Recognizing Named Entities in Tweets

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 359–367, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Recognizing Named Entities in Tweets Xiaohua Liu ‡ †, Shaodian Zhang∗ §, Furu Wei †, Ming Zhou † ‡School of Computer Science and Technology Harbin Institute of Technology, Harbin, 150001, China §Department of Computer Science and Engineering Shanghai Jiao Tong University, Shanghai, 200240, China †Microsoft Research Asia Beijing, 100190, China †{xiaoliu, fuwei, mingzhou}@microsoft.com § zhangsd.sjtu@gmail.com Abstract The challenges of Named Entities Recogni- tion (NER) for tweets lie in the insufficient information in a tweet and the unavailabil- ity of training data. We propose to com- bine a K-Nearest Neighbors (KNN) classi- fier with a linear Conditional Random Fields (CRF) model under a semi-supervised learn- ing framework to tackle these challenges. The KNN based classifier conducts pre-labeling to collect global coarse evidence across tweets while the CRF model conducts sequential la- beling to capture fine-grained information en- coded in a tweet. The semi-supervised learn- ing plus the gazetteers alleviate the lack of training data. Extensive experiments show the advantages of our method over the baselines as well as the effectiveness of KNN and semi- supervised learning. 1 Introduction Named Entities Recognition (NER) is generally un- derstood as the task of identifying mentions of rigid designators from text belonging to named-entity types such as persons, organizations and locations (Nadeau and Sekine, 2007). Proposed solutions to NER fall into three categories: 1) The rule-based (Krupka and Hausman, 1998); 2) the machine learn- ing based (Finkel and Manning, 2009; Singh et al., 2010) ; and 3) hybrid methods (Jansche and Abney, 2002). With the availability of annotated corpora, such as ACE05, Enron (Minkov et al., 2005) and ∗ This work has been done while the author was visiting Microsoft Research Asia. CoNLL03 (Tjong Kim Sang and De Meulder, 2003), the data driven methods now become the dominating methods. However, current NER mainly focuses on for- mal text such as news articles (Mccallum and Li, 2003; Etzioni et al., 2005). Exceptions include stud- ies on informal text such as emails, blogs, clini- cal notes (Wang, 2009). Because of the domain mismatch, current systems trained on non-tweets perform poorly on tweets, a new genre of text, which are short, informal, ungrammatical and noise prone. For example, the average F1 of the Stan- ford NER (Finkel et al., 2005) , which is trained on the CoNLL03 shared task data set and achieves state-of-the-art performance on that task, drops from 90.8% (Ratinov and Roth, 2009) to 45.8% on tweets. Thus, building a domain specific NER for tweets is necessary, which requires a lot of annotated tweets or rules. However, manually creating them is tedious and prohibitively unaffordable. Proposed solutions to alleviate this issue include: 1) Domain adaption, which aims to reuse the knowledge of the source do- main in a target domain. Two recent examples are Wu et al. (2009), which uses data that is informa- tive about the target domain and also easy …

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