Microblog-genre noise and impact on semantic annotation accuracy
| Autor: | Leon Derczynski, Niraj Aswani, Kalina Bontcheva, Diana Maynard |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Information retrieval
Language identification business.industry Microblogging Computer science Information access computer.software_genre Pipeline (software) Named-entity recognition Semantic computing Semantic technology Social media Artificial intelligence Noise (video) business computer Natural language processing |
| Zdroj: | HT |
| Popis: | Using semantic technologies for mining and intelligent information access to microblogs 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. Semantic annotation of tweets is typically performed in a pipeline, comprising successive stages of language identification, tokenisation, part-of-speech tagging, named entity recognition and entity disambiguation (e.g. with respect to DBpedia). Consequently, errors are cumulative, and earlier-stage problems can severely reduce the performance of final stages. This paper presents a characterisation of genre-specific problems at each semantic annotation stage and the impact on subsequent stages. Critically, we evaluate impact on two high-level semantic annotation tasks: named entity detection and disambiguation. Our results demonstrate the importance of making approaches specific to the genre, and indicate a diminishing returns effect that reduces the effectiveness of complex text normalisation. |
| Databáze: | OpenAIRE |
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