A bootstrapping approach for training a NER with Conditional Random Fields

10 Outubro 2011
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J. Teixeira, L. Sarmento and E. Oliveira

The 4th Track on Text Mining and Applications (TeMA 2011) in the 15th Portuguese Conference of Artificial Intelligence (EPIA), October 2011, Lisbon, Portugal

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In this paper we present a bootstrapping approach for training a Named Entity Recognition (NER) system. Our method starts by annotating persons’ names on a dataset of 50,000 news items. This is performed using a simple dictionary-based approach. Using such train- ing set we build a classification model based on Conditional Random Fields (CRF). We then use the inferred classification model to perform additional annotations of the initial seed corpus, which is then used for training a new classification model. This cycle is repeated until the NER model stabilizes. We evaluate each of the bootstrapping iterations by calculating: (i) the precision and recall of the NER model in annotating a small gold-standard collection (HAREM); (ii) the precision and recall of the CRF bootstrapping annotation method over a small sample of news; and (iii) the correctness and the number of new names identified. Additionally, we compare the NER model with a dictionary-based approach, our baseline method. Results show that our bootstrapping approach sta- bilizes after 7 iterations, achieving high values of precision (83%) and recall (68%).

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