Results 1 - 10
of
99
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 ..."
Abstract
-
Cited by 191 (16 self)
- Add to MetaCart
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.
11,001 new features for statistical machine translation
- In North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL-HLT
, 2009
"... We use the Margin Infused Relaxed Algorithm of Crammer et al. to add a large number of new features to two machine translation systems: the Hiero hierarchical phrasebased translation system and our syntax-based translation system. On a large-scale Chinese-English translation task, we obtain statisti ..."
Abstract
-
Cited by 117 (2 self)
- Add to MetaCart
We use the Margin Infused Relaxed Algorithm of Crammer et al. to add a large number of new features to two machine translation systems: the Hiero hierarchical phrasebased translation system and our syntax-based translation system. On a large-scale Chinese-English translation task, we obtain statistically significant improvements of +1.5 Bleu and +1.1 Bleu, respectively. We analyze the impact of the new features and the performance of the learning algorithm. 1
A survey of statistical machine translation
, 2007
"... Statistical machine translation (SMT) treats the translation of natural language as a machine learning problem. By examining many samples of human-produced translation, SMT algorithms automatically learn how to translate. SMT has made tremendous strides in less than two decades, and many popular tec ..."
Abstract
-
Cited by 93 (6 self)
- Add to MetaCart
Statistical machine translation (SMT) treats the translation of natural language as a machine learning problem. By examining many samples of human-produced translation, SMT algorithms automatically learn how to translate. SMT has made tremendous strides in less than two decades, and many popular techniques have only emerged within the last few years. This survey presents a tutorial overview of state-of-the-art SMT at the beginning of 2007. We begin with the context of the current research, and then move to a formal problem description and an overview of the four main subproblems: translational equivalence modeling, mathematical modeling, parameter estimation, and decoding. Along the way, we present a taxonomy of some different approaches within these areas. We conclude with an overview of evaluation and notes on future directions.
SemEval-2010 Task 3: Cross-lingual Word Sense Disambiguation
"... We propose a multilingual unsupervised Word Sense Disambiguation (WSD) task for a sample of English nouns. Instead of providing manually sensetagged examples for each sense of a polysemous noun, our sense inventory is built up on the basis of the Europarl parallel corpus. The multilingual setup invo ..."
Abstract
-
Cited by 41 (5 self)
- Add to MetaCart
(Show Context)
We propose a multilingual unsupervised Word Sense Disambiguation (WSD) task for a sample of English nouns. Instead of providing manually sensetagged examples for each sense of a polysemous noun, our sense inventory is built up on the basis of the Europarl parallel corpus. The multilingual setup involves the translations of a given English polysemous noun in five supported languages, viz. Dutch, French, German, Spanish and Italian. The task targets the following goals: (a) the manual creation of a multilingual sense inventory for a lexical sample of English nouns and (b) the evaluation of systems on their ability to disambiguate new occurrences of the selected polysemous nouns. For the creation of the hand-tagged gold standard, all translations of a given polysemous English noun are retrieved in the five languages and clustered by meaning. Systems can participate in 5 bilingual evaluation subtasks (English- Dutch, English- German, etc.) and in a multilingual subtask covering all language pairs. As WSD from cross-lingual evidence is gaining popularity, we believe it is important to create a multilingual gold standard and run cross-lingual WSD benchmark tests. 1
Extending Statistical Machine Translation with Discriminative and Trigger-Based Lexicon Models
"... In this work, we propose two extensions of standard word lexicons in statistical machine translation: A discriminative word lexicon that uses sentence-level source information to predict the target words and a trigger-based lexicon model that extends IBM model 1 with a second trigger, allowing for a ..."
Abstract
-
Cited by 41 (19 self)
- Add to MetaCart
(Show Context)
In this work, we propose two extensions of standard word lexicons in statistical machine translation: A discriminative word lexicon that uses sentence-level source information to predict the target words and a trigger-based lexicon model that extends IBM model 1 with a second trigger, allowing for a more fine-grained lexical choice of target words. The models capture dependencies that go beyond the scope of conventional SMT models such as phraseand language models. We show that the models improve translation quality by 1% in BLEU over a competitive baseline on a large-scale task. 1
The English lexical substitution task
, 2009
"... Since the inception of the SENSEVAL series there has been a great deal of debate in the word sense disambiguation (WSD) community on what the right sense distinctions are for evaluation, with the consensus of opinion being that the distinctions should be relevant to the intended application. A solut ..."
Abstract
-
Cited by 25 (7 self)
- Add to MetaCart
Since the inception of the SENSEVAL series there has been a great deal of debate in the word sense disambiguation (WSD) community on what the right sense distinctions are for evaluation, with the consensus of opinion being that the distinctions should be relevant to the intended application. A solution to the above issue is lexical substitution, i.e. the replacement of a target word in context with a suitable alternative substitute. In this paper, we describe the English lexical substitution task and report an exhaustive evaluation of the systems participating in the task organized at SemEval-2007. The aim of this task is to provide an evaluation where the sense inventory is not predefined and where performance on the task would bode well for applications. The task not only reflects WSD capabilities, but also can be used to compare lexical resources, whether man-made or automatically created, and has the potential to benefit several natural-language applications.
Triplet lexicon models for statistical machine translation
- In EMNLP ’08: Proceedings of the Conference on Empirical Methods in Natural Language Processing
, 2008
"... This paper describes a lexical trigger model for statistical machine translation. We present various methods using triplets incorporating long-distance dependencies that can go beyond the local context of phrases or n-gram based language models. We evaluate the presented methods on two translation t ..."
Abstract
-
Cited by 24 (9 self)
- Add to MetaCart
(Show Context)
This paper describes a lexical trigger model for statistical machine translation. We present various methods using triplets incorporating long-distance dependencies that can go beyond the local context of phrases or n-gram based language models. We evaluate the presented methods on two translation tasks in a reranking framework and compare it to the related IBM model 1. We show slightly improved translation quality in terms of BLEU and TER and address various constraints to speed up the training based on Expectation-Maximization and to lower the overall number of triplets without loss in translation performance. 1
Improving Statistical Machine Translation using Lexicalized Rule Selection
- In Proc. Coling
, 2008
"... This paper proposes a novel lexicalized approach for rule selection for syntax-based statistical machine translation (SMT). We build maximum entropy (MaxEnt) models which combine rich context information for selecting translation rules during decoding. We successfully integrate the MaxEnt-based rule ..."
Abstract
-
Cited by 24 (9 self)
- Add to MetaCart
This paper proposes a novel lexicalized approach for rule selection for syntax-based statistical machine translation (SMT). We build maximum entropy (MaxEnt) models which combine rich context information for selecting translation rules during decoding. We successfully integrate the MaxEnt-based rule selection models into the state-of-the-art syntax-based SMT model. Experiments show that our lexicalized approach for rule selection achieves statistically significant improvements over the state-of-the-art SMT system. 1
Graded Word Sense Assignment
"... Word sense disambiguation is typically phrased as the task of labeling a word in context with the best-fitting sense from a sense inventory such as WordNet. While questions have often been raised over the choice of sense inventory, computational linguists have readily accepted the bestfitting sense ..."
Abstract
-
Cited by 19 (1 self)
- Add to MetaCart
(Show Context)
Word sense disambiguation is typically phrased as the task of labeling a word in context with the best-fitting sense from a sense inventory such as WordNet. While questions have often been raised over the choice of sense inventory, computational linguists have readily accepted the bestfitting sense methodology despite the fact that the case for discrete sense boundaries is widely disputed by lexical semantics researchers. This paper studies graded word sense assignment, based on a recent dataset of graded word sense annotation. 1
Rich Source-Side Context for Statistical Machine Translation
"... We explore the augmentation of statistical machine translation models with features of the context of each phrase to be translated. This work extends several existing threads of research in statistical MT, including the use of context in example-based machine translation (Carl and Way, 2003) and the ..."
Abstract
-
Cited by 18 (3 self)
- Add to MetaCart
(Show Context)
We explore the augmentation of statistical machine translation models with features of the context of each phrase to be translated. This work extends several existing threads of research in statistical MT, including the use of context in example-based machine translation (Carl and Way, 2003) and the incorporation of word sense disambiguation into a translation model (Chan et al., 2007). The context features we consider use surrounding words and part-of-speech tags, local syntactic structure, and other properties of the source language sentence to help predict each phrase’s translation. Our approach requires very little computation beyond the standard phrase extraction algorithm and scales well to large data scenarios. We report significant improvements in automatic evaluation scores for Chineseto-English and English-to-German translation, and also describe our entry in the WMT-08 shared task based on this approach. 1
