Frederico Luiz Gonçalves de Freitas

A multi-document summarization system based on statistics and linguistic treatment

The massive quantity of data available today in the Internet has reached such a huge volume that it has become humanly unfeasible to efficiently sieve useful information from it. One solution to this problem is offered by using text summarization techniques. Text summarization, the process of automatically creating a shorter version of one or more text documents, is an important way of finding relevant information in large text libraries or in the Internet. This paper presents a multi-document summarization system that concisely extracts the main aspects of a set of documents, trying to avoid the typical problems of this type of summarization: information redundancy and diversity. Such a purpose is achieved through a new sentence clustering algorithm based on a graph model that makes use of statistic similarities and linguistic treatment. The DUC 2002 dataset was used to assess the performance of the proposed system, surpassing DUC competitors by a 50% margin of f-measure, in the best case.

A Context Based Text Summarization System

Text summarization is the process of creating a shorter version of one or more text documents. Automatic text summarization has become an important way of finding relevant information in large text libraries or in the Internet. Extractive text summarization techniques select entire sentences from documents according to some criteria to form a summary. Sentence scoring is the technique most used for extractive text summarization, today. Depending on the context, however, some techniques may yield better results than some others. This paper advocates the thesis that the quality of the summary obtained with combinations of sentence scoring methods depend on text subject. Such hypothesis is evaluated using three different contexts: news, blogs and articles. The results obtained show the validity of the hypothesis formulated and point at which techniques are more effective in each of those contexts studied.

A Four Dimension Graph Model for Automatic Text Summarization

Text summarization is the process of automatically creating a shorter version of one or more text documents. In this context, word-based, sentence-based and graph-based methods approaches are largely used. Among these, graph based methods for automatic text summarization produce summaries based on the relationships between sentences. These relationships may also support the creation of several text processing applications such as extractive and abstractive summaries, question-answering and information retrieval systems, among others. A new graph model for text processing applications is proposed in this paper. It relies on four dimensions (similarity, semantic similarity, co reference, discourse information) to create the graph. The rationale behind the proposal presented here is resorting to more dimensions than previous works, and taking into account co reference resolution, taking into account to the role of pronouns in connecting the sentences. Co reference was not used in any previous graph based summarization technique. An experiment was performed using the Text Rank algorithm with the presented approach, on the CNN corpus. The results show that the model proposed here outperforms the current approaches both quantitatively and qualitatively.

Ontology Issues and Applications Guest Editors’ Introduction

Fred Freitas Centro de Informatica Universidade Federal de Pernambuco Caixa Postal 7851 Cidade Universitaria 50732-970 Recife, PE, Brazil fred@cin.ufpe.br Heiner Stuckenschmidt Department of Mathematics and Computer Science Vrije Universiteit Amsterdam De Boelelaan 1081a 1081 HV Amsterdam, The Netherlands heiner@cs.vu.nl Natalya F. Noy Stanford Medical Informatics 251 Campus Drive Stanford, CA 94305-5479, USA noy@stanford.edu

Agent and ontology based information gathering on restricted web domains with AGATHE

Due to Web size and diversity of information, relevant information gathering on the Web is a very complex task. The main problem with most information retrieval approaches is neglecting the context of the pages, mainly because search engines are based on keyword indexing. Considering restricted domain, the policy of taking into account context may lead to more relevant information gathering in this paper, a specific cooperative information gathering approach based on the use of software agents and ontologies is proposed. To implement this approach, a generic software architecture, named AGATHE system, based on early prototype, the MASTER-Web system, permitting development of specific restricted-domain information gathering systems is presented in detail, with a focus on the extraction subsystem.

A New Sentence Similarity Method Based on a Three-Layer Sentence Representation

Sentence similarity methods are used to assess the degree of likelihood between phrases. Many natural language applications such as text summarization, information retrieval, text categorization, and machine translation employ measures of sentence similarity. The existing approaches for this problem represent sentences as vectors of bag of words or the syntactic information of the words in the phrase. The likelihood between phrases is calculated by composing the similarity between the words in the sentences. Such schemes do not address two important concerns in the area, however: the semantic problem and the word order. This paper proposes a new sentence similarity measure that attempts to address such problems by taking into account the lexical, syntactic, and semantic analysis of sentences. The new similarity measure proposed outperforms the state of the art systems in around 6%, when tested using a standard and publically available dataset.

A Confidence–Weighted Metric for Unsupervised Ontology Population from Web Texts

Knowledge engineers have had difficulty in automatically constructing and populating domain ontologies, mainly due to the well-known knowledge acquisition bottleneck. In this paper, we attempt to alleviate this problem by proposing an unsupervised approach for extracting class instances using the web as a big corpus and exploring linguistic patterns to identify and extract ontological class instances. The prototype implementation uses shallow syntactic parsing for disambiguation issues. In addition, we propose a confidence-weighted metric based on different versions of the classical PMI metric, WordNet similarity measures, and heuristics to calculate the final confidence score that can altogether improve the ranking of candidate instances retrieved by the system. We conducted preliminary experiments comparing the proposed confidence metric against some versions of the PMI metric. We obtained promising results for the final ranking of the candidate instances, achieving a gain in precision up to 24%.

An ontological approach for simulating legal action in the Brazilian penal code

The applicability of Artificial Intelligence for the Legal Domain has several and non-depleted lines of research. Since Colonization, the use of ambiguity in drafting the Brazilian Legal Documents was a palliative to solve cases involving economic, political and social interests between local authorities with the European Court. Further, when conflicts between norms emerge, only time, specificity and superiority criteria are not enough to break the tie, a second degree level governing what criteria should be used in different situations need to be addressed as well. In face of these tangle legal documents, this research project aims to present the OntoCrime and OntoLegalTask: ontological representations through which one can formalize the Brazilian Penal Law to check norm violation and automate legal reasoning. Finally, an experiment with the drinking-drive law is presented.

AGATHE: An Agent- and Ontology-Based System for Gathering Information about Restricted Web Domains

Due to Web size and diversity of information, relevant information gathering on the Web turns out to be a highly complex task. The main problem with most information retrieval approaches is neglecting pages’ context, given their inner deficiency: search engines are based on keyword indexing which cannot capture context. Considering restricted domains, taking into account contexts may lead to more relevant and accurate information gathering. In the last years, we have conducted research with this hypothesis, and proposed an agent- and ontology-based restricted-domain cooperative information gathering approach accordingly, that permit the development of specific information gathering systems. In this article, we present this novel approach based on these guiding ideas, and a generic software architecture, named AGATHE, which is a full-fledged scalable multi-agent system.

An adaptive information extraction system based on wrapper induction with POS tagging

Information Extraction (IE) performs two important tasks: identifying certain pieces of information from documents and storing them for future use. This work proposes an adaptive IE system based on Boosted Wrapper Induction (BWI), a supervised wrapper induction algorithm. However, some authors have shown that boosting techniques face difficulties during the processing of natural language texts. This fact became the rationale for coupling Parts-of-Speech tagging with the BWI algorithm in our proposed system. In order to evaluate its performance, several experiments were carried out on three standard corpora. The results obtained suggest that the union of POS tagging and BWI offers a small gain of 3--5% of performance over the original BWI algorithm for unstructured texts. These results position our system among the very best similar IE systems endowed with POS tagging, according to a comparison presented and discussed in the article.