5,071 research outputs found
A Survey of Paraphrasing and Textual Entailment Methods
Paraphrasing methods recognize, generate, or extract phrases, sentences, or
longer natural language expressions that convey almost the same information.
Textual entailment methods, on the other hand, recognize, generate, or extract
pairs of natural language expressions, such that a human who reads (and trusts)
the first element of a pair would most likely infer that the other element is
also true. Paraphrasing can be seen as bidirectional textual entailment and
methods from the two areas are often similar. Both kinds of methods are useful,
at least in principle, in a wide range of natural language processing
applications, including question answering, summarization, text generation, and
machine translation. We summarize key ideas from the two areas by considering
in turn recognition, generation, and extraction methods, also pointing to
prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of
Informatics, Athens University of Economics and Business, Greece, 201
Unsupervised relation extraction for e-learning applications
In this modern era many educational institutes and business organisations are adopting the e-Learning approach as it provides an effective method for educating and testing their students and staff. The continuous development in the area of information technology and increasing use of the internet has resulted in a huge global market and rapid growth for e-Learning. Multiple Choice Tests (MCTs) are a popular form of assessment and are quite frequently used by many e-Learning applications as they are well adapted to assessing factual, conceptual and procedural information. In this thesis, we present an alternative to the lengthy and time-consuming activity of developing MCTs by proposing a Natural Language Processing (NLP) based approach that relies on semantic relations extracted using Information Extraction to automatically generate MCTs. Information Extraction (IE) is an NLP field used to recognise the most important entities present in a text, and the relations between those concepts, regardless of their surface realisations. In IE, text is processed at a semantic level that allows the partial representation of the meaning of a sentence to be produced. IE has two major subtasks: Named Entity Recognition (NER) and Relation Extraction (RE). In this work, we present two unsupervised RE approaches (surface-based and dependency-based). The aim of both approaches is to identify the most important semantic relations in a document without assigning explicit labels to them in order to ensure broad coverage, unrestricted to predefined types of relations. In the surface-based approach, we examined different surface pattern types, each implementing different assumptions about the linguistic expression of semantic relations between named entities while in the dependency-based approach we explored how dependency relations based on dependency trees can be helpful in extracting relations between named entities. Our findings indicate that the presented approaches are capable of achieving high precision rates. Our experiments make use of traditional, manually compiled corpora along with similar corpora automatically collected from the Web. We found that an automatically collected web corpus is still unable to ensure the same level of topic relevance as attained in manually compiled traditional corpora. Comparison between the surface-based and the dependency-based approaches revealed that the dependency-based approach performs better. Our research enabled us to automatically generate questions regarding the important concepts present in a domain by relying on unsupervised relation extraction approaches as extracted semantic relations allow us to identify key information in a sentence. The extracted patterns (semantic relations) are then automatically transformed into questions. In the surface-based approach, questions are automatically generated from sentences matched by the extracted surface-based semantic pattern which relies on a certain set of rules. Conversely, in the dependency-based approach questions are automatically generated by traversing the dependency tree of extracted sentence matched by the dependency-based semantic patterns. The MCQ systems produced from these surface-based and dependency-based semantic patterns were extrinsically evaluated by two domain experts in terms of questions and distractors readability, usefulness of semantic relations, relevance, acceptability of questions and distractors and overall MCQ usability. The evaluation results revealed that the MCQ system based on dependency-based semantic relations performed better than the surface-based one. A major outcome of this work is an integrated system for MCQ generation that has been evaluated by potential end users.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Automatic Summarization
It has now been 50 years since the publication of Luhn’s seminal paper on automatic summarization. During these years the practical need for automatic summarization has become increasingly urgent and numerous papers have been published on the topic. As a result, it has become harder to find a single reference that gives an overview of past efforts or a complete view of summarization tasks and necessary system components. This article attempts to fill this void by providing a comprehensive overview of research in summarization, including the more traditional efforts in sentence extraction as well as the most novel recent approaches for determining important content, for domain and genre specific summarization and for evaluation of summarization. We also discuss the challenges that remain open, in particular the need for language generation and deeper semantic understanding of language that would be necessary for future advances in the field
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Gerando redes de conhecimento a partir de descrições de fenótipos
Orientadores: André Santanchè, Júlio César dos ReisDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Diversos sistemas computacionais usam informações sobre seres vivos, tais como chaves de identificação ¿ artefatos criados por biólogos para identificar espécimes de seres vivos seguindo uma cadeia de questões acerca das suas características observáveis (fenótipos). Tais questões estão em formato de texto livre, por exemplo, "Possui olhos grandes e pre- tos". Contudo, texto livre dificulta a interpretação de informação por máquinas, limitando sua capacidade de realização de tarefas de busca, integração e comparação de termos. Esta dissertação propõe um método para extrair informação a respeito de fenótipos a partir de textos escritos em linguagem natural, colocando-os no formato de Entidade-Qualidade ¿ um formato de dados biológicos para representar estruturas anatômicas (Entidade) e o seu modificador (Qualidade). A proposta permite que Entidades e Qualidades, reconhecidas automaticamente a partir de informação do nível textual, sejam relacionadas com con- ceitos presentes em ontologias de domínio. Ela adota ferramentas de Processamento de Linguagem Natural existentes, bem como contribui com novas técnicas que exploram as características de escrita e estruturação implícitas em textos presentes nas chaves de iden- tificação. A abordagem foi validada utilizando os dados da base FishBase, sobre a qual foram conduzidos experimentos explorando um conjunto de testes anotado manualmente para avaliar a precisão e aplicabilidade do método de extração proposto. Os resultados obtidos mostram os benefícios da técnica e possibilidades de estudos científicos utilizando a rede de conhecimento extraídaAbstract: Several computing systems rely on information about living beings, such as identification keys ¿ artifacts created by biologists to identify specimens following a flow of questions about their observable characters (phenotype). These questions are described in a free- text format, e.g., "big and black eye". Free-texts hamper the automatic information interpretation by machines, limiting their ability to perform search and comparison of terms, as well as integration tasks. This thesis proposes a method to extract phenotypic information from natural language texts from biology legacy information systems, trans- forming them in an Entity-Quality formalism ¿ a format to represent each phenotype character (Entity) and its state (Quality). Our approach aligns automatically recognized Entities and Qualities with domain concepts described in ontologies. It adopts existing Natural Language Processing techniques, adding an extra original step, which exploits intrinsic characteristics of phenotypic descriptions and of the organizational structure of identification keys. The approach was validated over the FishBase data. We conducted extensive experiments based on a manually annotated Gold Standard set to assess the precision and applicability of the proposed extraction method. The obtained results re- veal the feasibility of our technique, its benefits and possibilities of scientific studies using the extracted knowledge networkMestradoCiência da ComputaçãoMestre em Ciência da Computação1406900CAPE
Selecting and Generating Computational Meaning Representations for Short Texts
Language conveys meaning, so natural language processing (NLP) requires representations of meaning. This work addresses two broad questions: (1) What meaning representation should we use? and (2) How can we transform text to our chosen meaning representation? In the first part, we explore different meaning representations (MRs) of short texts, ranging from surface forms to deep-learning-based models. We show the advantages and disadvantages of a variety of MRs for summarization, paraphrase detection, and clustering. In the second part, we use SQL as a running example for an in-depth look at how we can parse text into our chosen MR. We examine the text-to-SQL problem from three perspectives—methodology, systems, and applications—and show how each contributes to a fuller understanding of the task.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143967/1/cfdollak_1.pd
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