Paulo Quaresma

A Methodology to Create Legal Ontologies in a Logic Programming Based Web Information Retrieval System

Web legal information retrieval systems need the capability to reason with the knowledge modeled by legal ontologies. Using this knowledge it is possible to represent and to make inferences about the semantic content of legal documents. In this paper a methodology for applying NLP techniques to automatically create a legal ontology is proposed. The ontology is defined in the OWL semantic web language and it is used in a logic programming framework, EVOLP+ISCO, to allow users to query the semantic content of the documents. ISCO allows an easy and efficient integration of declarative, object-oriented and constraint-based programming techniques with the capability to create connections with external databases. EVOLP is a dynamic logic programming framework allowing the definition of rules for actions and events. An application of the proposed methodology to the legal web information retrieval system of the Portuguese Attorney General’s Office is described.

A cooperative approach for composite ontology mapping

This paper proposes a cooperative approach for composite ontology mapping. We first present an extended classification of automated ontology matching and propose an automatic composite solution for the matching problem based on cooperation. In our proposal, agents apply individual mapping algorithms and cooperate in order to change their individual results. We assume that the approaches are complementary to each other and their combination produces better results than the individual ones. Next, we compare our model with three state of the art matching systems. The results are promising specially for what concerns precision and recall. Finally, we propose an argumentation formalism as an extension of our initial model. We compare our argumentation model with the matching systems, showing improvements on the results.

A Preliminary Approach to the Multilabel Classification Problem of Portuguese Juridical Documents

Portuguese juridical documents from Supreme Courts and the Attorney General’s Office are manually classified by juridical experts into a set of classes belonging to a taxonomy of concepts.

Luke, I am Your Father: Dealing with Out-of-Domain Requests by Using Movies Subtitles

Even when the role of a conversational agent is well known users persist in confronting them with Out-of-Domain input. This often results in inappropriate feedback, leaving the user unsatisfied. In this paper we explore the automatic creation/enrichment of conversational agents’ knowledge bases by taking advantage of natural language interactions present in the Web, such as movies subtitles. Thus, we introduce Filipe, a chatbot that answers users’ request by taking advantage of a corpus of turns obtained from movies subtitles (the Subtle corpus). Filipe is based on Say Something Smart, a tool responsible for indexing a corpus of turns and selecting the most appropriate answer, which we fully describe in this paper. Moreover, we show how this corpus of turns can help an existing conversational agent to answer Out-of-Domain interactions. A preliminary evaluation is also presented.

Is linguistic information relevant for the classification of legal texts

Text classification is an important task in the legal domain. In fact, most of the legal information is stored as text in a quite unstructured format and it is important to be able to automatically classify these texts into a predefined set of concepts.Support Vector Machines (SVM), a machine learning algorithm, has shown to be a good classifier for text bases [12]. In this paper, SVMs are applied to the classification of European Portuguese legal texts - the Portuguese Attorney Generals Office Decisions - and the relevance of linguistic information in this domain, namely lemmatisation and part-of-speech tags, is evaluated.The obtained results show that some linguistic information (namely, lemmatisation and the part-of-speech tags) can be successfully used to improve the classification results and, simultaneously, to decrease the number of features needed by the learning algorithm.

A methodology to create legal ontologies in a logic programming information retrieval system

Legal web information retrieval systems need the capability to reason with the knowledge modeled by legal ontologies. Using this knowledge it is possible to represent and to make inferences about the semantic content of legal documents. In this paper a methodology for applying NLP techniques to automatically create a legal ontology is proposed. The ontology is defined in the OWL semantic web language and it is used in a logic programming framework, EVOLP+ISCO, to allow users to query the semantic content of the documents. ISCO allows an easy and efficient integration of declarative, object-oriented and constraint-based programming techniques with the capability to create connections with external databases. EVOLP is a dynamic logic programming framework allowing the definition of rules for actions and events. An application of the proposed methodology to the legal information retrieval system of the Portuguese Attorney Generals Office is described.

Comparison of Different Graph Distance Metrics for Semantic Text Based Classification

Nowadays semantic information of text is used largely for text classification task instead of bag-of-words approaches. This is due to having some limitations of bag of word approaches to represent text appropriately for certain kind of documents. On the other hand, semantic information can be represented through feature vectors or graphs. Among them, graph is normally better than traditional feature vector due to its powerful data structure. However, very few methodologies exist in the literature for semantic representation of graph. Error tolerant graph matching techniques such as graph similarity measures can be utilised for text classification. However, the techniques like Maximum Common Subgraph (mcs) and Minimum Common Supergraph (MCS) for graph similarity measures are computationally NP-hard problem. In the present paper summarized texts are used during extraction of semantic information to make it computationally faster. The semantic information of texts are represented through the discourse representation structures and later transformed into graphs. Five different graph distance measures based on Maximum Common Subgraph (mcs) and Minimum Common Supergraph (MCS) are used with k-NN classifier to evaluate text classification task. The text documents are taken from Reuters21578 text database distributed over 20 classes. Ten documents of each class for both training and testing purpose are used in the present work. From the results, it has been observed that the techniques have more or less equivalent potential to do text classification and as good as traditional bag-of-words approaches.

A logic programming based approach to QA@CLEF05 track

In this paper the methodology followed to build a question-answering system for the Portuguese language is described. The system modules are built using computational linguistic tools such as: a Portuguese parser based on constraint grammars for the syntactic analysis of the documents sentences and the user questions; a semantic interpreter that rewrites sentences syntactic analysis into discourse representation structures in order to obtain the corpus documents and user questions semantic representation; and finally, a semantic/pragmatic interpreter in order to obtain a knowledge base with facts extracted from the documents using ontologies (general and domain specific) and logic inference. This article includes the system evaluation under the CLEF’05 question and answering track.

A Methodology to Create Ontology-Based Information Retrieval Systems

Modern information retrieval systems need the capability to reason about the knowledge conveyed by text bases.

Evaluating Quality of Health Information Sources

The Web is an important source for people who are seeking healthcare information. Users, who search for health information online, do so without professional guidance. A major problem faced is the possibility that poor information has detrimental effects on health. In this sense, the main goal of this work is to provide a framework that evaluates health web page designed especially for common users, those that may lack sufficient knowledge to validate health content. In order to achieve this goal, we proposed a new methodology to calculate a Trust rank based on reputation and a set of quality indicators. The proposed methodology has shown effective to evaluate the quality of health information sources.