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Text Chunking Using Transformation-Based Learning

Lance Ramshaw, +1 more
- pp 157-176
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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.

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Citations
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SciNER: Extracting Named Entities from Scientific Literature

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A memory-based shallow parser for spoken Dutch

TL;DR: It is concluded that existing memory-based parsing approaches can be applied to spoken Dutch successfully, but that there is room for improvement in the tagger-chunker.
References
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Book ChapterDOI

Parsing By Chunks

TL;DR: The typical chunk consists of a single content word surrounded by a constellation of function words, matching a fixed template, and the relationships between chunks are mediated more by lexical selection than by rigid templates.
Proceedings ArticleDOI

A Stochastic Parts Program and Noun Phrase Parser for Unrestricted Text

TL;DR: The authors used a linear-time dynamic programming algorithm to find an assignment of parts of speech to words that optimizes the product of (a) lexical probabilities (probability of observing part of speech i given word i) and (b) contextual probabilities (pb probability of observing n following partsof speech).
Proceedings Article

Some advances in transformation-based part of speech tagging

TL;DR: In this article, a rule-based approach to tagging unknown words is described, where the tagger-can be extended into a k-best tagger, where multiple tags can be assigned to words in some cases of uncertainty.
Journal ArticleDOI

Performance structures: A psycholinguistic and linguistic appraisal☆

TL;DR: In this paper, two lines of research are combined to deal with a long-standing problem in both fields: why the performance structures of sentences (structures based on experimental data, such as pausing and parsing values) are not fully accountable for by linguistic theories of phrase structure.
Book

A corpus-based approach to language learning

Eric D. Brill
TL;DR: A learning algorithm is described that takes a small structurally annotated corpus of text and a larger unannotated corpus as input, and automatically learns how to assign accurate structural descriptions to sentences not in the training corpus.
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