خطوتين لبناء البيانات وتصفية التعليقات التوضيحية بدرجات ثقة منخفضة.بناء على Corpus المشروح، نقدم نظاما بسيطا ولكنك قوي لتخليص سلسلة موضوع الخطاب التلقائي.
Discourse analysis has long been known to be fundamental in natural language processing. In this research, we present our insight on discourse-level topic chain (DTC) parsing which aims at discovering new topics and investigating how these topics evolve over time within an article. To address the lack of data, we contribute a new discourse corpus with DTC-style dependency graphs annotated upon news articles. In particular, we ensure the high reliability of the corpus by utilizing a two-step annotation strategy to build the data and filtering out the annotations with low confidence scores. Based on the annotated corpus, we introduce a simple yet robust system for automatic discourse-level topic chain parsing.
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