Relevant bibliographies by topics / Paraphrase extraction

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  4. Conference papers
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Academic literature on the topic 'Paraphrase extraction'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Paraphrase extraction"

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HO, CHUKFONG, MASRAH AZRIFAH AZMI MURAD, RABIAH ABDUL KADIR, and SHYAMALA DORAISAMY. "COMPARING TWO CORPUS-BASED METHODS FOR EXTRACTING PARAPHRASES TO DICTIONARY-BASED METHOD." International Journal of Semantic Computing 05, no. 02 (June 2011): 133–78. http://dx.doi.org/10.1142/s1793351x11001225.

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Paraphrase extraction plays an increasingly important role in language-related research and applications in areas such as information retrieval, question answering and automatic machine evaluation. Most of the existing methods extract paraphrases from different types of corpora by using syntactic-based approaches. Since a syntactic-based approach relies on the similarity of context to identify and capture paraphrases, other than paraphrases, other terms which tend to appear in a similar context such as loosely related terms and functionally similar yet unrelated terms tend to be extracted. Besides, different types of corpora suffer from different kinds of problems such as limited availability and domain biased. This paper presents a solely semantic-based paraphrase extraction model. This model collects paraphrases from multiple lexical resources and validates those paraphrases semantically in three ways; by computing domain similarity, definition similarity and word similarity. This model is benchmarked with two outstanding syntactic-based approaches. The experimental results from a manual evaluation show that the proposed model outperforms the benchmarks. The results indicate that a semantic-based approach should be applied in paraphrase extraction instead of a syntactic-based approach. The results further suggest that a hybrid of these two approaches should be applied if one targets strictly precise paraphrases.
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Pöckelmann, Marcus, Janis Dähne, Jörg Ritter, and Paul Molitor. "Fast paraphrase extraction in Ancient Greek literature." it - Information Technology 62, no. 2 (April 26, 2020): 75–89. http://dx.doi.org/10.1515/itit-2019-0042.

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AbstractIn this paper,A shorter version of the paper appeared in German in the final report of the Digital Plato project which was funded by the Volkswagen Foundation from 2016 to 2019. [35], [28]. we present a method for paraphrase extraction in Ancient Greek that can be applied to huge text corpora in interactive humanities applications. Since lexical databases and POS tagging are either unavailable or do not achieve sufficient accuracy for ancient languages, our approach is based on pure word embeddings and the word mover’s distance (WMD) [20]. We show how to adapt the WMD approach to paraphrase searching such that the expensive WMD computation has to be computed for a small fraction of the text segments contained in the corpus, only. Formally, the time complexity will be reduced from \mathcal{O}(N\cdot {K^{3}}\cdot \log K) to \mathcal{O}(N+{K^{3}}\cdot \log K), compared to the brute-force approach which computes the WMD between each text segment of the corpus and the search query. N is the length of the corpus and K the size of its vocabulary. The method, which searches not only for paraphrases of the same length as the search query but also for paraphrases of varying lengths, was evaluated on the Thesaurus Linguae Graecae® (TLG®) [25]. The TLG consists of about 75\cdot {10^{6}} Greek words. We searched the whole TLG for paraphrases for given passages of Plato. The experimental results show that our method and the brute-force approach, with only very few exceptions, propose the same text passages in the TLG as possible paraphrases. The computation times of our method are in a range that allows its application in interactive systems and let the humanities scholars work productively and smoothly.
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Chitra, A., and Anupriya Rajkumar. "Paraphrase Extraction using fuzzy hierarchical clustering." Applied Soft Computing 34 (September 2015): 426–37. http://dx.doi.org/10.1016/j.asoc.2015.05.017.

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VILA, M., H. RODRÍGUEZ, and M. A. MARTÍ. "Relational paraphrase acquisition from Wikipedia: The WRPA method and corpus." Natural Language Engineering 21, no. 3 (September 16, 2013): 355–89. http://dx.doi.org/10.1017/s1351324913000235.

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AbstractParaphrase corpora are an essential but scarce resource in Natural Language Processing. In this paper, we present the Wikipedia-based Relational Paraphrase Acquisition (WRPA) method, which extracts relational paraphrases from Wikipedia, and the derived WRPA paraphrase corpus. The WRPA corpus currently covers person-related and authorship relations in English and Spanish, respectively, suggesting that, given adequate Wikipedia coverage, our method is independent of the language and the relation addressed. WRPA extracts entity pairs from structured information in Wikipedia applying distant learning and, based on the distributional hypothesis, uses them as anchor points for candidate paraphrase extraction from the free text in the body of Wikipedia articles. Focussing on relational paraphrasing and taking advantage of Wikipedia-structured information allows for an automatic and consistent evaluation of the results. The WRPA corpus characteristics distinguish it from other types of corpora that rely on string similarity or transformation operations. WRPA relies on distributional similarity and is the result of the free use of language outside any reformulation framework. Validation results show a high precision for the corpus.
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Recasens, Marta, and Marta Vila. "On Paraphrase and Coreference." Computational Linguistics 36, no. 4 (December 2010): 639–47. http://dx.doi.org/10.1162/coli_a_00014.

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By providing a better understanding of paraphrase and coreference in terms of similarities and differences in their linguistic nature, this article delimits what the focus of paraphrase extraction and coreference resolution tasks should be, and to what extent they can help each other. We argue for the relevance of this discussion to Natural Language Processing.
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ZHAO, Shi-Qi, Lin ZHAO, Ting LIU, and Sheng LI. "Paraphrase Collocation Extraction Based on Binary Classification." Journal of Software 21, no. 6 (June 29, 2010): 1267–76. http://dx.doi.org/10.3724/sp.j.1001.2010.03586.

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Mauro Mirto, Ignazio. "Automatic Extraction of Semantic Roles in Support Verb Constructions." International Journal on Natural Language Computing 10, no. 03 (June 30, 2021): 1–10. http://dx.doi.org/10.5121/ijnlc.2021.10301.

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This paper deals with paraphrastic relations in Italian. In the following sentences: (a) Max strappò delle lacrime a Sara 'Max moved Sara to tears' and (b) Max fece piangere Sara 'Max made Sara cry', the verbs differ syntactically and semantically. Strappare 'tear/rip/wring' is transitive, fare ‘have/make’ is a causative, and piangere 'cry' is intransitive. Despite this, a translation of (a) as (b) is legitimate and therefore (a) is a paraphrase of (b). In theoretical linguistics this raises an issue concerning the relationship between strappare and fare/piangere in Italian, and that in English between move and make. In computational linguistics, can such paraphrases be obtained automatically? Which apparatus should be deployed? The aim of this paper is to suggest a pathway with which to answer these questions.
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Hu Hongsi, Zhang Wenbo, and Yao Tianfang. "Paraphrase Extraction from Interactive Q&A Communities." International Journal of Information Processing and Management 4, no. 2 (April 30, 2013): 45–52. http://dx.doi.org/10.4156/ijipm.vol4.issue2.6.

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Glazkova, Anna Valer'evna. "Statistical evaluation of the information content of attributes for the task of searching for semantically close sentences." Программные системы и вычислительные методы, no. 1 (January 2020): 8–17. http://dx.doi.org/10.7256/2454-0714.2020.1.31728.

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The paper presents the results of evaluating the informative value of quantitative and binary signs to solve the problem of finding semantically close sentences (paraphrases). Three types of signs are considered in the article: those built on vector representations of words (according to the Word2Vec model), based on the extraction of numbers and structured information and reflecting the quantitative characteristics of the text. As indicators of information content, the percentage of paraphrases among examples with a characteristic, and the percentage of paraphrases with a attribute (for binary characteristics), as well as estimates using the accumulated frequency method (for quantitative indicators) are used. The assessment was conducted on the Russian paraphrase corps. The set of features considered in the work was tested as input for two machine learning models for defining semantically close sentences: reference vector machines (SVMs) and a recurrent neural network model. The first model accepts only the considered set of signs as input parameters, the second - the text in the form of sequences and the set of signs as an additional input. The quality of the models was 67.06% (F-measure) and 69.49% (accuracy) and 79.85% (F-measure) and 74.16% (accuracy), respectively. The result obtained in the work is comparable with the best results of the systems presented in 2017 at the competition for the definition of paraphrase for the Russian language (the second result for the F-measure, the third result for accuracy). The results proposed in the work can be used both in the implementation of search models for semantically close fragments of texts in natural language, and for the analysis of Russian-language paraphrases from the point of view of computer linguistics.
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박에스더, 임해창, 김민정, and 이형규. "Pivot Discrimination Approach for Paraphrase Extraction from Bilingual Corpus." Korean Journal of Cognitive Science 22, no. 1 (March 2011): 57–78. http://dx.doi.org/10.19066/cogsci.2011.22.1.004.

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Dissertations / Theses on the topic "Paraphrase extraction"

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Kampeera, Wannachai. "Analyse linguistique et formalisation pour le traitement automatique de la paraphrase." Thesis, Besançon, 2013. http://www.theses.fr/2013BESA1011/document.

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Les relations paraphrastiques entre plusieurs ensembles de paraphrasespeuvent se décrire en termes de suites de transformations textuelles. Pour qu’il aitparaphrase, il faut qu’une substitution lexicale noyau se mette en route entrainantd’autres modifications syntaxiques, lexicales et morphologiques.Après avoir décrit les mécanismes de paraphrasage récurrents, nous avons proposédeux formalisations. La première est théorique et explique les différentes relationsparaphrastiques entretenues par les paraphrases entre-elles. La deuxième,tournée vers des applications, formalise les structures paraphrastiques sous-formede prédicats-arguments. Nous considérons cette dernière adaptée au traitementautomatique de la paraphrase.Nous avons à la suite implémenté un système d’extraction de structures paraphrastiques.Il s’agit d’un système opérationnel appliqué à un volume de donnéesrelevant de notre domaine d’étude, et dont le but est de donner un exemple concretd’emploi possible de notre formalisation.Mots-clés : paraphrase, structures paraphrastiques, traitement automatique dela paraphrase, extraction des structures paraphrastiques
The relations between sets of paraphrases can be described as seriesof textual transformations. To rephrase, an initial lexical substitution starts, thentriggers other syntactic, lexical and morphological changes.After having described the frequent paraphrasing mechanisms in our corpus,we propose two formalisations. The first one is theoretical, explaining the differentparaphrasing relationships maintained by the paraphrases between each other. Thesecond formalises paraphrase structures as predicate-argument ones. We considerthe latter suitable for paraphrase processing.Finally we have implemented a paraphrase structures extraction system. Thisis a compact operational system for the volume of data within our domain, the aimof which is to provide a concrete example of a possible use of our formalisation
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Ibrahim, Ali 1980. "Extracting paraphrases from aligned corpora." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/16970.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
Includes bibliographical references (leaves 58-60).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
by Ali Ibrahim.
M.Eng.
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Deléger, Louise. "Exploitation de corpus parallèles et comparables pour la détection de correspondances lexicales : application au domaine médical." Paris 6, 2009. http://www.theses.fr/2009PA066400.

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Dans ce travail, nous cherchons à mettre des propriétés des corpus textuels (parallélisme et comparabilité) à profit pour l'Informatique Médicale, en détectant des correspondances lexicales de deux types: des traductions de termes médicaux afin d'enrichir des terminologies; des paraphrases d'expressions spécialisées et grand public dans le but d'aider à rédiger des documents grand public. Une première expérience se base sur des approches éprouvées et un corpus parallèle, et met en place des méthodes d'alignement de corpus. Ceci nous a permis d'obtenir de nouvelles traductions françaises de termes anglais, dont certaines sont maintenant intégrées au thésaurus MeSH. Une seconde expérience examine les possibilités d'exploitation de corpus comparables monolingues. Deux méthodes ont été conçues: une première recherche des paraphrases de nominalisations; la deuxième des paraphrases de composés savants. Diverses paraphrases semblant cohérentes avec l'opposition spécialisé/grand public étudiée ont été obtenues.
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Jacquemin, Bernard. "Construction et interrogation de la structure informationnelle d'une base documentaire en français." Phd thesis, Université de la Sorbonne nouvelle - Paris III, 2003. http://tel.archives-ouvertes.fr/halshs-00003957.

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Cette thèse présente une méthode originale pour identifier et structurer l'information de documents et pour l'interroger. Comme les méthodes linguistiques améliorent les résultats des systèmes actuels, cette approche se base sur des analyses linguistiques et des ressources lexicales. Une analyse grammaticale de haut niveau (morphologique, syntaxique et sémantique) identifie d'abord les éléments d'information et les lie entre eux. Puisque le contexte des requêtes est faible, les textes sont analysés. Puis le contenu des ressources confère aux informations de nombreuses actualisations grâce à des transformations contextuelles : synonymie simple et complexe, dérivations avec adaptation du contexte syntaxique, adjonction de traits sémantiques... Enfin, l'interrogation des textes est testée. Une analyse morpho-syntaxique de la question en identifie les éléments d'information et choisit le type de la réponse attendue. Le fragment de texte contenant ces données constitue la réponse à la question.
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Chen, Yi-Chun, and 陳臆淳. "A Graph-based Automatic Paraphrase Extraction Algorithm." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/73031335025676176134.

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碩士
國立清華大學
資訊工程學系
101
Paraphrases are alternative ways to express the same meaning. Automatically generating paraphrases can be applied in many of National Language Processing tasks. We propose a method for generating paraphrases which preserve the meaning and the syntax of a given phrase. In our approach, the paraphrasing problem is transformed into a graph representing direct and indirect paraphrase relations. The method involves incorporating various linguistically motivated features to reflect the similarities of paraphrase candidates, and using Weighted PageRank Algorithm to evaluate the relevance of paraphrase candidates. Evaluation on a set of phrases commonly used in research articles shows that our method significantly outperforms the state-of-the-art methods under both semantic and syntactic considerations.
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Manuilov, Illia, and S. V. Petrasova. "Method for Paraphrase Extraction from the News Text Corpus." Thesis, 2019. http://repository.kpi.kharkov.ua/handle/KhPI-Press/46384.

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The paper discusses the process of automatic extraction of paraphrases used in rewriting. The researchers propose the method for extracting paraphrases from English news text corpora. The method is based on both the developed syntactic rules to define phrases and synsets to identify synonymous words in the designed text corpus of BBC news. In order to implement the method, Natural Language Toolkit, Universal Dependencies parser and WordNet are used.
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Book chapters on the topic "Paraphrase extraction"

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Pronoza, Ekaterina, Elena Yagunova, and Anton Pronoza. "Construction of a Russian Paraphrase Corpus: Unsupervised Paraphrase Extraction." In Communications in Computer and Information Science, 146–57. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41718-9_8.

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Zhang, Maoyuan, Wang Li, and Hong Zhang. "Paraphrase Collocations Extraction Based on Concept Expansion." In Advances in Intelligent Systems and Computing, 191–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54930-4_19.

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Kumar, Vivek, Abhishek Verma, Namita Mittal, and Sergey V. Gromov. "Anatomy of Preprocessing of Big Data for Monolingual Corpora Paraphrase Extraction: Source Language Sentence Selection." In Advances in Intelligent Systems and Computing, 495–505. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1501-5_43.

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Zhang, Yu, Le Qi, Linjie Wang, Linlin Yu, and Ting Liu. "Beyond Pivot for Extracting Chinese Paraphrases." In Lecture Notes in Computer Science, 132–44. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01012-6_11.

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Nakagawa, Hiroshi, and Hidetaka Masuda. "Extracting Paraphrases of Japanese Action Word of Sentence Ending Part from Web and Mobile News Articles." In Information Retrieval Technology, 94–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31871-2_9.

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Conference papers on the topic "Paraphrase extraction"

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Pavlick, Ellie, Juri Ganitkevitch, Tsz Ping Chan, Xuchen Yao, Benjamin Van Durme, and Chris Callison-Burch. "Domain-Specific Paraphrase Extraction." In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.3115/v1/p15-2010.

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Shinyama, Yusuke, and Satoshi Sekine. "Paraphrase acquisition for information extraction." In the second international workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2003. http://dx.doi.org/10.3115/1118984.1118993.

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Ferguson, James, Colin Lockard, Daniel Weld, and Hannaneh Hajishirzi. "Semi-Supervised Event Extraction with Paraphrase Clusters." In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/n18-2058.

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Yu, Junjie, Tong Zhu, Wenliang Chen, Wei Zhang, and Min Zhang. "Improving Relation Extraction with Relational Paraphrase Sentences." In Proceedings of the 28th International Conference on Computational Linguistics. Stroudsburg, PA, USA: International Committee on Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.coling-main.148.

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Yu, Junjie, Tong Zhu, Wenliang Chen, Wei Zhang, and Min Zhang. "Improving Relation Extraction with Relational Paraphrase Sentences." In Proceedings of the 28th International Conference on Computational Linguistics. Stroudsburg, PA, USA: International Committee on Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.coling-main.148.

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Pronoza, Ekaterina, and Elena Yagunova. "Comparison of sentence similarity measures for Russian paraphrase identification." In 2015 Artificial Intelligence and Natural Language and Information Extraction, Social Media and Web Search FRUCT Conference (AINL-ISMW FRUCT). IEEE, 2015. http://dx.doi.org/10.1109/ainl-ismw-fruct.2015.7382973.

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Shahmohammadi, Hassan, MirHossein Dezfoulian, and Muharram Mansoorizadeh. "An Extensive Comparison of Feature Extraction Methods for Paraphrase Detection." In 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE). IEEE, 2018. http://dx.doi.org/10.1109/iccke.2018.8566303.

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Alvi, Faisal, El-Sayed M. El-Alfy, Wasfi G. Al-Khatib, and Radwan E. Abdel-Aal. "Analysis and extraction of sentence-level paraphrase sub-corpus in CS education." In the 13th annual conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2380552.2380566.

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Park, Hancheol, Gahgene Gweon, and Jeong Heo. "Affix modification-based bilingual pivoting method for paraphrase extraction in agglutinative languages." In 2016 International Conference on Big Data and Smart Computing (BigComp). IEEE, 2016. http://dx.doi.org/10.1109/bigcomp.2016.7425914.

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Wang, Zhuxin, Guo Xie, Jinhua Du, and Yaru Zhang. "A syntactic knowledge constrained paraphrase extraction for Chinese-English statistical machine translation." In 2017 Chinese Automation Congress (CAC). IEEE, 2017. http://dx.doi.org/10.1109/cac.2017.8243697.

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Reports on the topic "Paraphrase extraction"

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Shinyama, Yusuke, and Satoshi Sekine. Paraphrase Acquisition for Information Extraction. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada460236.

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Ibrahim, Ali, Boris Katz, and Jimmy Lin. Extracting Structural Paraphrases from Aligned Monolingual Corpora. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada460577.

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