Relevant bibliographies by topics / Translation disambiguation

Academic literature on the topic 'Translation disambiguation'

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Published: 4 June 2021
Last updated: 25 January 2023

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Journal articles on the topic "Translation disambiguation"

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Zhang, Chun Xiang, Long Deng, Xue Yao Gao, and Li Li Guo. "Word Sense Disambiguation for Improving the Quality of Machine Translation." Advanced Materials Research 981 (July 2014): 153–56. http://dx.doi.org/10.4028/www.scientific.net/amr.981.153.

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Word sense disambiguation is key to many application problems in natural language processing. In this paper, a specific classifier of word sense disambiguation is introduced into machine translation system in order to improve the quality of the output translation. Firstly, translation of ambiguous word is deleted from machine translation of Chinese sentence. Secondly, ambiguous word is disambiguated and the classification labels are translations of ambiguous word. Thirdly, these two translations are combined. 50 Chinese sentences including ambiguous words are collected for test experiments. Experimental results show that the translation quality is improved after the proposed method is applied.
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Chingamtotattil, Rahul, and Rajamma Gopikakumar. "Neural machine translation for Sanskrit to Malayalam using morphology and evolutionary word sense disambiguation." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (October 7, 2022): 1709. http://dx.doi.org/10.11591/ijeecs.v28.i3.pp1709-1719.

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Neural machine translation (NMT) is a fast-evolving MT paradigm and showed good results, particularly in large training data circumstances, for several language pairs. In this paper, we have utilized Sanskrit to Malayalam language pair neural machines translation. The attention-based mechanism for the development of the machine translation system was particularly exploited. Word sense disambiguation (WSD) is a phenomenon for disambiguating the text to let the machine infer the proper definition of the particular word. Sequential deep learning approaches such as a recurrent neural network (RNN), a gated recurrent unit (GRU), a long short term memory (LSTM), and a bi-directional LSTM (BLSTM) were used to analyze the tagged data. By adding morphological elements and evolutionary word sense disambiguation, the suggested common character-word embedding-based NMT model gives a BLEU score of 38.58 which was higher than the others.
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Bajpai, Pratibha, Parul Verma, and Syed Q. Abbas. "Two Level Disambiguation Model for Query Translation." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (October 1, 2018): 3923. http://dx.doi.org/10.11591/ijece.v8i5.pp3923-3932.

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Selection of the most suitable translation among all translation candidates returned by bilingual dictionary has always been quiet challenging task for any cross language query translation. Researchers have frequently tried to use word co-occurrence statistics to determine the most probable translation for user query. Algorithms using such statistics have certain shortcomings, which are focused in this paper. We propose a novel method for ambiguity resolution, named ‘two level disambiguation model’. At first level disambiguation, the model properly weighs the importance of translation alternatives of query terms obtained from the dictionary. The importance factor measures the probability of a translation candidate of being selected as the final translation of a query term. This removes the problem of taking binary decision for translation candidates. At second level disambiguation, the model targets the user query as a single concept and deduces the translation of all query terms simultaneously, taking into account the weights of translation alternatives also. This is contrary to previous researches which select translation for each word in source language query independently. The experimental result with English-Hindi cross language information retrieval shows that the proposed two level disambiguation model achieved 79.53% and 83.50% of monolingual translation and 21.11% and 17.36% improvement compared to greedy disambiguation strategies in terms of MAP for short and long queries respectively.
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Boyarskaya, Elena. "Ambiguity matters in linguistics and translation." Slovo.ru: Baltic accent 10, no. 3 (2019): 81–93. http://dx.doi.org/10.5922/2225-5346-2019-3-6.

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Ambiguity implies that there are at least two distinct senses ascribed to one sign. It is in­herent to language and speech. In this article, I reflect on the types of ambiguity, its typology, production and effect and propose an algorithm for tackling ambiguity in translation. I posit that the choice of a translation strategy and the need for disambiguation in general depend on the type of ambiguity, its sources and character, i. e. whether ambiguity is intended or not. Intended ambiguity occurs when the speaker intentionally does not follow the logic of concep­tual clues (primes) and opts for a set of communicative strategies and linguistic means, which allow him/her to offer several possible interpretations of one event or even refer to several dif­ferent events. I explore a rarely analyzed event-referential ambiguity, which requires addi­tional conceptual information for disambiguation and, consequently, may pose a problem for translation. I argue that problems in disambiguation may occur for a variety of reasons: the translator and\or the recipient may have a wrong reference, have insufficient background knowledge to resolve the ambiguity or make wrong inferences since each recipient bears a different combination of cognitive, axiological, social, professional and gender attributes.
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Suo, Juan Juan, Bao Ying Yu, Yue Juan He, and Guang Ya Zang. "Study of Ambiguities of English-Chinese Machine Translation." Applied Mechanics and Materials 157-158 (February 2012): 472–75. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.472.

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Ambiguities is one of the biggest obstacles of English-Chinese machine translation. In order to make the disambiguation more easily and effectively, the reasons of ambiguities why the quality of machine translation is very low from the perspective of linguistics were analyzed. And then some measures of disambiguation were proposed. This study has significance for the development of English-Chinese machine translation.
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Mandici, Mădălina Elena. "Translation Solutions for Dealing With Ambiguity in Alice’s Adventures in Wonderland." Studia Universitatis Babeș-Bolyai Philologia 67, no. 4 (December 20, 2022): 417–36. http://dx.doi.org/10.24193/subbphilo.2022.4.21.

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"Translation solutions for dealing with ambiguity in Alice’s Adventures in Wonderland. This paper shows how different types of ambiguity embedded in the matrix of Lewis Carroll’s Alice’s Adventures in Wonderland (the 1993 edition) are dealt with in two prestigious Romanian translations – Frida Papadache’s Peripeţiile Alisei în Ţara Minunilor (1976) and Antoaneta Ralian’s Alice în Ţara Minunilor (2007) – as a tribute to the international appeal of Alice. My focal aim is to present a comparative analysis of the methods employed in translating Carroll’s equivocal lexical items, which make it increasingly difficult to match grammatical categories with function. This paper also aims at describing disambiguation techniques applied primarily in determining if the two translators managed to reinforce the original textual leeway at their disposal in the pure spirit of Carroll. My analysis relies heavily on Dirk Delabastita’s translation strategies as precautionary measures to cope with Carroll’s specialized type of literary discourse. The findings submitted by this paper are consistent with the idea that translating Carroll’s craft unavoidably entails a partial loss of meaning, brought about by the yawning gap between the intended message and interpretation, which can result in either overtranslation or undertranslation. The extensive use of double-entendre in the source-text cannot be recoded entirely in the target language, despite the translators’ excellent command of English. Keywords: Carrollian humor; ambiguity; translation solutions; disambiguation techniques; textual challenges "
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Jantsch, Simon, David Müller, Christel Baier, and Joachim Klein. "From LTL to unambiguous Büchi automata via disambiguation of alternating automata." Formal Methods in System Design 58, no. 1-2 (October 2021): 42–82. http://dx.doi.org/10.1007/s10703-021-00379-z.

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AbstractDue to the high complexity of translating linear temporal logic (LTL) to deterministic automata, several forms of “restricted” nondeterminism have been considered with the aim of maintaining some of the benefits of deterministic automata, while at the same time allowing more efficient translations from LTL. One of them is the notion of unambiguity. This paper proposes a new algorithm for the generation of unambiguous Büchi automata (UBA) from LTL formulas. Unlike other approaches it is based on a known translation from very weak alternating automata (VWAA) to NBA. A notion of unambiguity for alternating automata is introduced and it is shown that the VWAA-to-NBA translation preserves unambiguity. Checking unambiguity of VWAA is determined to be PSPACE-complete, both for the explicit and symbolic encodings of alternating automata. The core of the LTL-to-UBA translation is an iterative disambiguation procedure for VWAA. Several heuristics are introduced for different stages of the procedure. We report on an implementation of our approach in the tool and compare it to an existing LTL-to-UBA implementation in the tool set. Our experiments cover model checking of Markov chains, which is an important application of UBA.
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Li, Hang, and Cong Li. "Word Translation Disambiguation Using Bilingual Bootstrapping." Computational Linguistics 30, no. 1 (March 2004): 1–22. http://dx.doi.org/10.1162/089120104773633367.

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This article proposes a new method for word translation disambiguation, one that uses a machine-learning technique called bilingual bootstrapping. In learning to disambiguate words to be translated, bilingual bootstrapping makes use of a small amount of classified data and a large amount of unclassified data in both the source and the target languages. It repeatedly constructs classifiers in the two languages in parallel and boosts the performance of the classifiers by classifying unclassified data in the two languages and by exchanging information regarding classified data between the two languages. Experimental results indicate that word translation disambiguation based on bilingual bootstrapping consistently and significantly outperforms existing methods that are based on monolingual bootstrapping.
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Quantz, Joachim, and Birte Schmitz. "Knowledge-based disambiguation for machine translation." Minds and Machines 4, no. 1 (February 1994): 39–57. http://dx.doi.org/10.1007/bf00974203.

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Cheung, Percy, and Pascale Fung. "Translation Disambiguation in Mixed Language Queries." Machine Translation 18, no. 4 (December 2004): 251–73. http://dx.doi.org/10.1007/s10590-004-7692-5.

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Dissertations / Theses on the topic "Translation disambiguation"

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Carpuat, Marine Jacinthe. "Word sense disambiguation for statistical machine translation /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?CSED%202008%20CARPUA.

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Zhang, Ying, and ying yzhang@gmail com. "Improved Cross-language Information Retrieval via Disambiguation and Vocabulary Discovery." RMIT University. Computer Science and Information Technology, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090224.114940.

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Cross-lingual information retrieval (CLIR) allows people to find documents irrespective of the language used in the query or document. This thesis is concerned with the development of techniques to improve the effectiveness of Chinese-English CLIR. In Chinese-English CLIR, the accuracy of dictionary-based query translation is limited by two major factors: translation ambiguity and the presence of out-of-vocabulary (OOV) terms. We explore alternative methods for translation disambiguation, and demonstrate new techniques based on a Markov model and the use of web documents as a corpus to provide context for disambiguation. This simple disambiguation technique has proved to be extremely robust and successful. Queries that seek topical information typically contain OOV terms that may not be found in a translation dictionary, leading to inappropriate translations and consequent poor retrieval performance. Our novel OOV term translation method is based on the Chinese authorial practice of including unfamiliar English terms in both languages. It automatically extracts correct translations from the web and can be applied to both Chinese-English and English-Chinese CLIR. Our OOV translation technique does not rely on prior segmentation and is thus free from seg mentation error. It leads to a significant improvement in CLIR effectiveness and can also be used to improve Chinese segmentation accuracy. Good quality translation resources, especially bilingual dictionaries, are valuable resources for effective CLIR. We developed a system to facilitate construction of a large-scale translation lexicon of Chinese-English OOV terms using the web. Experimental results show that this method is reliable and of practical use in query translation. In addition, parallel corpora provide a rich source of translation information. We have also developed a system that uses multiple features to identify parallel texts via a k-nearest-neighbour classifier, to automatically collect high quality parallel Chinese-English corpora from the web. These two automatic web mining systems are highly reliable and easy to deploy. In this research, we provided new ways to acquire linguistic resources using multilingual content on the web. These linguistic resources not only improve the efficiency and effectiveness of Chinese-English cross-language web retrieval; but also have wider applications than CLIR.
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Martelli, Federico. "Word Sense Disambiguation in Tongue2Tongue, a Pioneering Computer-aided Translation Tool." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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Over the last years, technology has achieved a dominant position in a wide range of fields, causing a profound paradigm shift in our lives. In the area of translation, technology has brought to light useful applications such as machine translation and computer-aided translation. While the former is aimed at automatically translating texts, the latter is intended to support and facilitate the translation process. In consideration of the remarkable success of translation technology, the present master’s thesis proposes a prototype for an innovative CAT tool calledTongue2Tongue which exploits state-of-the-art natural language processing techniques for enabling a wide-coverage, semantically-aware and language-independent retrieval of parallel and comparable texts. More specifically, starting from a text segment in a source language, the proposed CAT tool is capable of providing similar text segments in a target language. This function aims at facilitating the translator in understanding the content of a source text and identifying the most appropriate and adequate translations. The major innovation brought about by Tongue2Tongue consists in the implementation of innovative knowledge-based word sense disambiguation algorithms and techniques which allow to compute large-scale cross-lingual and language-independent semantic similarity among text segments. This means that Tongue2Tongue will be capable of automatically supplying parallel and comparable text segments taking into consideration the semantics of texts and regardless of the languages employed. As far as we know, this approach isbeing implemented for the first time in a CAT tool.
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Rudnick, Alexander James. "Cross-Lingual Word Sense Disambiguation for Low-Resource Hybrid Machine Translation." Thesis, Indiana University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13422906.

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This thesis argues that cross-lingual word sense disambiguation (CL-WSD) can be used to improve lexical selection for machine translation when translating from a resource-rich language into an under-resourced one, especially when relatively little bitext is available. In CL-WSD, we perform word sense disambiguation, considering the senses of a word to be its possible translations into some target language, rather than using a sense inventory developed manually by lexicographers.

Using explicitly trained classifiers that make use of source-language context and of resources for the source language can help machine translation systems make better decisions when selecting target-language words. This is especially the case when the alternative is hand-written lexical selection rules developed by researchers with linguistic knowledge of the source and target languages, but also true when lexical selection would be performed by a statistical machine translation system, when there is a relatively small amount of available target-language text for training language models.

In this work, I present the Chipa system for CL-WSD and apply it to the task of translating from Spanish to Guarani and Quechua, two indigenous languages of South America. I demonstrate several extensions to the basic Chipa system, including techniques that allow us to benefit from the wealth of available unannotated Spanish text and existing text analysis tools for Spanish, as well as approaches for learning from bitext resources that pair Spanish with languages unrelated to our intended target languages. Finally, I provide proof-of-concept integrations of Chipa with existing machine translation systems, of two completely different architectures.

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Sumita, Eiichiro. "An Example-Based Approach to Transfer and Structural Disambiguation within Machine Translation." Kyoto University, 1999. http://hdl.handle.net/2433/181852.

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Ahmady, Tobias, and Rosmar Sander Klein. "Translation of keywords between English and Swedish." Thesis, KTH, Data- och elektroteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-146637.

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In this project, we have investigated how to perform rule-based machine translation of sets of keywords between two languages. The goal was to translate an input set, which contains one or more keywords in a source language, to a corresponding set of keywords, with the same number of elements, in the target language. However, some words in the source language may have several senses and may be translated to several, or no, words in the target language. If ambiguous translations occur, the best translation of the keyword should be chosen with respect to the context. In traditional machine translation, a word's context is determined by a phrase or sentences where the word occurs. In this project, the set of keywords represents the context. By investigating traditional approaches to machine translation (MT), we designed and described models for the specific purpose of keyword- translation. We have proposed a solution, based on direct translation for translating keywords between English and Swedish. In the proposed solu- tion, we also introduced a simple graph-based model for solving ambigu- ous translations.
I detta projekt har vi undersökt hur man utför regelbaserad maskinöver- sättning av nyckelord mellan två språk. Målet var att översätta en given mängd med ett eller flera nyckelord på ett källspråk till en motsvarande, lika stor mängd nyckelord på målspråket. Vissa ord i källspråket kan dock ha flera betydelser och kan översättas till flera, eller inga, ord på målsprå- ket. Om tvetydiga översättningar uppstår ska nyckelordets bästa över- sättning väljas med hänsyn till sammanhanget. I traditionell maskinö- versättning bestäms ett ords sammanhang av frasen eller meningen som det befinner sig i. I det här projektet representerar den givna mängden nyckelord sammanhanget. Genom att undersöka traditionella tillvägagångssätt för maskinöversätt- ning har vi designat och beskrivit modeller specifikt för översättning av nyckelord. Vi har presenterat en direkt maskinöversättningslösning av nyckelord mellan engelska och svenska där vi introducerat en enkel graf- baserad modell för tvetydiga översättningar.
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Hasler, Eva Cornelia. "Dynamic topic adaptation for improved contextual modelling in statistical machine translation." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/10522.

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In recent years there has been an increased interest in domain adaptation techniques for statistical machine translation (SMT) to deal with the growing amount of data from different sources. Topic modelling techniques applied to SMT are closely related to the field of domain adaptation but more flexible in dealing with unstructured text. Topic models can capture latent structure in texts and are therefore particularly suitable for modelling structure in between and beyond corpus boundaries, which are often arbitrary. In this thesis, the main focus is on dynamic translation model adaptation to texts of unknown origin, which is a typical scenario for an online MT engine translating web documents. We introduce a new bilingual topic model for SMT that takes the entire document context into account and for the first time directly estimates topic-dependent phrase translation probabilities in a Bayesian fashion. We demonstrate our model’s ability to improve over several domain adaptation baselines and further provide evidence for the advantages of bilingual topic modelling for SMT over the more common monolingual topic modelling. We also show improved performance when deriving further adapted translation features from the same model which measure different aspects of topical relatedness. We introduce another new topic model for SMT which exploits the distributional nature of phrase pair meaning by modelling topic distributions over phrase pairs using their distributional profiles. Using this model, we explore combinations of local and global contextual information and demonstrate the usefulness of different levels of contextual information, which had not been previously examined for SMT. We also show that combining this model with a topic model trained at the document-level further improves performance. Our dynamic topic adaptation approach performs competitively in comparison with two supervised domain-adapted systems. Finally, we shed light on the relationship between domain adaptation and topic adaptation and propose to combine multi-domain adaptation and topic adaptation in a framework that entails automatic prediction of domain labels at the document level. We show that while each technique provides complementary benefits to the overall performance, there is an amount of overlap between domain and topic adaptation. This can be exploited to build systems that require less adaptation effort at runtime.
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Oliveira, Francisco de. "Unsupervised Word Sense Disambiguation using non-aligned bilingual corpus in application to Portuguese-Chinese Machine Translation." Thesis, University of Macau, 2006. http://umaclib3.umac.mo/record=b1636970.

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Laffling, John D. "Machine disambiguation and translation of polysemous nouns : a lexicon-driven model for text-semantic analysis and parallel text-dependent transfer in German-English translation of party political texts." Thesis, University of Wolverhampton, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.254466.

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Lu, Chengye. "Peer to peer English/Chinese cross-language information retrieval." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/26444/1/Chengye_Lu_Thesis.pdf.

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Peer to peer systems have been widely used in the internet. However, most of the peer to peer information systems are still missing some of the important features, for example cross-language IR (Information Retrieval) and collection selection / fusion features. Cross-language IR is the state-of-art research area in IR research community. It has not been used in any real world IR systems yet. Cross-language IR has the ability to issue a query in one language and receive documents in other languages. In typical peer to peer environment, users are from multiple countries. Their collections are definitely in multiple languages. Cross-language IR can help users to find documents more easily. E.g. many Chinese researchers will search research papers in both Chinese and English. With Cross-language IR, they can do one query in Chinese and get documents in two languages. The Out Of Vocabulary (OOV) problem is one of the key research areas in crosslanguage information retrieval. In recent years, web mining was shown to be one of the effective approaches to solving this problem. However, how to extract Multiword Lexical Units (MLUs) from the web content and how to select the correct translations from the extracted candidate MLUs are still two difficult problems in web mining based automated translation approaches. Discovering resource descriptions and merging results obtained from remote search engines are two key issues in distributed information retrieval studies. In uncooperative environments, query-based sampling and normalized-score based merging strategies are well-known approaches to solve such problems. However, such approaches only consider the content of the remote database but do not consider the retrieval performance of the remote search engine. This thesis presents research on building a peer to peer IR system with crosslanguage IR and advance collection profiling technique for fusion features. Particularly, this thesis first presents a new Chinese term measurement and new Chinese MLU extraction process that works well on small corpora. An approach to selection of MLUs in a more accurate manner is also presented. After that, this thesis proposes a collection profiling strategy which can discover not only collection content but also retrieval performance of the remote search engine. Based on collection profiling, a web-based query classification method and two collection fusion approaches are developed and presented in this thesis. Our experiments show that the proposed strategies are effective in merging results in uncooperative peer to peer environments. Here, an uncooperative environment is defined as each peer in the system is autonomous. Peer like to share documents but they do not share collection statistics. This environment is a typical peer to peer IR environment. Finally, all those approaches are grouped together to build up a secure peer to peer multilingual IR system that cooperates through X.509 and email system.
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Books on the topic "Translation disambiguation"

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Maliszewski, Julian. Disambiguation of metaphor in specialized translation and interpreting. Częstochowa: Publishing Office of Czestochowa, University of Technology, 2010.

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Laffling, John. Machine disambiguation and translation of polysemous nouns: A lexicon-driven model for text-semantic analysis and parallel text-dependent transfer in German-English translation of party political texts. Wolverhampton: Wolverhampton Polytechnic, School of Languages and European Studies, 1990.

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Stevenson, Mark, and Yorick Wilks. Word-Sense Disambiguation. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0013.

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Word-sense disambiguation (WSD) is the process of identifying the meanings of words in context. This article begins with discussing the origins of the problem in the earliest machine translation systems. Early attempts to solve the WSD problem suffered from a lack of coverage. The main approaches to tackle the problem were dictionary-based, connectionist, and statistical strategies. This article concludes with a review of evaluation strategies for WSD and possible applications of the technology. WSD is an ‘intermediate’ task in language processing: like part-of-speech tagging or syntactic analysis, it is unlikely that anyone other than linguists would be interested in its results for their own sake. ‘Final’ tasks produce results of use to those without a specific interest in language and often make use of ‘intermediate’ tasks. WSD is a long-standing and important problem in the field of language processing.
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Sadler, Victor. Working with Analogical Semantics: Disambiguation Techniques in DLT. Walter De Gruyter Inc, 1989.

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Sadler, Victor. Working With Analogical Semantics: Disambiguation Techniques in Dlt. Foris Pubns USA, 1989.

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Working With Analogical Semantics: Disambiguation Techniques in Dlt. Foris Pubns USA, 1989.

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Sadler, V. Working with Analogical Semantics: Disambiguation Techniques in Dlt. de Gruyter GmbH, Walter, 1989.

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Book chapters on the topic "Translation disambiguation"

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Trujillo, Arturo. "Disambiguation." In Translation Engines: Techniques for Machine Translation, 223–50. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0587-9_9.

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Yang, Zhizhuo, Hu Zhang, Qian Chen, and Hongye Tan. "Word Sense Disambiguation Using Context Translation." In Natural Language Understanding and Intelligent Applications, 489–96. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50496-4_41.

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Andrés, Jesús, José R. Navarro, Alfons Juan, and Francisco Casacuberta. "Word Translation Disambiguation Using Multinomial Classifiers." In Pattern Recognition and Image Analysis, 622–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11492542_76.

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Sharma, Vijay Kumar, and Namita Mittal. "Named Entity Identification Based Translation Disambiguation Model." In Lecture Notes in Computer Science, 365–72. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69900-4_46.

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Romdhane, Wiem Ben, Bilel Elayeb, and Narjès Bellamine Ben Saoud. "A Discriminative Possibilistic Approach for Query Translation Disambiguation." In Natural Language Processing and Information Systems, 366–79. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59569-6_45.

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Vintar, Špela, and Darja Fišer. "Using WordNet-Based Word Sense Disambiguation to Improve MT Performance." In Hybrid Approaches to Machine Translation, 191–205. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-21311-8_8.

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Ben Romdhane, Wiem, Bilel Elayeb, and Narjès Bellamine Ben Saoud. "Automatic Query Translation Disambiguation Using Bilingual Proximity-Based Approach." In On the Move to Meaningful Internet Systems: OTM 2018 Workshops, 218–29. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11683-5_25.

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Kimura, Fuminori, Akira Maeda, Kenji Hatano, Jun Miyazaki, and Shunsuke Uemura. "Utilizing Web Directories for Translation Disambiguation in Cross-Language Information Retrieval." In Lecture Notes in Electrical Engineering, 95–107. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-9532-0_8.

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Boughanem, M., C. Chrisment, and N. Nassr. "Investigation on Disambiguation in CLIR: Aligned Corpus and Bi-directional Translation-Based Strategies." In Lecture Notes in Computer Science, 158–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45691-0_13.

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Lee, Hyun Ah, Juntae Yoon, and Gil Chang Kim. "Exploiting a Mono-bilingual Dictionary for English-Korean Translation Selection and Sense Disambiguation." In Computational Linguistics and Intelligent Text Processing, 321–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24630-5_39.

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Conference papers on the topic "Translation disambiguation"

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Li, Cong, and Hang Li. "Word translation disambiguation using Bilingual Bootstrapping." In the 40th Annual Meeting. Morristown, NJ, USA: Association for Computational Linguistics, 2001. http://dx.doi.org/10.3115/1073083.1073141.

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Vickrey, David, Luke Biewald, Marc Teyssier, and Daphne Koller. "Word-sense disambiguation for machine translation." In the conference. Morristown, NJ, USA: Association for Computational Linguistics, 2005. http://dx.doi.org/10.3115/1220575.1220672.

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Carpuat, Marine, and Dekai Wu. "Word sense disambiguation vs. statistical machine translation." In the 43rd Annual Meeting. Morristown, NJ, USA: Association for Computational Linguistics, 2005. http://dx.doi.org/10.3115/1219840.1219888.

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M., Meera, and Sony P. "Multilingual Machine Translation with Semantic and Disambiguation." In 2014 Fourth International Conference on Advances in Computing and Communications (ICACC). IEEE, 2014. http://dx.doi.org/10.1109/icacc.2014.60.

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Lu, Chengye, Yue Xu, and Shlomo Geva. "Translation disambiguation in web-based translation extraction for English-Chinese CLIR." In the 2007 ACM symposium. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1244002.1244184.

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Monz, Christof, and Bonnie J. Dorr. "Iterative translation disambiguation for cross-language information retrieval." In the 28th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1076034.1076123.

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Sevens, Leen, Gilles Jacobs, Vincent Vandeghinste, Ineke Schuurman, and Frank Van Eynde. "Improving Text-to-Pictograph Translation Through Word Sense Disambiguation." In Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/s16-2017.

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Ben Khiroun, Oussama, Bilel Elayeb, and Narjès Bellamine Ben Saoud. "Towards a Query Translation Disambiguation Approach using Possibility Theory." In 10th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006654706060613.

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Brown, Peter F., Stephen A. Della Pietra, Vincent J. Della Pietra, and Robert L. Mercer. "A statistical approach to sense disambiguation in machine translation." In the workshop. Morristown, NJ, USA: Association for Computational Linguistics, 1991. http://dx.doi.org/10.3115/112405.112427.

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Rios, Annette, Mathias Müller, and Rico Sennrich. "The Word Sense Disambiguation Test Suite at WMT18." In Proceedings of the Third Conference on Machine Translation: Shared Task Papers. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/w18-6437.

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