Extração de Informação

A Extração de Informação (EI) é desenvolvida com o objetivo de se obter informação estruturada de dados não-estruturados. Os primeiros trabalhos a debruçarem-se sobre o problema remontam à década de 1970, com a aplicação de gramáticas formais e parsers sintáticos para a estruturação de informação em domínios como prontuários médicos e textos jornalísticos. A comunidade científica demonstrou grande interesse pela área nas décadas posteriores devido à sua utilidade prática, seu foco no processamento de dados reais, suas tarefas bem-definidas e a facilidade de mensurar a qualidade dos resultados em comparação com o desempenho humano na mesma tarefa. Assim, nesse capítulo, iniciaremos com um pouco de história da Extração de Informação (EI) e sua evolução para Extração de Informação Aberta, e destacaremos as tarefas de Reconhecimeno de Entidades Nomeadas (REN) e Extração de Relação (ER)

The Generation of Compound Nominals to Represent the Essence of Text The COMMIX System

This thesis concerns the COMMIX system, which automatically extracts information on what a text is about, and generates that information in the highly compacted form of compound nominal expressions. The expressions generated are complex and may include novel terms which do not appear themselves in the input text. From the practical point of view, the work is driven by the need for better representations of content: for representations which are shorter and more concise than would appear in an abstract, yet more informative and representative of the actual aboutness than commonly occurs in indexing expressions and key terms. This additional layer of representation is referred to in this work as pertaining to the essence of a particular text. From a theoretical standpoint, the thesis shows how the compound nominal as a construct can be successfully employed in these highly informative representations. It involves an exploration of the claim that there is sufficient semantic information contained within the standard dictionary glosses for individual words to enable the construction of useful and highly representative novel compound nominal expressions, without recourse to standard syntactic and statistical methods. It shows how a shallow semantic approach to content identification which is based on lexical overlap can produce some very encouraging results. The methodology employed, and described herein, is domain-independent, and does not require the specification of templates with which the input text must comply. In these two respects, the methodology developed in this work avoids two of the most common problems associated with information extraction. As regards the evaluation of this type of work, the thesis introduces and utilises the notion of percentage attainment value, which is used in conjunction with subjects' opinions about the degree to which the aboutness terms succeed in indicating the subject matter of the texts for which they were generated

Extracting pragmatic content from Email.

This research presents results concerning the large scale automatic extraction of pragmatic content from Email, by a system based on a phrase matching approach to Speech Act detection combined with the empirical detection of Speech Act patterns in corpora. The results show that most Speech Acts that occur in such a corpus can be recognized by the approach. This investigation is supported by the analysis of a corpus consisting of 1000 Emails. We describe experimental work to sort a substantial sample of Emails based on their function, which is to say, whether they contain a statement of fact, a request for the recipient to do something, or ask a question. This could be highly desirable functionality for the overburdened Email user, especially if combined with other, more traditional, measures of content relevance and filters based on desirable and undesirable mail sources. We have attempted to apply an lE engine to the extraction of message content located in the message, in part by the use of speech-act detection criteria, e. g. for what it is to be a request for action, under the many possible surface forms that can be used to express that in English, so as to locate the action requested as well as the fact it is a request. The work may have potential practical uses, but here we describe it as the challenge of adapting an IE engine to a somewhat different, task: that of message function detection. The major contributions are: Defining Request Speech Act types. The Request Speech Act is one of the most important functions of an utterance to be recognised, in order to find out the gist of a message. The present work has concentrated on three sub-types of Requests: Requests for Information, Action, and Permission. An algorithm to recognise Speech Acts Patterns found frequently in a domain, together with linguistic rules, make it possible to recognise most of the examples of Requests in the corpus. The results of the evaluation of the system are encouraging and suggest that, in order to avoid long-response time systems, a fast and friendly system is the right approach to implement

Populating knowledge bases with temporal information

Recent progress in information extraction has enabled the automatic construction of large knowledge bases. Knowledge bases contain millions of entities (e.g. persons, organizations, events, etc.), their semantic classes, and facts about them. Knowledge bases have become a great asset for semantic search, entity linking, deep analytics, and question answering. However, a common limitation of current knowledge bases is the poor coverage of temporal knowledge. First of all, so far, knowledge bases have focused on popular events and ignored long tail events such as political scandals, local festivals, or protests. Secondly, they do not cover the textual phrases denoting events and temporal facts at all. The goal of this dissertation, thus, is to automatically populate knowledge bases with this kind of temporal knowledge. The dissertation makes the following contributions to address the afore mentioned limitations. The first contribution is a method for extracting events from news articles. The method reconciles the extracted events into canonicalized representations and organizes them into fine-grained semantic classes. The second contribution is a method for mining the textual phrases denoting the events and facts. The method infers the temporal scopes of these phrases and maps them to a knowledge base. Our experimental evaluations demonstrate that our methods yield high quality output compared to state-of- the-art approaches, and can indeed populate knowledge bases with temporal knowledge.Der Fortschritt in der Informationsextraktion ermöglicht heute das automatischen Erstellen von Wissensbasen. Derartige Wissensbasen enthalten Entitäten wie Personen, Organisationen oder Events sowie Informationen über diese und deren semantische Klasse. Automatisch generierte Wissensbasen bilden eine wesentliche Grundlage für das semantische Suchen, das Verknüpfen von Entitäten, die Textanalyse und für natürlichsprachliche Frage-Antwortsysteme. Eine Schwäche aktueller Wissensbasen ist jedoch die unzureichende Erfassung von temporalen Informationen. Wissenbasen fokussieren in erster Linie auf populäre Events und ignorieren weniger bekannnte Events wie z.B. politische Skandale, lokale Veranstaltungen oder Demonstrationen. Zudem werden Textphrasen zur Bezeichung von Events und temporalen Fakten nicht erfasst. Ziel der vorliegenden Arbeit ist es, Methoden zu entwickeln, die temporales Wissen au- tomatisch in Wissensbasen integrieren. Dazu leistet die Dissertation folgende Beiträge: 1. Die Entwicklung einer Methode zur Extrahierung von Events aus Nachrichtenartikeln sowie deren Darstellung in einer kanonischen Form und ihrer Einordnung in detaillierte semantische Klassen. 2. Die Entwicklung einer Methode zur Gewinnung von Textphrasen, die Events und Fakten in Wissensbasen bezeichnen sowie einer Methode zur Ableitung ihres zeitlichen Verlaufs und ihrer Dauer. Unsere Experimente belegen, dass die von uns entwickelten Methoden zu qualitativ deutlich besseren Ausgabewerten führen als bisherige Verfahren und Wissensbasen tatsächlich um temporales Wissen erweitern können

Automatic extraction of facts, relations, and entities for web-scale knowledge base population

Equipping machines with knowledge, through the construction of machinereadable knowledge bases, presents a key asset for semantic search, machine translation, question answering, and other formidable challenges in artificial intelligence. However, human knowledge predominantly resides in books and other natural language text forms. This means that knowledge bases must be extracted and synthesized from natural language text. When the source of text is the Web, extraction methods must cope with ambiguity, noise, scale, and updates. The goal of this dissertation is to develop knowledge base population methods that address the afore mentioned characteristics of Web text. The dissertation makes three contributions. The first contribution is a method for mining high-quality facts at scale, through distributed constraint reasoning and a pattern representation model that is robust against noisy patterns. The second contribution is a method for mining a large comprehensive collection of relation types beyond those commonly found in existing knowledge bases. The third contribution is a method for extracting facts from dynamic Web sources such as news articles and social media where one of the key challenges is the constant emergence of new entities. All methods have been evaluated through experiments involving Web-scale text collections.Maschinenlesbare Wissensbasen sind ein zentraler Baustein für semantische Suche, maschinelles Übersetzen, automatisches Beantworten von Fragen und andere komplexe Fragestellungen der Künstlichen Intelligenz. Allerdings findet man menschliches Wissen bis dato überwiegend in Büchern und anderen natürlichsprachigen Texten. Das hat zur Folge, dass Wissensbasen durch automatische Extraktion aus Texten erstellt werden müssen. Bei Texten aus dem Web müssen Extraktionsmethoden mit einem hohen Maß an Mehrdeutigkeit und Rauschen sowie mit sehr großen Datenvolumina und häufiger Aktualisierung zurechtkommen. Das Ziel dieser Dissertation ist, Methoden zu entwickeln, die die automatische Erstellung von Wissensbasen unter den zuvor genannten Unwägbarkeiten von Texten aus dem Web ermöglichen. Die Dissertation leistet dazu drei Beiträge. Der erste Beitrag ist ein skalierbar verteiltes Verfahren, das die effiziente Extraktion hochwertiger Fakten unterstützt, indem logische Inferenzen mit robuster Textmustererkennung kombiniert werden. Der zweite Beitrag der Arbeit ist eine Methodik zur automatischen Konstruktion einer umfassenden Sammlung typisierter Relationen, die weit über die in existierenden Wissensbasen bekannten Relationen hinausgeht. Der dritte Beitrag ist ein neuartiges Verfahren zur Extraktion von Fakten aus dynamischen Webinhalten wie Nachrichtenartikeln und sozialen Medien. Insbesondere werden Lösungen vorgestellt zur Erkennung und Registrierung neuer Entitäten, die bislang in keiner Wissenbasis enthalten sind. Alle Verfahren wurden durch umfassende Experimente auf großen Text und Webkorpora evaluiert