Exploiting extensible background knowledge for clustering-based automatic keyphrase extraction

Keyphrases are single- or multi-word phrases that are used to describe the essential content of a document. Utilizing an external knowledge source such as WordNet is often used in keyphrase extraction methods to obtain relation information about terms and thus improves the result, but the drawback is that a sole knowledge source is often limited. This problem is identified as the coverage limitation problem. In this paper, we introduce SemCluster, a clustering-based unsupervised keyphrase extraction method that addresses the coverage limitation problem by using an extensible approach that integrates an internal ontology (i.e., WordNet) with other knowledge sources to gain a wider background knowledge. SemCluster is evaluated against three unsupervised methods, TextRank, ExpandRank, and KeyCluster, and under the F1-measure metric. The evaluation results demonstrate that SemCluster has better accuracy and computational efficiency and is more robust when dealing with documents from different domains

Construindo grafos de conhecimento utilizando documentos textuais para análise de literatura científica

Orientador: Julio Cesar dos ReisDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O número de publicações científicas que pesquisadores tem que ler vem aumento nos últimos anos. Consequentemente, dentre várias opções, é difícil para eles identificarem documentos relevantes relacionados aos seus estudos. Ademais, para entender como um campo científico é organizado, e para estudar o seu estado da arte, pesquisadores geralmente se baseiam em artigos de revisão de uma área. Estes artigos podem estar indisponíveis ou desatualizados dependendo do tema estudado. Usualmente, pesquisadores têm que realizar esta árdua tarefa de pesquisa fundamental manualmente. Pesquisas recentes vêm desenvolvendo mecanismos para auxiliar outros pesquisadores a entender como campos científicos são estruturados. Entretanto, estes mecanismos são focados exclusivamente em recomendar artigos relevantes para os pesquisadores ou os auxiliar em entender como um ramo da ciência é organizado ao nível de publicação. Desta forma, estes métodos limitam o entendimento sobre o ramo estudado, não permitindo que interessados estudem os conceitos e relações abstratas que compõe um ramo da ciência e as suas subáreas. Esta dissertação de mestrado propõe um framework para estruturar, analisar, e rastrear a evolução de um campo científico no nível dos seus conceitos. Ela primeiramente estrutura o campo científico como um grafo-de-conhecimento utilizando os seus conceitos como vértices. A seguir, ela automaticamente identifica as principais subáreas do campo estudado, extrai as suas frases-chave, e estuda as suas relações. Nosso framework representa o campo científico em diferentes períodos do tempo. Esta dissertação compara estas representações, e identifica como as subáreas do campo estudado evoluiram no decorrer dos anos. Avaliamos cada etapa do nosso framework representando e analisando dados científicos provenientes de diferentes áreas de conhecimento em casos de uso. Nossas descobertas indicam o sucesso em detectar resultados similares em diferentes casos de uso, indicando que nossa abordagem é aplicável à diferentes domínios da ciência. Esta pesquisa também contribui com uma aplicação com interface web para auxiliar pesquisadores a utilizarem nosso framework de forma gráfica. Ao utilizar nossa aplicação, pesquisadores podem ter uma análise geral de como um campo científico é estruturado e como ele evoluiAbstract: The amount of publications a researcher must absorb has been increasing over the last years. Consequently, among so many options, it is hard for them to identify interesting documents to read related to their studies. Researchers usually search for review articles to understand how a scientific field is organized and to study its state of the art. This option can be unavailable or outdated depending on the studied area. Usually, they have to do such laborious task of background research manually. Recent researches have developed mechanisms to assist researchers in understanding the structure of scientific fields. However, those mechanisms focus on recommending relevant articles to researchers or supporting them in understanding how a scientific field is organized considering documents that belong to it. These methods limit the field understanding, not allowing researchers to study the underlying concepts and relations that compose a scientific field and its sub-areas. This Ms.c. thesis proposes a framework to structure, analyze, and track the evolution of a scientific field at a concept level. Given a set of textual documents as research papers, it first structures a scientific field as a knowledge graph using its detected concepts as vertices. Then, it automatically identifies the field's main sub-areas, extracts their keyphrases, and studies their relations. Our framework enables to represent the scientific field in distinct time-periods. It allows to compare its representations and identify how the field's areas changed over time. We evaluate each step of our framework representing and analyzing scientific data from distinct fields of knowledge in case studies. Our findings indicate the success in detecting the sub-areas based on the generated graph from natural language documents. We observe similar outcomes in the different case studies by indicating our approach applicable to distinct domains. This research also contributes with a web-based software tool that allows researchers to use the proposed framework graphically. By using our application, researchers can have an overview analysis of how a scientific field is structured and how it evolvedMestradoCiência da ComputaçãoMestre em Ciência da Computação2013/08293-7 ; 2017/02325-5FAPESPCAPE

Topic Distiller:distilling semantic topics from documents

Abstract. This thesis details the design and implementation of a system that can find relevant and latent semantic topics from textual documents. The design of this system, named Topic Distiller, is inspired by research conducted on automatic keyphrase extraction and automatic topic labeling, and it employs entity linking and knowledge bases to reduce text documents to their semantic topics. The Topic Distiller is evaluated using methods and datasets used in information retrieval and automatic keyphrase extraction. On top of the common datasets used in the literature three additional datasets are created to evaluate the system. The evaluation reveals that the Topic Distiller is able to find relevant and latent topics from textual documents, beating the state-of-the-art automatic keyphrase methods in performance when used on news articles and social media posts.Semanttisten aiheiden suodattaminen dokumenteista. Tiivistelmä. Tässä diplomityössä tarkastellaan järjestelmää, joka pystyy löytämään tekstistä relevantteja ja piileviä semanttisia aihealueita, sekä kyseisen järjestelmän suunnittelua ja implementaatiota. Tämän Topic Distiller -järjestelmän suunnittelu ammentaa inspiraatiota automaattisen termintunnistamisen ja automaattisen aiheiden nimeämisen tutkimuksesta sekä hyödyntää automaattista semanttista annotointia ja tietämyskantoja tekstin aihealueiden löytämisessä. Topic Distiller -järjestelmän suorituskykyä mitataan hyödyntämällä kirjallisuudessa paljon käytettyjä automaattisen termintunnistamisen evaluontimenetelmiä ja aineistoja. Näiden yleisten aineistojen lisäksi esittelemme kolme uutta aineistoa, jotka on luotu Topic Distiller -järjestelmän arviointia varten. Evaluointi tuo ilmi, että Topic Distiller kykenee löytämään relevantteja ja piileviä aiheita tekstistä. Se päihittää kirjallisuuden viimeisimmät automaattisen termintunnistamisen menetelmät suorituskyvyssä, kun sitä käytetään uutisartikkelien sekä sosiaalisen median julkaisujen analysointiin

Learning to Extract Keyphrases from Text

Many academic journals ask their authors to provide a list of about five to fifteen key words, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a surprisingly wide variety of tasks for which keyphrases are useful, as we discuss in this paper. Recent commercial software, such as Microsoft?s Word 97 and Verity?s Search 97, includes algorithms that automatically extract keyphrases from documents. In this paper, we approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for this task. The third set of experiments examines the performance of GenEx on the task of metadata generation, relative to the performance of Microsoft?s Word 97. The fourth and final set of experiments investigates the performance of GenEx on the task of highlighting, relative to Verity?s Search 97. The experimental results support the claim that a specialized learning algorithm (GenEx) can generate better keyphrases than a general-purpose learning algorithm (C4.5) and the non-learning algorithms that are used in commercial software (Word 97 and Search 97)

Keyphrase Identification Using Minimal Labeled Data with Hierarchical Context and Transfer Learning

Interoperable clinical decision support system (CDSS) rules provide a pathway to interoperability, a well-recognized challenge in health information technology. Building an ontology facilitates creating interoperable CDSS rules, which can be achieved by identifying the keyphrases (KP) from the existing literature. However, KP identification for data labeling requires human expertise, consensus, and contextual understanding. This paper aims to present a semi-supervised KP identification framework using minimal labeled data based on hierarchical attention over the documents and domain adaptation. Our method outperforms the prior neural architectures by learning through synthetic labels for initial training, document-level contextual learning, language modeling, and fine-tuning with limited gold standard label data. To the best of our knowledge, this is the first functional framework for the CDSS sub-domain to identify KPs, which is trained on limited labeled data. It contributes to the general natural language processing (NLP) architectures in areas such as clinical NLP, where manual data labeling is challenging, and light-weighted deep learning models play a role in real-time KP identification as a complementary approach to human experts' effort

Mining the Web for Lexical Knowledge to Improve Keyphrase Extraction: Learning from Labeled and Unlabeled Data.

A journal article is often accompanied by a list of keyphrases, composed of about five to fifteen important words and phrases that capture the article’s main topics. Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task of automatic keyphrase extraction is to select keyphrases from within the text of a given document. Automatic keyphrase extraction makes it feasible to generate keyphrases for the huge number of documents that do not have manually assigned keyphrases. Good performance on this task has been obtained by approaching it as a supervised learning problem. An input document is treated as a set of candidate phrases that must be classified as either keyphrases or non-keyphrases. To classify a candidate phrase as a keyphrase, the most important features (attributes) appear to be the frequency and location of the candidate phrase in the document. Recent work has demonstrated that it is also useful to know the frequency of the candidate phrase as a manually assigned keyphrase for other documents in the same domain as the given document (e.g., the domain of computer science). Unfortunately, this keyphrase-frequency feature is domain-specific (the learning process must be repeated for each new domain) and training-intensive (good performance requires a relatively large number of training documents in the given domain, with manually assigned keyphrases). The aim of the work described here is to remove these limitations. In this paper, I introduce new features that are conceptually related to keyphrase-frequency and I present experiments that show that the new features result in improved keyphrase extraction, although they are neither domain-specific nor training-intensive. The new features are generated by issuing queries to a Web search engine, based on the candidate phrases in the input document. The feature values are calculated from the number of hits for the queries (the number of matching Web pages). In essence, these new features are derived by mining lexical knowledge from a very large collection of unlabeled data, consisting of approximately 350 million Web pages without manually assigned keyphrases