Analysis of Named Entity Recognition and Linking for Tweets

Abstract

Applying natural language processing for mining and intelligent information access to tweets (a form of microblog) is a challenging, emerging research area. Unlike carefully authored news text and other longer content, tweets pose a number of new challenges, due to their short, noisy, context-dependent, and dynamic nature. Information extraction from tweets is typically performed in a pipeline, comprising consecutive stages of language identification, tokenisation, part-of-speech tagging, named entity recognition and entity disambiguation (e.g. with respect to DBpedia). In this work, we describe a new Twitter entity disambiguation dataset, and conduct an empirical analysis of named entity recognition and disambiguation, investigating how robust a number of state-of-the-art systems are on such noisy texts, what the main sources of error are, and which problems should be further investigated to improve the state of the art.

35 pages, accepted to journal Information Processing and Management

References in corpus (2)

• Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition
• USFD at KBP 2011: Entity Linking, Slot Filling and Temporal Bounding

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• Interpretable Multi-dataset Evaluation for Named Entity Recognition
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• Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task
• Recognizing Musical Entities in User-generated Content
• Evaluating end-to-end entity linking on domain-specific knowledge bases: Learning about ancient technologies from museum collections
• TMR: Evaluating NER Recall on Tough Mentions
• WikiGUM: Exhaustive Entity Linking for Wikification in 12 Genres
• Entities of Interest