The Experts below are selected from a list of 4947 Experts worldwide ranked by ideXlab platform
Mark Mclauchlan - One of the best experts on this subject based on the ideXlab platform.
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thesauruses for Prepositional Phrase attachment
Conference on Computational Natural Language Learning, 2004Co-Authors: Mark MclauchlanAbstract:Probabilistic models have been effective in resolving Prepositional Phrase attachment ambiguity, but sparse data remains a significant problem. We propose a solution based on similarity-based smoothing, where the probability of new PPs is estimated with information from similar examples generated using a thesaurus. Three thesauruses are compared on this task: two existing generic thesauruses and a new specialist PP thesaurus tailored for this problem. We also compare three smoothing techniques for Prepositional Phrases. We find that the similarity scores provided by the thesaurus tend to weight distant neighbours too highly, and describe a better score based on the rank of a word in the list of similar words. Our smoothing methods are applied to an existing PP attachment model and we obtain significant improvements over the baseline.
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CoNLL - Thesauruses for Prepositional Phrase Attachment.
2004Co-Authors: Mark MclauchlanAbstract:Probabilistic models have been effective in resolving Prepositional Phrase attachment ambiguity, but sparse data remains a significant problem. We propose a solution based on similarity-based smoothing, where the probability of new PPs is estimated with information from similar examples generated using a thesaurus. Three thesauruses are compared on this task: two existing generic thesauruses and a new specialist PP thesaurus tailored for this problem. We also compare three smoothing techniques for Prepositional Phrases. We find that the similarity scores provided by the thesaurus tend to weight distant neighbours too highly, and describe a better score based on the rank of a word in the list of similar words. Our smoothing methods are applied to an existing PP attachment model and we obtain significant improvements over the baseline.
Alexander Gelbukh - One of the best experts on this subject based on the ideXlab platform.
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Prepositional Phrase attachment disambiguation
2018Co-Authors: Alexander Gelbukh, Hiram CalvoAbstract:The problem of disambiguating PP attachments consists of determining if a PP is part of a noun Phrase (as in He sees the room with books) or a verb Phrase (as in He fills the room with books).
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web based model for disambiguation of Prepositional Phrase usage
Mexican International Conference on Artificial Intelligence, 2007Co-Authors: Sofia N Galiciaharo, Alexander GelbukhAbstract:We explore some Web-based methods to differentiate strings of words corresponding to Spanish Prepositional Phrases that can perform either as a regular Prepositional Phrase or as idiomatic Prepositional Phrase. The type of these Spanish Prepositional Phrases is preposition-nominal Phrase-preposition (P-NP-P), for example: por medio de 'by means of', a fin de 'in order to', con respecto a 'with respect to'. We propose an unsupervised method that verifies linguistics properties of idiomatic Prepositional Phrases. Results are presented with the method applied to newspaper sentences.
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acquiring selectional preferences from untagged text for Prepositional Phrase attachment disambiguation
Applications of Natural Language to Data Bases, 2004Co-Authors: Hiram Calvo, Alexander GelbukhAbstract:Extracting information automatically from texts for database representation requires previously well-grouped Phrases so that entities can be separated adequately. This problem is known as Prepositional Phrase (PP) attachment disambiguation. Current PP attachment disambiguation systems require an annotated treebank or they use an Internet connection to achieve a precision of more than 90. Unfortunately, these resources are not always available. In addition, using the same techniques that use the Web as corpus may not achieve the same results when using local corpora. In this paper, we present an unsupervised method for generalizing local corpora information by means of semantic classification of nouns based on the top 25 unique beginner concepts of WordNet. Then we propose a method for using this information for PP attachment disambiguation.
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improving Prepositional Phrase attachment disambiguation using the web as corpus
Iberoamerican Congress on Pattern Recognition, 2003Co-Authors: Hiram Calvo, Alexander GelbukhAbstract:The problem of Prepositional Phrase (PP) attachment disambiguation consists in determining if a PP is part of a noun Phrase, as in He sees the room with books, or an argument of a verb, as in He fills the room with books. Volk has proposed two variants of a method that queries an Internet search engine to find the most probable attachment variant. In this paper we apply the latest variant of Volk’s method to Spanish with several differences that allow us to attain a better performance close to that of statistical methods using treebanks.
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MICAI - Web-based model for disambiguation of Prepositional Phrase usage
MICAI 2007: Advances in Artificial Intelligence, 1Co-Authors: Sofía N. Galicia-haro, Alexander GelbukhAbstract:We explore some Web-based methods to differentiate strings of words corresponding to Spanish Prepositional Phrases that can perform either as a regular Prepositional Phrase or as idiomatic Prepositional Phrase. The type of these Spanish Prepositional Phrases is preposition-nominal Phrase-preposition (P-NP-P), for example: por medio de 'by means of', a fin de 'in order to', con respecto a 'with respect to'. We propose an unsupervised method that verifies linguistics properties of idiomatic Prepositional Phrases. Results are presented with the method applied to newspaper sentences.
Christian R. Huyck - One of the best experts on this subject based on the ideXlab platform.
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A neurocomputational approach to Prepositional Phrase attachment ambiguity resolution
Neural Computation, 2012Co-Authors: Kailash Nadh, Christian R. HuyckAbstract:A neurocomputational model based on emergent massively overlapping neural cell assemblies (CAs) for resolving Prepositional Phrase (PP) attachment ambiguity is described. PP attachment ambiguity is a well-studied task in natural language processing and is a case where semantics is used to determine the syntactic structure. A large network of biologically plausible fatiguing leaky integrate-and-fire neurons is trained with semantic hierarchies (obtained from WordNet) on sentences with PP attachment ambiguity extracted from the Penn Treebank corpus. During training, overlapping CAs representing semantic similarities between the component words of the ambiguous sentences emerge and then act as categorizers for novel input. The resulting average resolution accuracy of 84.56% is on par with known machine learning algorithms.
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Prepositional Phrase attachment ambiguity resolution using semantic hierarchies
2009Co-Authors: Kailash Nadh, Christian R. HuyckAbstract:This paper describes a system that resolves Prepositional Phrase attachment ambiguity in English sentence process- ing. This attachment problem is ubiquitous in English text, and is widely known as a place where semantics determines syntactic form. The decision is made based on a four-tuple composed of the head verb of the verb Phrase, the head noun of the noun Phrase, and the preposition and head noun in the Prepositional Phrase. A corpus with known results, the Penn Treebank, is used for training and testing purposes. During training, known results are used to build a lattice of hierarchical categories taken from WordNet. These lattices are then compared to the novel lattices derived from the test four-tuples. The results of the system are 90.53% correct attachment decisions.
Adwait Ratnaparkhi - One of the best experts on this subject based on the ideXlab platform.
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Statistical Models for Unsupervised Prepositional Phrase Attachment
arXiv: Computation and Language, 1998Co-Authors: Adwait RatnaparkhiAbstract:We present several unsupervised statistical models for the Prepositional Phrase attachment task that approach the accuracy of the best supervised methods for this task. Our unsupervised approach uses a heuristic based on attachment proximity and trains from raw text that is annotated with only part-of-speech tags and morphological base forms, as opposed to attachment information. It is therefore less resource-intensive and more portable than previous corpus-based algorithms proposed for this task. We present results for Prepositional Phrase attachment in both English and Spanish.
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COLING-ACL - Statistical Models for Unsupervised Prepositional Phrase Attachment
Proceedings of the 36th annual meeting on Association for Computational Linguistics -, 1998Co-Authors: Adwait RatnaparkhiAbstract:We present several unsupervised statistical models for the Prepositional Phrase attachment task that approach the accuracy of the best supervised methods for this task. Our unsupervised approach uses a heuristic based on attachment proximity and trains from raw text that is annotated with only part-of-speech tags and morphological base forms, as opposed to attachment information. It is therefore less resource-intensive and more portable than previous corpus-based algorithm proposed for this task. We present results for Prepositional Phrase attachment in both English and Spanish.
Dekang Lin - One of the best experts on this subject based on the ideXlab platform.
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an unsupervised approach to Prepositional Phrase attachment using contextually similar words
Meeting of the Association for Computational Linguistics, 2000Co-Authors: Patrick Pantel, Dekang LinAbstract:Prepositional Phrase attachment is a common source of ambiguity in natural language processing. We present an unsupervised corpus-based approach to Prepositional Phrase attachment that achieves similar performance to supervised methods. Unlike previous unsupervised approaches in which training data is obtained by heuristic extraction of unambiguous examples from a corpus, we use an iterative process to extract training data from an automatically parsed corpus. Attachment decisions are made using a linear combination of features and low frequency events are approximated using contextually similar words.
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ACL - An unsupervised approach to Prepositional Phrase attachment using contextually similar words
Proceedings of the 38th Annual Meeting on Association for Computational Linguistics - ACL '00, 2000Co-Authors: Patrick Pantel, Dekang LinAbstract:Prepositional Phrase attachment is a common source of ambiguity in natural language processing. We present an unsupervised corpus-based approach to Prepositional Phrase attachment that achieves similar performance to supervised methods. Unlike previous unsupervised approaches in which training data is obtained by heuristic extraction of unambiguous examples from a corpus, we use an iterative process to extract training data from an automatically parsed corpus. Attachment decisions are made using a linear combination of features and low frequency events are approximated using contextually similar words.
