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.
Full textZhang, 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.
Full textMartelli, Federico. "Word Sense Disambiguation in Tongue2Tongue, a Pioneering Computer-aided Translation Tool." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.
Find full textRudnick, Alexander James. "Cross-Lingual Word Sense Disambiguation for Low-Resource Hybrid Machine Translation." Thesis, Indiana University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13422906.
Full textThis 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.
Sumita, Eiichiro. "An Example-Based Approach to Transfer and Structural Disambiguation within Machine Translation." Kyoto University, 1999. http://hdl.handle.net/2433/181852.
Full textAhmady, 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.
Full textI 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.
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.
Full textOliveira, 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.
Full textLaffling, 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.
Full textLu, 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.
Full textLu, Chengye. "Peer to peer English/Chinese cross-language information retrieval." Queensland University of Technology, 2008. http://eprints.qut.edu.au/26444/.
Full textSpecia, Lucia. "Uma abordagem híbrida relacional para a desambiguação lexical de sentido na tradução automática." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-05122007-205308/.
Full textCrosslingual communication has become a very imperative task in the current scenario with the increasing amount of information dissemination in several languages. In this context, machine translation systems, which can facilitate such communication by providing automatic translations, are of great importance. Although research in Machine Translation dates back to the 1950\'s, the area still has many problems. One of the main problems is that of lexical ambiguity, that is, the need for lexical choice when translating a source language word that has several translation options in the target language. This problem is even more complex when only sense variations are found in the translation options, a problem named \"sense ambiguity\". Several approaches have been proposed for word sense disambiguation, but they are in general monolingual (for English) and application-independent. Moreover, they have limitations regarding the types of knowledge sources that can be exploited. Particularly, there is no significant research aiming to word sense disambiguation involving Portuguese. The goal of this PhD work is the proposal and development of a novel approach for word sense disambiguation which is specifically designed for machine translation, follows a hybrid methodology (knowledge and corpus-based), and employs a relational formalism to represent various kinds of knowledge sources and disambiguation examples, by using Inductive Logic Programming. Several experiments have shown that the proposed approach overcomes alternative approaches in multilingual disambiguation and achieves higher or comparable results to the state of the art in monolingual disambiguation. Additionally, the approach has shown to effectively assist lexical choice in a statistical machine translation system
Vial, Loïc. "Modèles neuronaux joints de désambiguïsation lexicale et de traduction automatique." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM032.
Full textWord Sense Disambiguation (WSD) and Machine Translation (MT) are two central and among the oldest tasks of Natural Language Processing (NLP). Although they share a common origin, WSD being initially conceived as a fundamental problem to be solved for MT, the two tasks have subsequently evolved very independently of each other. Indeed, on the one hand, MT has been able to overcome the explicit disambiguation of terms thanks to statistical and neural models trained on large amounts of parallel corpora, and on the other hand, WSD, which faces some limitations such as the lack of unified resources and a restricted scope of applications, remains a major challenge to allow a better understanding of the language in general.Today, in a context in which neural networks and word embeddings are becoming more and more important in NLP research, the recent neural architectures and the new pre-trained language models offer not only some new possibilities for developing more efficient WSD and MT systems, but also an opportunity to bring the two tasks together through joint neural models, which facilitate the study of their interactions.In this thesis, our contributions will initially focus on the improvement of WSD systems by unifying the ressources that are necessary for their implementation, constructing new neural architectures and developing original approaches to improve the coverage and the performance of these systems. Then, we will develop and compare different approaches for the integration of our state of the art WSD systems and language models into MT systems for the overall improvement of their performance. Finally, we will present a new architecture that allows to train a joint model for both WSD and MT, based on our best neural systems
Hsiao, Meng-Chin, and 蕭孟勤. "Word Translation Disambiguation via Dependency." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/00309720927426754978.
Full text國立清華大學
資訊系統與應用研究所
95
We introduce a new method for automatically disambiguation of word translations by using dependency relationships. In our approach, we learn the relationships between translations and dependency relationships from a parallel corpus. The method consists of a training stage and a runtime stage. During the training stage, the system automatically learns a translation decision list based on source sentences and its dependency relationships. At runtime, for each content word in the given sentence, we give a most appropriate Chinese translation relevant to the context of the given sentence according to the decision list. We also describe the implementation of the proposed method using bilingual Hong Kong news and Hong Kong Hansard corpus. In the experiment, we use five different ways to translate content words in the test data and evaluate the results based an automatic BLEU-like evaluation methodology. Experimental results indicate that dependency relations can obviously help us to disambiguate word translations and some kinds of dependency are more effective than others.
Chun-Chin, Chang, and 張俊欽. "Web-Based Unsupervised Method for Word Translation Disambiguation." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/79966976872434227526.
Full text國立清華大學
資訊工程學系
93
We introduce a new method for automatically disambiguation of word translations by using collocations. In our approach, we learn the the relationships between translation categories and collocations using the information on the Web. The method consists of a training stage and a runtime stage. During the training stage, the method involves automatically acquisition of collocates of target words from a large corpus, distinguishing of collocations into two or more parts by translations of a given word, and learning a translation decision list based on sentences with the target word and its collocates automatically acquired from the Web. At runtime, the target word in the given sentence is translated according to the decision list model. We also describe the implementation of a prototype system of the proposed method, experiments, and evaluation. In the experiment, we used four polysemous words to assess the performance of the method compare the results against judgments made by human subjects. Experimental results indicate that the proposed unsupervised method based on the Web as corpus overcomes the knowledge acquisition bottleneck and provides a promising approach for word translation disambiguation.
Huang, Jyun-Wei, and 黃俊瑋. "Japanese Opinion Word Translation Based on Unsupervised Word Sense Disambiguation in the Travel Domain." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/40023352212851154295.
Full text元智大學
資訊工程學系
98
This paper proposes a Japanese opinion word translation method based on unsupervised word sense disambiguation. The method comprises the corpus preparation, opinion word dictionary construction, and weighting method. Different from the machine translation, our method does not need parallel corpora, tagged corpora or parsing tree banks. Our method is low-cost but effective, and requires a well-made bilingual dictionary only. Besides, our method can extract key information from the opinions to help users understand the opinions. We construct four configurations and evaluate our method on four Japanese opinion words with high frequency. The evaluation result shows that the dependency grammar and opinion word dictionary is effective on opinion word translation. Our method can deal with the translation disambiguation problem and improve the translation precision to help user realize Japanese opinions.