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213
Cross-language information filtering: Word sense disambiguation vs. distributional models
- In AI*IA 2011, volume 6934 of LNCS (2011
"... Abstract. The exponential growth of the Web is the most influential factor that contributes to the increasing importance of text retrieval and filtering systems. Anyway, since information exists in many languages, users could also consider as relevant documents written in different lan-guages from t ..."
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Cited by 2 (1 self)
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in a language-independent way? In this paper, we compared two content-based techniques able to pro-vide users with cross-language recommendations: the first one relies on a knowledge-based word sense disambiguation technique that uses Multi-WordNet as sense inventory, while the latter is based on a
Comparing Word Sense Disambiguation and Distributional Models for Cross-Language Information Filtering
"... Abstract. In this paper we deal with the problem of providing users with cross-language recommendations by comparing two different contentbased techniques: the first one relies on a knowledge-based word sense disambiguation algorithm that uses MultiWordNet as sense inventory, while the latter is bas ..."
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Abstract. In this paper we deal with the problem of providing users with cross-language recommendations by comparing two different contentbased techniques: the first one relies on a knowledge-based word sense disambiguation algorithm that uses MultiWordNet as sense inventory, while the latter
Knowledge lean word-sense disambiguation
- In Proceedings of the Fifteenth National Conference on Artificial Intelligence
, 1998
"... We present a corpus{based approach to word{sense disambiguation that only requires information that can be automatically extracted from untagged text. We use unsupervised techniques to estimate the parameters of a model describing the conditional distribution of the sense group given the known conte ..."
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Cited by 37 (6 self)
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We present a corpus{based approach to word{sense disambiguation that only requires information that can be automatically extracted from untagged text. We use unsupervised techniques to estimate the parameters of a model describing the conditional distribution of the sense group given the known
TQDL: Integrated Models for Cross-Language Document Retrieval
"... This paper proposed an integrated approach for Cross-Language Information Retrieval (CLIR), which integrated with four statistical models: Translation model, Query generation model, Document retrieval model and Length Filter model. Given a certain document in the source language, it will be translat ..."
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Cited by 1 (1 self)
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This paper proposed an integrated approach for Cross-Language Information Retrieval (CLIR), which integrated with four statistical models: Translation model, Query generation model, Document retrieval model and Length Filter model. Given a certain document in the source language
Word Sense Disambiguation Improves Information Retrieval
"... Previous research has conflicting conclusions on whether word sense disambiguation (WSD) systems can improve information retrieval (IR) performance. In this paper, we propose a method to estimate sense distributions for short queries. Together with the senses predicted for words in documents, we pro ..."
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Cited by 2 (0 self)
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Previous research has conflicting conclusions on whether word sense disambiguation (WSD) systems can improve information retrieval (IR) performance. In this paper, we propose a method to estimate sense distributions for short queries. Together with the senses predicted for words in documents, we
Combining Relational and Distributional Knowledge for Word Sense Disambiguation
"... We present a new approach to word sense disambiguation derived from recent ideas in distributional semantics. The input to the algorithm is a large unlabeled cor-pus and a graph describing how senses are related; no sense-annotated corpus is needed. The fundamental idea is to em-bed meaning represen ..."
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Cited by 1 (1 self)
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We present a new approach to word sense disambiguation derived from recent ideas in distributional semantics. The input to the algorithm is a large unlabeled cor-pus and a graph describing how senses are related; no sense-annotated corpus is needed. The fundamental idea is to em-bed meaning
A New Approach to Word Sense Disambiguation
- In Proceedings of the ARPA Workshop on Human Language Technology
, 1994
"... This paper presents and evaluates models created according to a schema that provides a description of the joint distribution of the values of sense tags and contextual features that is potentially applicable to a wide range of content words. The models are evaluated through a series of experiments, ..."
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Cited by 15 (5 self)
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the tag that has the highest estimated probability of having occurred in the given context. Designing a probabilistic classifier for word-sense disambiguation includes two main sub-tasks: specifying an appropriate model and estimating the parameters of that model. The former involves selecting informative
Unsupervised domain relevance estimation for word sense disambiguation
- In Proceedings of International Conference on Empirical Methods in Natural Language Processing (EMNLP’04
, 2004
"... This paper presents Domain Relevance Estimation (DRE), a fully unsupervised text categorization technique based on the statistical estimation of the relevance of a text with respect to a certain category. We use a pre-defined set of categories (we call them domains) which have been previously associ ..."
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Cited by 3 (0 self)
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that maximize the likelihood of the model on the empirical data. The correct identification of the domain of the text is a crucial point for Domain Driven Disambiguation, an unsupervised Word Sense Disambiguation (WSD) methodology that makes use of only domain information. Therefore, DRE has been exploited
Enriched Page Rank for Multilingual Word Sense Disambiguation
"... Abstract. Word Sense Disambiguation (WSD) is an hard challenge especially for language different from english. Porting supervised models, that are state-of-art for english, on different languages is too much expensive. So unsupervised or semi-supervised WSD models are much more applicable to differe ..."
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Abstract. Word Sense Disambiguation (WSD) is an hard challenge especially for language different from english. Porting supervised models, that are state-of-art for english, on different languages is too much expensive. So unsupervised or semi-supervised WSD models are much more applicable
Using EuroWordNet in a Concept-based Approach to Cross-Language Text Retrieval
- Applied Artificial Intelligence
, 1999
"... W e present an approach to cross ± language text retrieval based on the EuroWordNet (EWN) multilingual semantic database. EuroW ordNet is a multilingual, W ordNet ± like database with basic semantic relations between words for several European languages (English, Dutch, Spanish, Italian, German, Fre ..."
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Cited by 20 (2 self)
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to word sense disambiguation errors. In our monolingual experiments, the classical, vector space model for text retrieval is shown to give better results (up to 29 % better in our experiments) if W ordNet synsets are chosen as the indexing space, instead of word forms. T his result is obtained for a
Results 1 - 10
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213
