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Journal articles on the topic 'Translation disambiguation'

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Author: Grafiati

Published: 4 June 2021

Last updated: 25 January 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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Nakagawa, Hiroshi. "Disambiguation of single noun translations extracted from bilingual comparable corpora." Terminology 7, no. 1 (December 7, 2001): 63–83. http://dx.doi.org/10.1075/term.7.1.06nak.

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Bilingual machine readable dictionaries are important and indispensable resources of information for cross-language information retrieval, and machine translation. Recently, these cross-language informational activities have begun to focus on specific academic or technological domains. In this paper, we describe a bilingual dictionary acquisition system which extracts translations from non-parallel but comparable corpora of a specific academic domain and disambiguates the extracted translations. The proposed method is two-fold. At the first stage, candidate terms are extracted from a Japanese and English corpus, respectively, and ranked according to their importance as terms. At the second stage, ambiguous translations are resolved by selecting the target language translation which is the nearest in rank to the source language term. Finally, we evaluate the proposed method in an experiment.

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Choe, Changil, and Hyonil Kim. "Noun Sense Disambiguation using Co-Occurrence Relation in Machine Translation." Serdica Journal of Computing 6, no. 4 (March 20, 2013): 401–8. http://dx.doi.org/10.55630/sjc.2012.6.401-408.

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Word Sense Disambiguation, the process of identifying the meaning of a word in a sentence when the word has multiple meanings, is a critical problem of machine translation. It is generally very difficult to select the correct meaning of a word in a sentence, especially when the syntactical difference between the source and target language is big, e.g., English-Korean machine translation. To achieve a high level of accuracy of noun sense selection in machine translation, we introduced a statistical method based on co-occurrence relation of words in sentences and applied it to the English-Korean machine translator RyongNamSan. ACM Computing Classification System (1998): I.2.7.

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13

Liu, Yi, Rong Jin, and Joyce Y. Chai. "A statistical framework for query translation disambiguation." ACM Transactions on Asian Language Information Processing 5, no. 4 (December 2006): 360–87. http://dx.doi.org/10.1145/1236181.1236185.

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Kumar, M. Anand, S. Rajendran, and K. P. Soman. "Cross-Lingual Preposition Disambiguation for Machine Translation." Procedia Computer Science 54 (2015): 291–300. http://dx.doi.org/10.1016/j.procs.2015.06.034.

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Liu, Peng-yuan, Tie-jun Zhao, Mu-yun Yang, and Zhuang Li. "Unsupervised Translation Disambiguation Based on Equivalent PseudoTranslation Model." Journal of Electronics & Information Technology 30, no. 7 (March 25, 2011): 1690–94. http://dx.doi.org/10.3724/sp.j.1146.2007.01029.

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KIKUI, GENICHIRO. "Disambiguation in Termlist Translation based on Semantic Proximity." Journal of Natural Language Processing 7, no. 3 (2000): 79–96. http://dx.doi.org/10.5715/jnlp.7.3_79.

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17

Wang, Lei, and Qun Ai. "Numerical Simulation of Ambiguity Resolution in Multiple Information Streams Based on Network Machine Translation." Complexity 2020 (August 17, 2020): 1–10. http://dx.doi.org/10.1155/2020/7278085.

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In natural language, the phenomenon of polysemy is widespread, which makes it very difficult for machines to process natural language. Word sense disambiguation is a key issue in the field of natural language processing. This paper introduces the more common statistical learning methods used in the field of word sense disambiguation. Using the naive Bayesian machine learning method and the feature vector set extracted and constructed by the Dice coefficient method, a semantic word disambiguation model based on semantics is realized. The results of comparative experiments show that the proposed method is better compared with known systems. This paper proposes a method for disambiguation of word segmentation in professional fields based on unsupervised learning. This method does not rely on professional domain knowledge and training corpus and only uses the frequency, mutual information, and boundary entropy information of the string in the test corpus to solve the problem of word segmentation ambiguity. The experimental results show that these three evaluation standards can solve the problem of word segmentation ambiguity in professional fields and improve the effect of word segmentation. Among them, the segmentation result using mutual information is the best, and the performance is stable.

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Kishida, Kazuaki, and Emi Ishita. "Translation disambiguation for cross-language information retrieval using context-based translation probability." Journal of Information Science 35, no. 4 (May 19, 2009): 481–95. http://dx.doi.org/10.1177/0165551509103599.

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Zhao, Guo Zhen, and Wan Li Zuo. "Semi-Supervised Word Sense Disambiguation via Context Weighting." Advanced Materials Research 1049-1050 (October 2014): 1327–38. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1327.

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Word sense disambiguation as a central research topic in natural language processing can promote the development of many applications such as information retrieval, speech synthesis, machine translation, summarization and question answering. Previous approaches can be grouped into three categories: supervised, unsupervised and knowledge-based. The accuracy of supervised methods is the highest, but they suffer from knowledge acquisition bottleneck. Unsupervised method can avoid knowledge acquisition bottleneck, but its effect is not satisfactory. With the built-up of large-scale knowledge, knowledge-based approach has attracted more and more attention. This paper introduces a new context weighting method, and based on which proposes a novel semi-supervised approach for word sense disambiguation. The significant contribution of our method is that thesaurus and machine learning techniques are integrated in word sense disambiguation. Compared with the state of the art on the test data of the English all words disambiguation task in Sensaval-3, our method yields obvious improvements over existing methods in nouns, adjectives and verbs disambiguation.

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20

Keandoungchun, Nantapong, and Nithinant Thammakoranonta. "A Word Sense Disambiguation Approach for English-Thai Translation." Applied Mechanics and Materials 411-414 (September 2013): 287–90. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.287.

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This paper proposes a novel approach for word sense disambiguation (WSD) in English to Thai. The approach generated a knowledge base which stored information of local context and then applied this information to analyze probabilities of several meanings of a target word. The meanings with the maximum probability are translated as Thai meaning of that English target word. The approach has been evaluated by analyzing the percentage of accuracy of the target word translation in each paper. It also compared the accuracy with Google translation. The experimental results indicate that the proposed approach is more accuracy than Google Translation by using paired T-test statistic equals to 6.628 with sig. = 0.00 (< 0.05)

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T.S., Santosh Kumar. "Word Sense Disambiguation Using Semantic Web for Tamil to English Statistical Machine Translation." IRA-International Journal of Technology & Engineering (ISSN 2455-4480) 5, no. 2 (November 26, 2016): 22. http://dx.doi.org/10.21013/jte.v5.n2.p1.

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<div><p><em> Machine Translation has been an area of linguistic research for almost more than two decades now. But it still remains a very challenging task for devising an automated system which will deliver accurate translations of the natural languages. However, great strides have been made in this field with more success owing to the development of technologies of the web and off late there is a renewed interest in this area of research. </em></p><p><em> Technological advancements in the preceding two decades have influenced Machine Translation in a considerable way. Several MT approaches including Statistical Machine Translation greatly benefitted from these advancements, basically making use of the availability of extensive corpora. Web technology web3.0 uses the semantic web technology which represents any object or resource in the web both syntactically and semantically. This type of representation is very much useful for the computing systems to search any content on the internet similar to lexical search and improve the internet based translations making it more effective and efficient.</em></p><p><em> In this paper we propose a technique to improve existing statistical Machine Translation methods by making use of semantic web technology. Our focus will be on Tamil and Tamil to English MT. The proposed method could successfully integrate a semantic web technique in the process of WSD which forms part of the MT system. The integration is accomplished by using the capabilities of RDFS and OWL into the WSD component of the MT model. The contribution of this work lies in showing that integrating a Semantic web technique in the WSD system significantly improves the performance of a statistical MT system for a translation from Tamil to English.</em></p></div><em> In this paper we assume the availability of large corpora in Tamil language and specific domain based ontologies with Tamil semantic web technology using web3.0. We are positive on the expansion and development of Tamil semantic web and subsequently infer that Tamil to English MT will greatly improve the disambiguation concept apart from other related benefits. This method could enable the enhancement of translation quality by improving on word sense disambiguation process while text is translated from Tamil to English language. This method can also be extended to other languages such as Hindi and Indian Languages.</em>

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Suo, Juan Juan, Bao Ying Yu, and Yue Juan He. "Improvement of Computational Translation by Using Entropy Theory." Applied Mechanics and Materials 157-158 (February 2012): 1153–56. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.1153.

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In order to improve the accuracy of the computational translation, an effective tool---the cross entropy was proposed. After the analysis of the reasons of the low accuracy, the information entropy was introduced into the disambiguation. The practice of ambiguity elimination shows the method has high accuracy and this study provides an effective way to improve the computational translation.

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Lefever, Els, and Véronique Hoste. "Parallel corpora make sense." International Journal of Corpus Linguistics 19, no. 3 (September 1, 2014): 333–67. http://dx.doi.org/10.1075/ijcl.19.3.02lef.

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We present a multilingual approach to Word Sense Disambiguation (WSD), which automatically assigns the contextually appropriate sense to a given word. Instead of using a predefined monolingual sense-inventory, we use a language-independent framework by deriving the senses of a given word from word alignments on a multilingual parallel corpus, which we made available for corpus linguistics research. We built five WSD systems with English as the input language and translations in five supported languages (viz. French, Dutch, Italian, Spanish and German) as senses. The systems incorporate both binary translation features and local context features. The experimental results are very competitive, which confirms our initial hypothesis that each language contributes to the disambiguation of polysemous words. Because our system extracts all information from the parallel corpus, it offers a flexible language-independent approach, which implicitly deals with the sense distinctness issue and allows us to bypass the knowledge acquisition bottleneck for WSD.

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Pu, Xiao, Nikolaos Pappas, James Henderson, and Andrei Popescu-Belis. "Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation." Transactions of the Association for Computational Linguistics 6 (December 2018): 635–49. http://dx.doi.org/10.1162/tacl_a_00242.

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This paper demonstrates that word sense disambiguation (WSD) can improve neural machine translation (NMT) by widening the source context considered when modeling the senses of potentially ambiguous words. We first introduce three adaptive clustering algorithms for WSD, based on k-means, Chinese restaurant processes, and random walks, which are then applied to large word contexts represented in a low-rank space and evaluated on SemEval shared-task data. We then learn word vectors jointly with sense vectors defined by our best WSD method, within a state-of-the-art NMT system. We show that the concatenation of these vectors, and the use of a sense selection mechanism based on the weighted average of sense vectors, outperforms several baselines including sense-aware ones. This is demonstrated by translation on five language pairs. The improvements are more than 1 BLEU point over strong NMT baselines, +4% accuracy over all ambiguous nouns and verbs, or +20% when scored manually over several challenging words.

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Thwet Thwet Aung, Nyein, Khin Mar Soe, and Ni Lar Thein. "Ambiguous Myanmar Word Disambiguation System for MyanmarEnglish Statistical Machine Translation." International Journal of Computer Applications 27, no. 8 (August 31, 2011): 5–11. http://dx.doi.org/10.5120/3323-4568.

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DUQUE, ANDRES, LOURDES ARAUJO, and JUAN MARTINEZ-ROMO. "CO-graph: A new graph-based technique for cross-lingual word sense disambiguation." Natural Language Engineering 21, no. 5 (April 16, 2015): 743–72. http://dx.doi.org/10.1017/s1351324915000091.

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AbstractIn this paper, we present a new method based on co-occurrence graphs for performing Cross-Lingual Word Sense Disambiguation (CLWSD). The proposed approach comprises the automatic generation of bilingual dictionaries, and a new technique for the construction of a co-occurrence graph used to select the most suitable translations from the dictionary. Different algorithms that combine both the dictionary and the co-occurrence graph are then used for performing this selection of the final translations: techniques based on sub-graphs (communities) containing clusters of words with related meanings, based on distances between nodes representing words, and based on the relative importance of each node in the whole graph. The initial output of the system is enhanced with translation probabilities, provided by a statistical bilingual dictionary. The system is evaluated using datasets from two competitions: task 3 of SemEval 2010, and task 10 of SemEval 2013. Results obtained by the different disambiguation techniques are analysed and compared to those obtained by the systems participating in the competitions. Our system offers the best results in comparison with other unsupervised systems in most of the experiments, and even overcomes supervised systems in some cases.

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Hayes, Jeff. "Intentional Ambiguity in Ruth 4.5: Implications for Interpretation of Ruth." Journal for the Study of the Old Testament 41, no. 2 (December 2016): 159–82. http://dx.doi.org/10.1177/0309089215611546.

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Starting from Robert Holmstedt's translation of Ruth 4.5, this article develops and applies a preliminary hermeneutic for ambiguity to Boaz's speech in 4.5 and its disambiguation in 4.9–10. Arguing for freedom to interpret ambiguous texts strategically, this study demonstrates how Holmstedt's translation coupled with the author's preliminary hermeneutic leads to a reassessment of the scene at the city gate that also resolves several persistent interpretive difficulties in Ruth.

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Ge, Ren, Yang Yong, and Xu Chun. "Uyghur-Chinese Translation Disambiguation Method Research Based on Knowledge Automatic-Acquisition." Open Cybernetics & Systemics Journal 8, no. 1 (December 31, 2014): 739–44. http://dx.doi.org/10.2174/1874110x01408010739.

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LIU, Peng-Yuan, and Tie-Jun ZHAO. "Unsupervised Translation Disambiguation Based on Web Indirect Association of Bilingual Word." Journal of Software 21, no. 4 (March 11, 2010): 575–85. http://dx.doi.org/10.3724/sp.j.1001.2010.03574.

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Kale, Swati, and Ujjwala Gawande. "A brief review on Word Sense Disambiguation Approaches." International Journal of Engineering & Technology 7, no. 2.14 (April 6, 2018): 458. http://dx.doi.org/10.14419/ijet.v7i2.9649.

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To deal with the problem of text retrieval, machine translation, query processing, speech processing, the word sense disambiguation (WSD) performs very important role. WSD is an AI complete problem. This paper presents the significance, approaches along with work done in the field of WSD. Apple amount of work has been done in WSD for foreign languages but for Indian languages this issue is still about to concern. This paper tries to find out different limitations and challenges in WSD, based on which better approach will be decided by the researchers to solve the problem of WSD.

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Abd-Rashid, Amir, Shuzlina Abdul-Rahman, Nor Nadiah Yusof, and Azlinah Mohamed. "WORD SENSE DISAMBIGUATION USING FUZZY SEMANTIC-BASED STRING SIMILARITY MODEL." MALAYSIAN JOURNAL OF COMPUTING 3, no. 2 (December 31, 2018): 154. http://dx.doi.org/10.24191/mjoc.v3i2.4890.

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Sentences are the language of human communication. This communication medium is so fluid that words and meaning can have many interpretations by readers. Besides, a document that consists of thousands of sentences would be tough for the reader to understand the content. In this case, computer power is required to analyse the gigantic batch size of the text. However, there are several arguments that actively discuss regarding the output generated by a computer toward the meaning of the passage in terms of accuracy. One of the reasons for this issue is the existing of the ambiguous word with multiple meanings in a sentence. The passage might be incorrectly translated due to wrong sense selection during the early phase of sentence translation. Translating sentence in this paper means either the sentence has a negative or positive meaning. Thus, this research discusses on how to disambiguate the term in a sentence by referring to the Wordnet repository by proposing the use of fuzzy semantic-based similarity model. The proposed model promising to return a good result for detecting the similarity of two sentences that has been proven in the past research. At the end of this paper, preliminary result which shows the flow of how the proposed framework working is discussed.

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Zhang, Chun-Xiang, Rui Liu, Xue-Yao Gao, and Bo Yu. "Graph Convolutional Network for Word Sense Disambiguation." Discrete Dynamics in Nature and Society 2021 (September 30, 2021): 1–12. http://dx.doi.org/10.1155/2021/2822126.

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Word sense disambiguation (WSD) is an important research topic in natural language processing, which is widely applied to text classification, machine translation, and information retrieval. In order to improve disambiguation accuracy, this paper proposes a WSD method based on the graph convolutional network (GCN). Word, part of speech, and semantic category are extracted from contexts of the ambiguous word as discriminative features. Discriminative features and sentence containing the ambiguous word are used as nodes to construct the WSD graph. Word2Vec tool, Doc2Vec tool, pointwise mutual information (PMI), and TF-IDF are applied to compute embeddings of nodes and edge weights. GCN is used to fuse features of a node and its neighbors, and the softmax function is applied to determine the semantic category of the ambiguous word. Training corpus of SemEval-2007: Task #5 is adopted to optimize the proposed WSD classifier. Test corpus of SemEval-2007: Task #5 is used to test the performance of WSD classifier. Experimental results show that average accuracy of the proposed method is improved.

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Togeby, Ole. "Parsing Danish Text in Eurotra." Nordic Journal of Linguistics 11, no. 1-2 (June 1988): 175–91. http://dx.doi.org/10.1017/s0332586500001803.

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The machine translation project Eurotra is described as a multilanguage modular translation system with 9 monolingual analysis modules, 72 bilingual transfer modules, and 9 monolingual synthesis modules. The analysis module for Danish is described as a three-step parser with structure generation rules for immediate constituent structure, syntactic structure, and semantic structure, and translation rules between them. The topological grammatical description of Danish proposed by Paul Diderichsen, is shown to be useful in building the parser for Danish, especially with respect to the interaction between empty slots and filled slot in the topological pattern. Lastly, the special problem with parsing and disambiguation of sentences that allow many pp attachments patterns is mentioned and a solution is suggested.

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Lee, Hyun-Ah. "Translation Disambiguation Based on 'Word-to-Sense and Sense-to-Word' Relationship." KIPS Transactions:PartB 13B, no. 1 (February 1, 2006): 71–76. http://dx.doi.org/10.3745/kipstb.2006.13b.1.071.

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Gurleen Kaur Sidhu, Gurleen Kaur Sidhu. "Role of Machine Translation and Word Sense Disambiguation in Natural Language Processing." IOSR Journal of Computer Engineering 11, no. 3 (2013): 78–83. http://dx.doi.org/10.9790/0661-1137883.

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Miangah, Tayebeh Mosavi, and Ali Delavar Khalafi. "Word Sense Disambiguation Using Target Language Corpus in a Machine Translation System." Digital Scholarship in the Humanities 20, no. 2 (June 1, 2005): 237–49. http://dx.doi.org/10.1093/llc/fqi029.

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LEE, HYUN AH, JUNTAE YOON, and GIL CHANG KIM. "Translation Selection by Combining Multiple Measures for Sense Disambiguation and Word Selection." International Journal of Computer Processing of Languages 16, no. 03 (September 2003): 219–39. http://dx.doi.org/10.1142/s0219427903000905.

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DUAN, JIANYONG, RUZHAN LU, and XUENING LI. "MULTI-ENGINE COLLABORATIVE BOOTSTRAPPING FOR WORD SENSE DISAMBIGUATION." International Journal on Artificial Intelligence Tools 16, no. 03 (June 2007): 465–82. http://dx.doi.org/10.1142/s0218213007003369.

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In this paper we propose a new word sense disambiguation method called Multi-engine Collaborative Bootstrapping (MCB) that combines different types of corpora and also uses two languages for bootstrapping. MCB uses the bilingual bootstrapping as its core algorithm that leading to incremental knowledge acquisition. The EM model is applied to train parameters in a base learner. The feature translation model is improved by semantic correlation estimation. In addition we use multi-engine selection to produce qualified starting seeds from parallel corpora and monolingual corpora. Those seeds that are generated through unsupervised machine learning approaches can also ensure bootstrapping effectiveness in contrast with manually selected seeds in spite of their different selection mechanisms. Experimental results prove the effectiveness of MCB. Some factors including feature space and starting seed number are concerned involved in our experiments because the EM algorithm is sensitive to starting values. Limitation of resources is also a concern.

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Zhang, M., X. Xiao, D. Xiong, and Q. Liu. "Topic-Based Dissimilarity and Sensitivity Models for Translation Rule Selection." Journal of Artificial Intelligence Research 50 (May 8, 2014): 1–30. http://dx.doi.org/10.1613/jair.4265.

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Translation rule selection is a task of selecting appropriate translation rules for an ambiguous source-language segment. As translation ambiguities are pervasive in statistical machine translation, we introduce two topic-based models for translation rule selection which incorporates global topic information into translation disambiguation. We associate each synchronous translation rule with source- and target-side topic distributions.With these topic distributions, we propose a topic dissimilarity model to select desirable (less dissimilar) rules by imposing penalties for rules with a large value of dissimilarity of their topic distributions to those of given documents. In order to encourage the use of non-topic specific translation rules, we also present a topic sensitivity model to balance translation rule selection between generic rules and topic-specific rules. Furthermore, we project target-side topic distributions onto the source-side topic model space so that we can benefit from topic information of both the source and target language. We integrate the proposed topic dissimilarity and sensitivity model into hierarchical phrase-based machine translation for synchronous translation rule selection. Experiments show that our topic-based translation rule selection model can substantially improve translation quality.

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Osimo, Bruno. "On psychological aspects of translation." Sign Systems Studies 30, no. 2 (December 31, 2002): 607–27. http://dx.doi.org/10.12697/sss.2002.30.2.15.

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Translation science is going through a preliminary stage of selfdefinition. Jakobson’s essay “On linguistic aspects of translation”, whose title is re-echoed in the title of this article, despite the linguistic approach suggested, opened, in 1959, the study of translation to disciplines other than linguistics, semiotics to start with. Many developments in the semiotics of translation — particularly Torop’s theory of total translation — take their cue from the celebrated category “intersemiotic translation or transmutation” outlined in that 1959 article. I intend to outline here the contributions that the science of translation — following a semiotic perspective opened by Peirce and continued by Torop — can gather from another discipline: psychology. The “totalistic” approach to translation provided by Torop can be more deeply enforced by applying to it the consequences deriving from the psychological insight offered by the concept of “interpretant” as mental sign; the perceptual interpretation of the prototext; reading and writing as intersemiotic translation processes; unlimited semiosis as interminable analysis; primary and secondary process in dreams and in other kinds of translation; metaphor and disambiguation as mental processes; the defenses activated when translation criticism (review) and self-criticism (revision) are made.

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Flati, T., and R. Navigli. "The CQC Algorithm: Cycling in Graphs to Semantically Enrich and Enhance a Bilingual Dictionary." Journal of Artificial Intelligence Research 43 (February 19, 2012): 135–71. http://dx.doi.org/10.1613/jair.3456.

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Bilingual machine-readable dictionaries are knowledge resources useful in many automatic tasks. However, compared to monolingual computational lexicons like WordNet, bilingual dictionaries typically provide a lower amount of structured information, such as lexical and semantic relations, and often do not cover the entire range of possible translations for a word of interest. In this paper we present Cycles and Quasi-Cycles (CQC), a novel algorithm for the automated disambiguation of ambiguous translations in the lexical entries of a bilingual machine-readable dictionary. The dictionary is represented as a graph, and cyclic patterns are sought in the graph to assign an appropriate sense tag to each translation in a lexical entry. Further, we use the algorithm's output to improve the quality of the dictionary itself, by suggesting accurate solutions to structural problems such as misalignments, partial alignments and missing entries. Finally, we successfully apply CQC to the task of synonym extraction.

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Grif, Mikhail G., Olga O. Korolkova, and Yuliya S. Manueva. "A new algorithm and other software for disambiguation of polysemy and homonymy for computer translation into Russian Sign Language based on a semantic principle." NSU Vestnik. Series: Linguistics and Intercultural Communication 16, no. 3 (2018): 32–44. http://dx.doi.org/10.25205/1818-7935-2018-16-3-32-44.

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The paper analyses current computer Sign Language translation systems. Their advantages and disadvantages are detected. The main drawback is the lack of original text semantic analysis module capable of solving the task of disambiguation. A general scheme of translation system from phonic Russian to Russian Sign language including a module for semantic analysis is presented. It includes a block of source code analysis, developed by the authors, responsible for handling the semantic component of the Russian language. The semantic module relies on Tuzov’s dictionary. The semantic analysis algorithm is also described. The text analysis is completed when each word gets only one semantic description thus solving the problem of ambiguity. The most important developments of the semantic analysis module include the following: expanded collection of gestures, parsing of complex sentences, account in the algorithm analyses predicates classifier of Russian Sign Language. Testing of algorithm is made. The article compares the existing systems of computer translation from phonic to the sign language. The advantages and disadvantages of the considered systems are revealed and a conclusion is made about the need to take into account the semantic aspect of the translation process. A technology of semantic analysis is suggested. The model to choose an adequate meaning of a polysemic word or homonym on the basis of the automatic text processing system «Dialing» is described. Examples of the use of the software are given. The questions of testing the working capacity of the semantic analysis module are given due attention too. To enhance its efficiency, the system of semantic analysis was added to the translation system «Surdophone». To verify the efficiency of the semantic module’s operation, a comparison is made with the definition of some words’ semantic meanings by the systems «Yandex Translator» and «Google Translator». The present system showed its advantage in more complex cases. Also, the base of gestures of the RSL whose names are homonyms and polysemic words of the Russian language, were added and the features of their performance were revealed.

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Kim, Yu-Seop, and Jeong-Ho Chang. "Target Word Selection Disambiguation using Untagged Text Data in English-Korean Machine Translation." KIPS Transactions:PartB 11B, no. 6 (October 1, 2004): 749–58. http://dx.doi.org/10.3745/kipstb.2004.11b.6.749.

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LIU, Peng-Yuan, and Tie-Jun ZHAO. "Unsupervised Translation Disambiguation by Using Semantic Dictionary and Mining Language Model from Web." Journal of Software 20, no. 5 (March 10, 2010): 1292–300. http://dx.doi.org/10.3724/sp.j.1001.2009.03367.

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Nurifan, Farza, Riyanarto Sarno, and Cahyaningtyas Sekar Wahyuni. "Developing Corpora using Wikipedia and Word2vec for Word Sense Disambiguation." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 3 (December 1, 2018): 1239. http://dx.doi.org/10.11591/ijeecs.v12.i3.pp1239-1246.

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Word Sense Disambiguation (WSD) is one of the most difficult problems in the artificial intelligence field or well known as AI-hard or AI-complete. A lot of problems can be solved using word sense disambiguation approaches like sentiment analysis, machine translation, search engine relevance, coherence, anaphora resolution, and inference. In this paper, we do research to solve WSD problem with two small corpora. We propose the use of Word2vec and Wikipedia to develop the corpora. After developing the corpora, we measure the sentence similarity with the corpora using cosine similarity to determine the meaning of the ambiguous word. Lastly, to improve accuracy, we use Lesk algorithms and Wu Palmer similarity to deal with problems when there is no word from a sentence in the corpora (we call it as semantic similarity). The results of our research show an 86.94% accuracy rate and the semantic similarity improve the accuracy rate by 12.96% in determining the meaning of ambiguous words.

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Floor, Sebastian. "Four Bible Translation Types and Some Criteria to Distinguish Them." Journal of Translation 3, no. 2 (2007): 1–22. http://dx.doi.org/10.54395/jot-pfw5h.

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The purpose of this paper is to contribute to the discussion on the classification of Bible translation types. This paper proposes four types instead of the traditional two: literal and idiomatic or dynamic equivalent. The four types are Type 1) close (or literal) resemblance, Type 2) open resemblance, Type 3) close (or limited) interpretative, and Type 4) open interpretative. There are several continua of criteria: the degree of resemblance to the original semantic content, the degree of explicitness, and the type of adjustments needed to unpack the meaning. Eight criteria of adjustments are proposed to distinguish these four types: 1) order of clauses and phrases, 2) sentence length, 3) reference disambiguation and tracking, 4) concordance of lexical items, 5) key terms and unknown terms, 6) figurative usage and idioms, 7) transition marking, and 8) information structure.

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Mallat, Souheyl, Mohamed Achraf Ben Mohamed, Emna Hkiri, Anis Zouaghi, and Mounir Zrigui. "Semantic and Contextual Knowledge Representation for Lexical Disambiguation: Case of Arabic-French Query Translation." Journal of Computing and Information Technology 22, no. 3 (2014): 191. http://dx.doi.org/10.2498/cit.1002234.

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Nguyen, Quang-Phuoc, Anh-Dung Vo, Joon-Choul Shin, Phuoc Tran, and Cheol-Young Ock. "Korean-Vietnamese Neural Machine Translation System With Korean Morphological Analysis and Word Sense Disambiguation." IEEE Access 7 (2019): 32602–16. http://dx.doi.org/10.1109/access.2019.2902270.

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Nguyen, Quang-Phuoc, Anh-Dung Vo, Joon-Choul Shin, and Cheol-Young Ock. "Effect of Word Sense Disambiguation on Neural Machine Translation: A Case Study in Korean." IEEE Access 6 (2018): 38512–23. http://dx.doi.org/10.1109/access.2018.2851281.

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Zhang, Pei Ying. "Word Similarity Computation Based on WordNet and HowNet." Applied Mechanics and Materials 336-338 (July 2013): 2115–18. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2115.

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Word similarity computation is broadly used in many applications, such as information retrieval, information extraction, text categorization, word sense disambiguation and example-based machine translation and so on. The main obstacle of word similarity computation lie in that how to develop a computational algorithm that is capable of generating satisfactory results close to how human perceive. This paper proposed an approach of word similarity computation which combined WordNet and HowNet. Experiments on Chinese word pairs show that our method is closest to human similarity judgments when compared to the major state-of-art methods.

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