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Semantic Relatedness Based Query Translation Disambiguation Approach for Cross-Language Web Search
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Most of query translation disambiguation approaches rely on a parallel corpus to resolve the translation ambiguity. This is because parallel corpus are good resources for resolving translation ambiguity. However, they have limitations in translating with a low convergence, specific domain, handling multi-word expressions, and non-availability in all pairs of languages. This work proposes a query translation disambiguation approach, which is based on measuring the semantic relatedness between the terms of the target query. It uses the semantic relatedness to select the suitable translation candidate of query terms and it doesn’t rely on parallel corpus. BabelNet lexical resource is used for extracting translating candidates of words and multi-word expressions. Moreover, splitting of complex compounds, and a back-translation is applied to enhance the final translation. The proposed approach is evaluated using the standard CLEF 2014 dataset collections. The results of the proposed approach are better than the previous corpus-based approaches for the Cross-Language Web Search (CLWS).
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We describe in this paper a hybrid ap-proach to build automatically bilingual lexicons of Multiword Expressions (MWEs) from parallel corpora. We more specifically investigate the impact of us-ing a domain-specific bilingual lexicon of MWEs on domain adaptation of an Example-Based Machine Translation (EBMT) system. We conducted experi-ments on the English-French language pair and two kinds of texts: in-domain texts from Europarl (European Parliament proceedings) and out-of-domain texts from Emea (European Medicines Agency documents) and Ecb (European Central Bank corpus). The obtained results indi-cate that integrating domain-specific bi-lingual lexicons of MWEs improves translation quality of the EBMT system when texts to translate are related to the specific domain and induces a relatively slight deterioration of translation quality when translating general-purpose texts.
Cross-Language Information Retrieval (CLIR)
is a most demanding research area of Information
Retrieval (IR) which deals with retrieval of documents
different from query language. In CLIR, translation is an
important activity for retrieving relevant results. Its goal
is to translate query or document from one language into
another language. The correct translation of the query is
an essential task of CLIR because incorrect translation
may affect the relevancy of retrieved results.
The purpose of this paper is to compute the accuracy of
query translation using the back translation for a HindiEnglish
CLIR system. For experimental analysis, we used
FIRE- 2011 dataset to select Hindi queries. Our analysis
shows that back translation can be effective in improving
the accuracy of query translation of the three translators
used for analysis (i.e. Google, Microsoft and Babylon).
Google is found best for the purpose.
Approaches of query translation in Cross-Language Information Retrieval (CLIR) have frequently used dictionaries which suffer from translation ambiguity. Besides, a word-by-word query translation is not sufficient. In this paper, we propose, evaluate and compare a new possibilistic approach for query translation in order to improve the previous dictionary-based ones. This approach uses a probability-to-possibility transformation as a mean to introduce further tolerance in query translation process. Firstly, we identify noun phrases (NPs) in the source query and translate them as units using translation patterns and a language model. Secondly, source query terms which are not included in any selected NPs are translated word-by-word using our new possibilistic approach of single word translation. Indeed, we take into account all query words and their translations when we choose the suitable translation of a given word. We start from the idea that the correct suitable translations of query terms have a tendency to co-occur in the target language documents unlike unsuitable ones. Finally, to increase the coverage of the bilingual dictionary, additional words and their translations are automatically generated from a parallel bilingual corpus. We tested our approach using the French-English parallel text corpus Europarl and the CLEF-2003 French-English CLIR test collection. The reported experiments showed the performance of the probability-to-possibility transformation-based approach compared to the probabilistic one and to some state-of-the-art CLIR tools.
This paper presents a supervised prepositional ambiguity resolution method for machine translation models in which the target language is Tamil and source language is English. We restrict our transfer ambiguity resolution problem with few prepositions only. This resolution method is based on supervised models which exploit collocation occurrences and linguistic information as features. This attempt will rectify the challenges in handling prepositions in English to Tamil automatic translation system. The preliminary results obtained from the evaluation shows that the proposed method is suitable for preposition resolution problem.
We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.
We propose, assess and compare in this paper a new discriminative possibilistic query translation (QT) disambiguation approach using both a bilingual dictionary and a parallel text corpus in order to overcome some drawbacks of the dictionary-based techniques. In this approach, the translation relevance of a given source query term is modeled by two measures: the possible relevance allows rejecting irrelevant translations, whereas the necessary relevance makes it possible to reinforce the translations not eliminated by the possibility. We experiment this new approach using the French-English parallel text corpus Europarl and the CLEF-2003 French-English CLIR test collection. Our experiments highlighted the performance of our new discriminative possibilistic approach compared to both the probabilistic and the probability-to-possibility transformation-based approaches, especially for short queries and using different assessment metrics.
We show in this paper how Semantic Query Disambiguation (SQD) combined with Semantic Query Expansion (SQE) can improve the effectiveness of intelligent information retrieval. Firstly, we propose and assess a possibilistic-based approach mixing SQD and SQE. This approach is based on corpus analysis using co-occurrence graphs modeled by possibilistic networks. Indeed, our model for relevance judgment uses possibility theory to take advantage of a double measure (possibility and necessity). Secondly, we propose and evaluate a probabilistic circuit-based approach combining SQD and SQE in an intelligent information retrieval context. In this approach, both SQD and SQE tasks are based on a graph data model, in which circuits between its nodes (words) represent the probabilistic scores for their semantic proximities. In order to compare the performance of these two approaches, we perform our experiments using the standard ROMANSEVAL test collection for the SQD task and the CLEF-2003 benchmark for the SQE process in French monolingual information retrieval evaluation. The results show the impact of SQD on SQE based on the recall/precision standard metrics for both the possibilistic and the probabilistic circuit-based approaches. Besides, the results of the possibilistic approach outperform the probabilistic ones, since it takes into account of imprecision cases.
Cross-language information retrieval (CLIR) deals with retrieving relevant documents in one language using queries expressed in another language. As CLIR tools rely on translation techniques, they are challenged by the properties of highly derivational and flexional languages like Arabic. Much work has been done on CLIR for different languages including Arabic. In this article, we introduce the reader to the motivations for solving some problems related to Arabic CLIR approaches. The evaluation of these approaches is discussed starting from the 2001 and 2002 TREC Arabic CLIR tracks, which aim to objectively evaluate CLIR systems. We also study many other research works to highlight the unresolved problems or those that require further investigation. These works are discussed in the light of a deep study of the specificities and the tasks of Arabic information retrieval (IR). Particular attention is given to translation techniques and CLIR resources, which are key issues challenging Arabic CLIR. To push research in this field, we discuss how a new standard collection can improve Arabic IR and CLIR tracks.
