Text Chunking Using Transformation-Based Learning
Lance Ramshaw,Mitchell Marcus +1 more
- pp 157-176
Reads0
Chats0
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
More filters
Proceedings ArticleDOI
Error-Driven Pruning of Treebank Grammars for Base Noun Phrase Identification
Claire Cardie,David Pierce +1 more
TL;DR: This paper proposed a corpus-based approach for finding base NPs by matching part-of-speech tag sequences, which achieved surprising accuracy in an evaluation on the Penn Treebank Wall Street Journal.
Proceedings ArticleDOI
Cascaded Markov Models
TL;DR: A new approach to partial parsing of context-free structures based on Markov Models that yields very good results for NP/PP chunking of German newspaper texts.
Journal ArticleDOI
Exploring entity recognition and disambiguation for cultural heritage collections
TL;DR: The article proposes an evaluation of the performance of three third-party entity extraction services through a comprehensive case study, based on the descriptive fields of the Smithsonian Cooper–Hewitt National Design Museum in New York, and offers a quantitative analysis of named entities retrieved by the services in terms of precision and recall compared with a manually annotated gold-standard corpus.
Journal ArticleDOI
Sequence labeling with multiple annotators
TL;DR: A probabilistic approach for sequence labeling using Conditional Random Fields (CRF) for situations where label sequences from multiple annotators are available but there is no actual ground truth.
Proceedings ArticleDOI
Exploring evidence for shallow parsing
TL;DR: It is concluded that directly learning to perform these tasks as shallow parsers do is advantageous over full parsers both in terms of performance and robustness to new and lower quality texts.
References
More filters
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☆
James Paul Gee,François Grosjean +1 more
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
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.