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263
TextTiling: Segmenting text into multi-paragraph subtopic passages
- Computational Linguistics
, 1997
"... TextTiling is a technique for subdividing texts into multi-paragraph units that represent passages, or subtopics. The discourse cues for identifying major subtopic shifts are patterns of lexical co-occurrence and distribution. The algorithm is fully implemented and is shown to produce segmentation t ..."
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Cited by 458 (2 self)
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TextTiling is a technique for subdividing texts into multi-paragraph units that represent passages, or subtopics. The discourse cues for identifying major subtopic shifts are patterns of lexical co-occurrence and distribution. The algorithm is fully implemented and is shown to produce segmentation that corresponds well to human judgments of the subtopic boundaries of 12 texts. Multi-paragraph subtopic segmentation should be useful for many text analysis tasks, including information retrieval and summarization. 1.
SELECTION AND INFORMATION: A CLASS-BASED APPROACH TO LEXICAL RELATIONSHIPS
, 1993
"... Selectional constraints are limitations on the applicability of predicates to arguments. For example, the statement “The number two is blue” may be syntactically well formed, but at some level it is anomalous — BLUE is not a predicate that can be applied to numbers. According to the influential theo ..."
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Cited by 272 (9 self)
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Selectional constraints are limitations on the applicability of predicates to arguments. For example, the statement “The number two is blue” may be syntactically well formed, but at some level it is anomalous — BLUE is not a predicate that can be applied to numbers. According to the influential theory of (Katz and Fodor, 1964), a predicate associates a set of defining features with each argument, expressed within a restricted semantic vocabulary. Despite the persistence of this theory, however, there is widespread agreement about its empirical shortcomings (McCawley, 1968; Fodor, 1977). As an alternative, some critics of the Katz-Fodor theory (e.g. (Johnson-Laird, 1983)) have abandoned the treatment of selectional constraints as semantic, instead treating them as indistinguishable from inferences made on the basis of factual knowledge. This provides a better match for the empirical phenomena, but it opens up a different problem: if selectional constraints are the same as inferences in general, then accounting for them will require a much more complete understanding of knowledge representation and inference than we have at present. The problem, then, is this: how can a theory of selectional constraints be elaborated without first having either an empirically adequate theory of defining features or a comprehensive theory of inference? In this dissertation, I suggest that an answer to this question lies in the representation of conceptual
Introduction to the special issue on word sense disambiguation
- Computational Linguistics J
, 1998
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Large-scale named entity disambiguation based on Wikipedia data
- In Proc. 2007 Joint Conference on EMNLP and CNLL
, 2007
"... This paper presents a large-scale system for the recognition and semantic disambiguation of named entities based on information extracted from a large encyclopedic collection and Web search results. It describes in detail the disambiguation paradigm employed and the information extraction process fr ..."
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Cited by 238 (3 self)
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This paper presents a large-scale system for the recognition and semantic disambiguation of named entities based on information extracted from a large encyclopedic collection and Web search results. It describes in detail the disambiguation paradigm employed and the information extraction process from Wikipedia. Through a process of maximizing the agreement between the contextual information extracted from Wikipedia and the context of a document, as well as the agreement among the category tags associated with the candidate entities, the implemented system shows high disambiguation accuracy on both news stories and Wikipedia articles. 1 Introduction and Related Work
Using Corpus Statistics and WordNet Relations for Sense Identification
, 1998
"... Introduction An impressive array of statistical methods have been developed for word sense identification. They range from dictionary-based approaches that rely on definitions (Vronis and Ide 1990; Wilks et al. 1993) to corpus-based approaches that use only word cooccurrence frequencies extracted f ..."
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Cited by 201 (0 self)
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Introduction An impressive array of statistical methods have been developed for word sense identification. They range from dictionary-based approaches that rely on definitions (Vronis and Ide 1990; Wilks et al. 1993) to corpus-based approaches that use only word cooccurrence frequencies extracted from large textual corpora (Schfitze 1995; Dagan and Itai 1994). We have drawn on these two traditions, using corpus-based co-occurrence and the lexical knowledge base that is embodied in the WordNet lexicon. The two traditions complement each other. Corpus-based approaches have the advantage of being generally applicable to new texts, domains, and corpora without needing costly and perhaps error-prone parsing or semantic analysis. They require only training corpora in which the sense distinctions have been marked, but therein lies their weakness. Obtaining training materials for statistical methods is costly and timeconsuming --it is a "knowledge acquisition bottleneck" (Gale, Church, and Y
Word sense disambiguation: a survey
- ACM COMPUTING SURVEYS
, 2009
"... Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. We introduce the reader to the ..."
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Cited by 191 (16 self)
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Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. We introduce the reader to the motivations for solving the ambiguity of words and provide a description of the task. We overview supervised, unsupervised, and knowledge-based approaches. The assessment of WSD systems is discussed in the context of the Senseval/Semeval campaigns, aiming at the objective evaluation of systems participating in several different disambiguation tasks. Finally, applications, open problems, and future directions are discussed.
One sense per collocation
- In Proceedings of the ARPA Human Language Technology Workshop
, 1993
"... Previous work [Gale, Church and Yarowsky, 1992] showed that with high probability a polysemous word has one sense per discourse. In this paper we show that for certain definitions of collocation, a polysemous word exhibits essentially only one sense per collocation. We test this empirical hypothesis ..."
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Cited by 186 (6 self)
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Previous work [Gale, Church and Yarowsky, 1992] showed that with high probability a polysemous word has one sense per discourse. In this paper we show that for certain definitions of collocation, a polysemous word exhibits essentially only one sense per collocation. We test this empirical hypothesis for several definitions of sense and collocation, and discover that it holds with 90-99 % accuracy for binary ambiguities. We utilize this property in a disambiguation algorithm that achieves precision of 92 % using combined models of very local context. 1.
Word sense disambiguation: The state of the art
- Computational Linguistics
, 1998
"... The automatic disambiguation of word senses has been an interest and concern since the earliest days of computer treatment of language in the 1950's. Sense disambiguation is an “intermediate task ” (Wilks and Stevenson, 1996) which is not an end in itself, but rather is necessary at one level o ..."
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Cited by 152 (3 self)
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The automatic disambiguation of word senses has been an interest and concern since the earliest days of computer treatment of language in the 1950's. Sense disambiguation is an “intermediate task ” (Wilks and Stevenson, 1996) which is not an end in itself, but rather is necessary at one level or another to accomplish most natural language processing tasks. It is
Distinguishing Systems and Distinguishing Senses: New Evaluation Methods for Word Sense Disambiguation
, 1998
"... Resnik and Yarowsky (1997) made a set of observations about the state of the art in automatic word sense disambiguation and, motivated by those observations, offered several specific proposals regarding improved evaluation criteria, common training and testing resources, and the definition of sense ..."
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Cited by 125 (8 self)
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Resnik and Yarowsky (1997) made a set of observations about the state of the art in automatic word sense disambiguation and, motivated by those observations, offered several specific proposals regarding improved evaluation criteria, common training and testing resources, and the definition of sense inventories. Subsequent discussion of those proposals resulted in senseval, the first evaluation exercise for word sense disambiguation (Kilgarriff and Palmer forthcoming). This article is a revised and extended version of our 1997 workshop paper, reviewing its observations and proposals and discussing them in light of the senseval exercise. It also includes a new in-depth empirical study of translingually-based sense inventories and distance measures, using statistics collected from native-speaker annotations of 222 polysemous contexts across 12 languages. These data show that monolingual sense distinctions at most levels of granularity can be effectively captured by translations into some ...
Language Independent Named Entity Recognition Combining Morphological and Contextual Evidence
, 1999
"... Identifying and classifying personal, geographic, institutional or other names in a text is an important task for numerous applications. This paper describes and evaluates a language-independent bootstrapping algorithm based on iterative learning and re-estimation of contextual and morphological pat ..."
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Cited by 123 (5 self)
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Identifying and classifying personal, geographic, institutional or other names in a text is an important task for numerous applications. This paper describes and evaluates a language-independent bootstrapping algorithm based on iterative learning and re-estimation of contextual and morphological patterns captured in hierarchicaily smoothed trie models. The algorithm learns from unannotated text and achieves competitive performance when trained on a very short labelled name list with no other required language-specific information, tokenizers or tools.
