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- 241 creator adarsh-alex.
- 241 creator amit-sheth.
- 241 creator krishnaprasad-thirunarayan.
- 241 creator pablo-mendes.
- 241 creator sujan-perera.
- 241 type InProceedings.
- 241 label "Implicit Entity Linking in Tweets".
- 241 sameAs 241.
- 241 abstract "Over the years, Twitter has become one of the largest communication platforms providing key data to various applications such as brand monitoring, trend detection, among others. Natural language understanding from tweets is particularly difficult due to prevalent short length and unconventional grammar. Entity linking associates text to knowledge bases in order to provide unambiguous references and additional context to help with understanding. State-of-the-art techniques have focused on linking explicitly mentioned entities on tweets with reasonable success. However, we argue that in addition to explicit mentions -- i.e. `You guys have no idea how wet the movie Gravity makes me' -- entities (movie Gravity) can also be mentioned implicitly -- i.e. `This new space movie is crazy. you must watch it!'. This paper introduces the problem of implicit entity linking in tweets. We propose an approach that models the entities by exploiting their factual and contextual knowledge. We demonstrate how to use these models to perform implicit entity linking on a ground truth dataset with 400 tweets from two domains, namely, movie and book. Specifically, we show: 1) the value of performing implicit entity linking along with explicit entity linking task, and the importance of exploiting contextual knowledge of an entity in linking their implicit mentions on tweets.".
- 241 hasAuthorList authorList.
- 241 isPartOf proceedings.
- 241 title "Implicit Entity Linking in Tweets".