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409
Automatic Acquisition of Hyponyms from Large Text Corpora
, 1992
"... We describe a method for the automatic acquisition of the hyponymy lexical relation from unrestricted text. Two goals motivate the approach: (i) avoidante of the need for pre-encoded knowledge and (ii) applicability across a wide range of text. We identify a set of lexico-syntactic patterns that are ..."
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Cited by 1261 (4 self)
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We describe a method for the automatic acquisition of the hyponymy lexical relation from unrestricted text. Two goals motivate the approach: (i) avoidante of the need for pre-encoded knowledge and (ii) applicability across a wide range of text. We identify a set of lexico-syntactic patterns that are easily recognizable, that occur frequently and across text genre boundaries, and that indisputably indicate the lexical relation of interest. We describe a method for discovering these patterns and suggest that other lexical relations will also he acquirable iu this way. A subset of the acquisitiou algorithm is implemented and the results are used to augment and critique the structure of a large hand-built thesaurus. Extensions and applications to areas such as information retrieval are suggested.
Probabilistic Part-of-Speech Tagging Using Decision Trees
, 1994
"... In this paper, a new probabilistic tagging method is presented which avoids problems that Markov Model based taggers face, when they have to estimate transition probabilities from sparse data. In this tagging method, transition probabilities are estimated using a decision tree. Based on this method, ..."
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Cited by 1058 (9 self)
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In this paper, a new probabilistic tagging method is presented which avoids problems that Markov Model based taggers face, when they have to estimate transition probabilities from sparse data. In this tagging method, transition probabilities are estimated using a decision tree. Based on this method, a part-of-speech tagger (called TreeTagger) has been implemented which achieves 96.36 % accuracy on Penn-Treebank data which is better than that of a trigram tagger (96.06 %) on the same data.
Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging
- Computational Linguistics
, 1995
"... this paper, we will describe a simple rule-based approach to automated learning of linguistic knowledge. This approach has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance. We present a detailed case study of this learni ..."
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Cited by 924 (8 self)
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this paper, we will describe a simple rule-based approach to automated learning of linguistic knowledge. This approach has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance. We present a detailed case study of this learning method applied to part of speech tagging
A Simple Rule-Based Part of Speech Tagger
, 1992
"... Automatic part of speech tagging is an area of natural language processing where statistical techniques have been more successful than rule- based methods. In this paper, we present a sim- ple rule-based part of speech tagger which automatically acquires its rules and tags with accuracy coinparable ..."
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Cited by 596 (9 self)
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Automatic part of speech tagging is an area of natural language processing where statistical techniques have been more successful than rule- based methods. In this paper, we present a sim- ple rule-based part of speech tagger which automatically acquires its rules and tags with accuracy coinparable to stochastic taggers. The rule-based tagger has many advantages over these taggers, including: a vast reduction in stored information required, the perspicuity of a sinall set of meaningful rules, ease of finding and implementing improvements to the tagger, and better portability from one tag set, cor- pus genre or language to another. Perhaps the biggest contribution of this work is in demonstrating that the stochastic method is not the only viable method for part of speech tagging. The fact that a simple rule-based tagger that automatically learns its rules can perform so well should offer encouragement for researchers to further explore rule-based tagging, searching for a better and more expressive set of rule templates and other variations on the simple but effective theme described below.
TnT - A Statistical Part-Of-Speech Tagger
, 2000
"... Trigrams'n'Tags (TnT) is an efficient statistical part-of-speech tagger. Contrary to claims found elsewhere in the literature, we argue that a tagger based on Markov models performs at least as well as other current approaches, including the Maximum Entropy framework. A recent comparison h ..."
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Cited by 540 (5 self)
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Trigrams'n'Tags (TnT) is an efficient statistical part-of-speech tagger. Contrary to claims found elsewhere in the literature, we argue that a tagger based on Markov models performs at least as well as other current approaches, including the Maximum Entropy framework. A recent comparison has even shown that TnT performs significantly better for the tested corpora. We describe the basic model of TnT, the techniques used for smoothing and for handling unknown words. Furthermore, we present evaluations on two corpora.
Effective Mapping of Biomedical Text to the UMLS Metathesaurus: The MetaMap Program
, 2001
"... The UMLS® Metathesaurus®, the largest thesaurus in
the biomedical domain, provides a representation of
biomedical knowledge consisting of concepts classified
by semantic type and both hierarchical and nonhierarchical
relationships among the concepts. This
knowledge has proved useful for many applica ..."
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Cited by 380 (4 self)
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The UMLS® Metathesaurus®, the largest thesaurus in
the biomedical domain, provides a representation of
biomedical knowledge consisting of concepts classified
by semantic type and both hierarchical and nonhierarchical
relationships among the concepts. This
knowledge has proved useful for many applications
including decision support systems, management of
patient records, information retrieval (IR) and data
mining. Gaining effective access to the knowledge is
critical to the success of these applications. This
paper describes MetaMap, a program developed at
the National Library of Medicine (NLM) to map biomedical
text to the Metathesaurus or, equivalently, to
discover Metathesaurus concepts referred to in text.
MetaMap uses a knowledge intensive approach based
on symbolic, natural language processing (NLP) and
computational linguistic techniques. Besides being
applied for both IR and data mining applications,
MetaMap is one of the foundations of NLM’s Indexing
Initiative System which is being applied to both semiautomatic
and fully automatic indexing of the biomedical
literature at the library.
Tagging English Text with a Probabilistic Model
, 1994
"... In this paper we present some experiments on the use of a probabilistic model to tag English text, i.e. to assign to each word the correct tag (part of speech) in the context of the sentence. The main novelty of these experiments is the use of untagged text in the training of the model. We have used ..."
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Cited by 307 (0 self)
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In this paper we present some experiments on the use of a probabilistic model to tag English text, i.e. to assign to each word the correct tag (part of speech) in the context of the sentence. The main novelty of these experiments is the use of untagged text in the training of the model. We have used a simple triclass Markov model and are looking for the best way to estimate the parameters of this model, depending on the kind and amount of training data provided. Two approaches in particular are compared and combined: using text that has been tagged by hand and computing relative frequency counts, using text without tags and training the model as a hidden Markov process, according to a Maximum Likelihood principle
Some advances in transformation-based part-of-speech tagging
- In Proceedings of the Twelfth National Conference on Artificial Intelligence
, 1994
"... Most recent research in trainable part of speech taggers has explored stochastic tagging. While these taggers obtain high accuracy, linguistic information is captured indirectly, typically in tens of thousands of lexical and contextual probabilities. In (Brill 1992), a trainable rule-based tagger wa ..."
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Cited by 294 (1 self)
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Most recent research in trainable part of speech taggers has explored stochastic tagging. While these taggers obtain high accuracy, linguistic information is captured indirectly, typically in tens of thousands of lexical and contextual probabilities. In (Brill 1992), a trainable rule-based tagger was described that obtained performance comparable to that of stochastic taggers, but captured relevant linguistic information in a small number of simple non-stochastic rules. In this paper, we describe a number of extensions to this rule-based tagger. First, we describe a method for expressing lexical relations in tagging that stochastic taggers are currently unable to express. Next, we show a rule-based approach to tagging unknown words. Finally, we show how the tagger can be extended into a k-best tagger, where multiple tags can be assigned to words in some cases of uncertainty.
Integrating Multiple Knowledge Sources to Disambiguate Word Sense: An Exemplar-Based Approach
- IN PROCEEDINGS OF THE 34TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS
, 1996
"... In this paper, we present a new approach for word sense disambiguation (WSD) using an exemplar-based learning algorithm. This approach ..."
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Cited by 279 (9 self)
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In this paper, we present a new approach for word sense disambiguation (WSD) using an exemplar-based learning algorithm. This approach
Improvements In Part-of-Speech Tagging With an Application To German
- In Proceedings of the ACL SIGDAT-Workshop
, 1995
"... This paper presents a couple of extensions to a basic Markov Model tagger (called TreeTagger) which improve its accuracy when trained on small corpora. The basic tagger was originally developed for English [Schmid, 1994]. The extensions together reduced error rates on a German test corpus by more th ..."
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Cited by 216 (1 self)
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This paper presents a couple of extensions to a basic Markov Model tagger (called TreeTagger) which improve its accuracy when trained on small corpora. The basic tagger was originally developed for English [Schmid, 1994]. The extensions together reduced error rates on a German test corpus by more than a third.
