Text Chunking Using Transformation-Based Learning
Lance Ramshaw,Mitchell Marcus +1 more
- pp 157-176
TLDR
This work has shown that the transformation-based learning approach can be applied at a higher level of textual interpretation for locating chunks in the tagged text, including non-recursive “baseNP” chunks.Abstract:
Transformation-based learning, a technique introduced by Eric Brill (1993b), has been shown to do part-of-speech tagging with fairly high accuracy. This same method can be applied at a higher level of textual interpretation for locating chunks in the tagged text, including non-recursive “baseNP” chunks. For this purpose, it is convenient to view chunking as a tagging problem by encoding the chunk structure in new tags attached to each word. In automatic tests using Treebank-derived data, this technique achieved recall and precision rates of roughly 93% for baseNP chunks (trained on 950K words) and 88% for somewhat more complex chunks that partition the sentence (trained on 200K words). Working in this new application and with larger template and training sets has also required some interesting adaptations to the transformation-based learning approach.read more
Citations
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Book ChapterDOI
Chunking in Turkish with Conditional Random Fields
TL;DR: This work used the data that was generated by manually translating a subset of the Penn Treebank to automatically identify and label chunks in their Turkish translations and used conditional random fields (CRF) to train a model over the annotated data.
Proceedings ArticleDOI
A comparative study of segment representation for biomedical named entity recognition
TL;DR: Support Vector Machines (SVMs) and Conditional Random fields (CRFs) are used to train different BioNER models with the benchmark JNLPBA 2004 and i2b2 2010 shared task dataset using different SRs and the performance of SR models shows that more complex the model worse performance of f-score.
Proceedings Article
A Platform for Event Extraction in Hindi
TL;DR: An Event Extraction framework for Hindi language is presented by creating an annotated resource for benchmarking, and then developing deep learning based models to set as the baselines, and developing deeplearning based models for Event Trigger Detection and Classification, Argument Detection and classification and Event-Argument Linking.
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Urdu Noun Phrase Chunking: Hybrid Approach
TL;DR: The approach used in this work is hybrid that combines statistical method and hand crafted rules and it is observed that the input sequence which is successful in this regard is merging of POS annotation with IOB annotation.
References
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