Neural Fine-Grained Entity Type Classification

Neural Fine-grained Entity Type Classification is the task of assigning a fine-grained type (e.g., athlete or artist instead of just person) to an entity mentioned in text. Fine-grained types have been shown superior to the standard generic types for most NLP tasks, including information extraction which is our main ...

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Semantic Understanding of Relational Data on the Web

Tables and structured lists on Web pages are a potential source of valuable information which are often missing from state-of-the-art knowledge graphs, for various reasons. Our work is concerned with annotating such relational data sources with semantic information so that it can be queried or otherwise integrated into knowledge graphs ...

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Walking NED

Two crucial tasks in natural language understanding have to do with named entities, which are the persons, organizations, locations, etc. that are explicitly mentioned in text using proper nouns: Named Entity Recognition (NER) which is finding mentions to entities in the text; and Named Entity Disambiguation (NED), also known as ...

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Sentiment Analysis to Help Prevent Cyberbullying

Cyberbullying is a a real and worrisome problem affecting many school children across the world. Sadly, it is becoming customary to hear tragic stories in the news about cases of cyberbullying leading to self harm or worse. Although these extreme cases are a minority, a large number of children go ...

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Going Beyond Infoboxes to Extract Information from Wikipedia

Wikipedia remains one of the primary sources of knowledge on the Web, and for many good reasons. First, it is open. Second, it is edited by a large team of passionate and dedicated editors, who are truly committed to providing informative and accurate factual information. Finally, it is subject to ...

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