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Neural Entity Linking for Company Names
LOCATION: TEL, Room Auditorium 3 (20th floor), Ernst-Reuter-Platz 7, 10587 Berlin
Date/Time: 29.07.2019, 14:15-15:00
SPEAKER: Zhanwang Chen (TU Berlin)
Entity Linking is the task to link entity mentions in text with their corresponding entities in a knowledge base. Entity Linking is essential in many NLP tasks such as improving the performances of knowledge network construction, knowledge fusion, information retrieval, and knowledge base population. A large percentage of the web data is in the form of natural language, which is highly ambiguous, primarily the named entities. To make ambiguously named entities mentioned in the web machine-readable, we need to link the named entities to structured databases with clean semantics. A named entity referring to a company can for instance occur in variations: a list of company names might contain "Dell Inc", but that company might also be referred to as "DELL", "Dell Technologies". Furthermore, the named entity "dell" may have different meanings depending on the context. Two different companies may both be referred to by the word "dell". There is little research specifically on company named entity linking. In this work, we study the performance of neural networks for entity linking of company names. We examine the impact of different neural components used in current neural entity linking systems such as mention embedding, entity embedding, attention mechanism in candidate ranking. We compare the effect of traditional static word embeddings like word2vec or GloVe with the more recent contextual embedding such as ELMo and character embeddings. Company name related entity linking methods will be analyzed, such as how to generate the alias of the company name, how to measure the ambiguity of company named entities and possible reasons for incorrect disambiguation of a company named entity.