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

Using Tag Semantic Network for Keyphrase Extraction in Blogs

Folksonomies provide a comfortable way to search and browse the blogosphere. As the tags in the blogosphere are sparse, ambiguous and too general, this paper proposes both a supervised and an unsupervised approach that extract tags from posts using a tag semantic network. We evaluate the two methods on a blog dataset and observe an improvement in F1-measure from 0.23 to 0.50 when compared to the baseline system

Consensus-based Approach for Keyword Extraction from Urban Events Collections

Automatic keyword extraction (AKE) from textual sources took a valuable step towards harnessing the problem of efficient scanning of large document collections. Particularly in the context of urban mobility, where the most relevant events in the city are advertised on-line, it becomes difficult to know exactly what is happening in a place./nIn this paper we tackle this problem by extracting a set of keywords from different kinds of textual sources, focusing on the urban events context. We propose an ensemble of automatic keyword extraction systems KEA (Key-phrase Extraction Algorithm) and KUSCO (Knowledge Unsupervised Search for instantiating Concepts on lightweight Ontologies) and Conditional Random Fields (CRF)./nUnlike KEA and KUSCO which are well-known tools for automatic keyword extraction, CRF needs further pre-processing. Therefore, a tool for handling AKE from the documents using CRF is developed. The architecture for the AKE ensemble system is designed and efficient integration of component applications is presented in which a consensus between such classifiers is achieved. Finally, we empirically show that our AKE ensemble system significantly succeeds on baseline sources and urban events collections

Knowledge-Based Techniques for Scholarly Data Access: Towards Automatic Curation

Accessing up-to-date and quality scientific literature is a critical preliminary step in any research activity. Identifying relevant scholarly literature for the extents of a given task or application is, however a complex and time consuming activity. Despite the large number of tools developed over the years to support scholars in their literature surveying activity, such as Google Scholar, Microsoft Academic search, and others, the best way to access quality papers remains asking a domain expert who is actively involved in the field and knows research trends and directions. State of the art systems, in fact, either do not allow exploratory search activity, such as identifying the active research directions within a given topic, or do not offer proactive features, such as content recommendation, which are both critical to researchers. To overcome these limitations, we strongly advocate a paradigm shift in the development of scholarly data access tools: moving from traditional information retrieval and filtering tools towards automated agents able to make sense of the textual content of published papers and therefore monitor the state of the art. Building such a system is however a complex task that implies tackling non trivial problems in the fields of Natural Language Processing, Big Data Analysis, User Modelling, and Information Filtering. In this work, we introduce the concept of Automatic Curator System and present its fundamental components.openDottorato di ricerca in InformaticaopenDe Nart, Dari

Concept graphs: Applications to biomedical text categorization and concept extraction

As science advances, the underlying literature grows rapidly providing valuable knowledge mines for researchers and practitioners. The text content that makes up these knowledge collections is often unstructured and, thus, extracting relevant or novel information could be nontrivial and costly. In addition, human knowledge and expertise are being transformed into structured digital information in the form of vocabulary databases and ontologies. These knowledge bases hold substantial hierarchical and semantic relationships of common domain concepts. Consequently, automating learning tasks could be reinforced with those knowledge bases through constructing human-like representations of knowledge. This allows developing algorithms that simulate the human reasoning tasks of content perception, concept identification, and classification. This study explores the representation of text documents using concept graphs that are constructed with the help of a domain ontology. In particular, the target data sets are collections of biomedical text documents, and the domain ontology is a collection of predefined biomedical concepts and relationships among them. The proposed representation preserves those relationships and allows using the structural features of graphs in text mining and learning algorithms. Those features emphasize the significance of the underlying relationship information that exists in the text content behind the interrelated topics and concepts of a text document. The experiments presented in this study include text categorization and concept extraction applied on biomedical data sets. The experimental results demonstrate how the relationships extracted from text and captured in graph structures can be used to improve the performance of the aforementioned applications. The discussed techniques can be used in creating and maintaining digital libraries through enhancing indexing, retrieval, and management of documents as well as in a broad range of domain-specific applications such as drug discovery, hypothesis generation, and the analysis of molecular structures in chemoinformatics

Entity-centric knowledge discovery for idiosyncratic domains

Technical and scientific knowledge is produced at an ever-accelerating pace, leading to increasing issues when trying to automatically organize or process it, e.g., when searching for relevant prior work. Knowledge can today be produced both in unstructured (plain text) and structured (metadata or linked data) forms. However, unstructured content is still themost dominant formused to represent scientific knowledge. In order to facilitate the extraction and discovery of relevant content, new automated and scalable methods for processing, structuring and organizing scientific knowledge are called for. In this context, a number of applications are emerging, ranging fromNamed Entity Recognition (NER) and Entity Linking tools for scientific papers to specific platforms leveraging information extraction techniques to organize scientific knowledge. In this thesis, we tackle the tasks of Entity Recognition, Disambiguation and Linking in idiosyncratic domains with an emphasis on scientific literature. Furthermore, we study the related task of co-reference resolution with a specific focus on named entities. We start by exploring Named Entity Recognition, a task that aims to identify the boundaries of named entities in textual contents. We propose a newmethod to generate candidate named entities based on n-gram collocation statistics and design several entity recognition features to further classify them. In addition, we show how the use of external knowledge bases (either domain-specific like DBLP or generic like DBPedia) can be leveraged to improve the effectiveness of NER for idiosyncratic domains. Subsequently, we move to Entity Disambiguation, which is typically performed after entity recognition in order to link an entity to a knowledge base. We propose novel semi-supervised methods for word disambiguation leveraging the structure of a community-based ontology of scientific concepts. Our approach exploits the graph structure that connects different terms and their definitions to automatically identify the correct sense that was originally picked by the authors of a scientific publication. We then turn to co-reference resolution, a task aiming at identifying entities that appear using various forms throughout the text. We propose an approach to type entities leveraging an inverted index built on top of a knowledge base, and to subsequently re-assign entities based on the semantic relatedness of the introduced types. Finally, we describe an application which goal is to help researchers discover and manage scientific publications. We focus on the problem of selecting relevant tags to organize collections of research papers in that context. We experimentally demonstrate that the use of a community-authored ontology together with information about the position of the concepts in the documents allows to significantly increase the precision of tag selection over standard methods

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