Paraphrasing and Translation

Paraphrasing and translation have previously been treated as unconnected natural lan¬ guage processing tasks. Whereas translation represents the preservation of meaning when an idea is rendered in the words in a different language, paraphrasing represents the preservation of meaning when an idea is expressed using different words in the same language. We show that the two are intimately related. The major contributions of this thesis are as follows:• We define a novel technique for automatically generating paraphrases using bilingual parallel corpora, which are more commonly used as training data for statistical models of translation.• We show that paraphrases can be used to improve the quality of statistical ma¬ chine translation by addressing the problem of coverage and introducing a degree of generalization into the models.• We explore the topic of automatic evaluation of translation quality, and show that the current standard evaluation methodology cannot be guaranteed to correlate with human judgments of translation quality.Whereas previous data-driven approaches to paraphrasing were dependent upon either data sources which were uncommon such as multiple translation of the same source text, or language specific resources such as parsers, our approach is able to harness more widely parallel corpora and can be applied to any language which has a parallel corpus. The technique was evaluated by replacing phrases with their para¬ phrases, and asking judges whether the meaning of the original phrase was retained and whether the resulting sentence remained grammatical. Paraphrases extracted from a parallel corpus with manual alignments are judged to be accurate (both meaningful and grammatical) 75% of the time, retaining the meaning of the original phrase 85% of the time. Using automatic alignments, meaning can be retained at a rate of 70%.Being a language independent and probabilistic approach allows our method to be easily integrated into statistical machine translation. A paraphrase model derived from parallel corpora other than the one used to train the translation model can be used to increase the coverage of statistical machine translation by adding translations of previously unseen words and phrases. If the translation of a word was not learned, but a translation of a synonymous word has been learned, then the word is paraphrased and its paraphrase is translated. Phrases can be treated similarly. Results show that augmenting a state-of-the-art SMT system with paraphrases in this way leads to significantly improved coverage and translation quality. For a training corpus with 10,000 sentence pairs, we increase the coverage of unique test set unigrams from 48% to 90%, with more than half of the newly covered items accurately translated, as opposed to none in current approaches

A semantic framework for textual data enrichment

In this work we present a semantic framework suitable of being used as support tool for recommender systems. Our purpose is to use the semantic information provided by a set of integrated resources to enrich texts by conducting different NLP tasks: WSD, domain classification, semantic similarities and sentiment analysis. After obtaining the textual semantic enrichment we would be able to recommend similar content or even to rate texts according to different dimensions. First of all, we describe the main characteristics of the semantic integrated resources with an exhaustive evaluation. Next, we demonstrate the usefulness of our resource in different NLP tasks and campaigns. Moreover, we present a combination of different NLP approaches that provide enough knowledge for being used as support tool for recommender systems. Finally, we illustrate a case of study with information related to movies and TV series to demonstrate that our framework works properly.This research work has been partially funded by the University of Alicante, Generalitat Valenciana, Spanish Government and the European Commission through the Projects, TIN2015-65136-C2-2- R, TIN2015-65100-R, SAM (FP7-611312), and PROMETEOII/2014/001

Linguistic-based computational treatment of textual entailment recognition

In this thesis, I investigate how lexical resources based on the organisation of lexical knowledge in classes which share common (syntactic, semantic, etc.) features support natural language processing and in particular symbolic recognition of textual entailment. First, I present a robust and wide coverage approach to lexico-structural verb paraphrase recognition based on Levin\u27s (1993) classification of English verbs. Then, I show that by extending Levin\u27s framework to general inference patterns, a classification of English adjectives can be obtained that compared with previous approaches, provides a more fine grained semantic characterisation of their inferential properties. Further, I develop a compositional semantic framework to assign a semantic representation to adjectives based on an ontologically promiscuous approach (Hobbs, 1985) and thereby supporting first order inference for all types of adjectives including extensional ones. Finally, I present a test suite for adjectival inference I developed as a resource for the evaluation of computational systems handling natural language inference.In der vorliegenden Dissertation habe ich untersucht, wie lexikalische Ressourcen, die auf der Gliederung lexikalischen Wissens in Klassen mit gemeinsamen Eigenschaften (lexikalische, semantische etc,) basieren, die computergestützte Verarbeitung natürlicher Sprache und insbesondere die symbolische Erkennung von Entailment unterstützen. Basierend auf Levins (1993) Klassifikation englischer Verben, wurde zuerst ein robuster, für die Verarbeitung beliebiger Texte geeigneter Ansatz zur Paraphrasenerkennung vorgestellt. Dann habe ich aufgezeigt, dass man durch eine Erweiterung von Levins Systematik zur Behandlung allgemeiner Inferenzmuster, eine Klassifikation von englischen Adjektiven erhält, die verglichen mit früheren Ansätzen, eine feinkörnige semantische Charakterisierung ihrer inferentiellen Eigenschaften gestattet und so die Basis für die computergestützte Behandlung von Inferenz bei Adjektiven bildet. Ein anderes beachtliches Ergebnis der vorliegenden Arbeit ist die Test Suite, die ich entwickelt habe und die als Ressource für NPL Anwendungen, die Inferenzen (insbesondere Inferenzen bei Adjektiven) behandeln, genutzt werden kann. Durch die Konstruktion dieser Test Suite beabsichtige ich, den Weg für die Schaffung von Ressourcen zu ebnen, die einen tieferen Einblick in die für Inferenz verantwortlichen Phänomene ermöglichen

Proceedings of the Sixth International Conference Formal Approaches to South Slavic and Balkan languages

Proceedings of the Sixth International Conference Formal Approaches to South Slavic and Balkan Languages publishes 22 papers that were presented at the conference organised in Dubrovnik, Croatia, 25-28 Septembre 2008

Métodos eficientes de deteção de plágio em grandes corpora

O crescente aumento da quantidade de informação publicada na Web, na forma de publicações literárias, científicas e académicas, implica uma constante verificação da integridade de novos documentos (suspeitos) em função dos documentos existentes (fonte). Surge, portanto, a necessidade de aumentar: a eficiência na redução do espaço de procura em grandes conjuntos de documentos fonte; a eficácia na deteção de plágios cada vez mais sofisticados. Nesta dissertação descreve-se uma metodologia baseada em dois atos: (i) indexação do corpus fonte, com um motor de pesquisa (código aberto), e extração de documentos fonte (candidatos), através de pesquisa por palavras relevantes e caraterísticas textuais; (ii) localização de excertos de plágio em documentos suspeitos, com uma métrica robusta, criada através da aplicação de programação genética sobre as caraterísticas de dados plagiados. Os resultados experimentais obtidos mostram uma redução significativa no tempo de processamento, devido à estratificação do corpus, assim como a capacidade de detetar eficientemente excertos de plágio literal, modificado e ofuscado.The increasing information volume published in the Web, either in terms of literary publications or scientific and academic papers, requires a constant surveillance to verify the integrity of daily entering new documents (suspicious), on the basis of the existing ones (sources). As a consequence arises the need to improve the efficiency in reducing the search space for large sets of documents source and the effectiveness in detecting increasingly sophisticated plagiarism events. In this dissertation it is described a methodology based on two actions: (I) indexing the source corpus, with a search engine (open-source), and the extraction of source documents (candidates) by searching for key relevant words and textual features; (II) locating plagiarized passages in suspicious documents with a hybrid metric created by applying genetic programming on the characteristics of plagiarized data. The results show a significant reduction in processing time due to the corpus stratification, as well as a high success rate in detecting plagiarism passages, having none, low, and high obfuscation. The experimental results show a significant reduction in processing time due to stratification of the corpus, as well as the ability to detect plagiarism extracts of diffrent kind: literal, modified and obfuscated

State of New Hampshire. Reports, 1907-1908, volume IV.- Biennial

Sometimes issued both annually and biennially; Each vol. contains the reports of various departments of the government of the state of New Hampshire; Includes attorneys general\u27s opinion

1920 Xavier University Course Catalog

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Xavier University Course Catalog

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1921 May Xavier University Course Catalog - Monthly

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