Catálogo de publicaciones - libros

For knowledge representation and reasoning, there is a need to consider the nature of events because event data describe various features and behaviors of the occurrences of actions and changes in the real world. In this paper, we propose to establish an upper event-ontology in order-sorted logic as an infrastructure for event knowledge bases. Our event ontology contains a classification of event entities (e.g., natural events and artificial events) and event relationships (e.g., causal relations and next-event relations). These ontological characterizations are needed for a theoretical basis of applications such as implementation of event databases, detection of event relationships, and annotation of event data.

An ontological extension on the frames in FrameNet is presented in this paper. The general conceptual relations between frame elements, in conjunction with existing characteristics of this lexical resource, suggest more sophisticated semantic analysis of lexical chains (e.g. predicate chains) exploited in many text understanding applications. In particular, we have investigated its benefit for meaning-aware question answering when combined with an inference strategy. The proposed knowledge representation mechanism on the frame elements of FrameNet has been shown to have an impact on answering natural language questions on the basis of our case analysis.

This paper introduces an approach that achieves automated data extraction from semi-structured Web pages by clustering. Both HTML tags and the textual features of text tokens are considered for similarity comparison. The first clustering process groups similar text tokens into the same and the second clustering process groups similar data tuples into . A is a strong candidate of a repetitive data region.

This paper proposes an algorithm which can chunk a given sentence into meaningful and coherent segments. The algorithm is based on the assumption that segment boundaries can be identified by analyzing various information-theoretic measures of the part-of-speech (POS) -grams within the sentence. The assumption is supported by a series of experiments using the POS-tagged corpus and Treebank from Academia Sinica. Experimental results show that the combination of different classifiers based on the measures improves the system coverage while maintaining its precision in our evaluation of 10,000 sentences.

Temporal expressions—references to points in time or periods of time—are widespread in text, and their proper interpretation is essential for any natural language processing task that requires the extraction of temporal information. Work on the interpretation of temporal expressions in text has generally been pursued in one of two paradigms: the formal semantics approach, where an attempt is made to provide a well-grounded theoretical basis for the interpretation of these expressions, and the more pragmatically-focused approach represented by the development of the TIMEX2 standard, with its origins in work in information extraction. The former emphasises formal elegance and consistency; the latter emphasises broad coverage for practical applications. In this paper, we report on the development of a framework that attempts to integrate insights from both perspectives, with the aim of achieving broad coverage of the domain in a well-grounded manner from a formal perspective. We focus in particular on the development of a compact notation for representing the semantics of underspecified temporal expressions that enables the component-level evaluation of systems.

This paper describes and compares the use of methods based on N-grams (specifically trigrams and pentagrams), together with five features, to recognise the syntactic and semantic categories of numeral strings representing money, number, date, etc., in texts. The system employs three interpretation processes: word N-grams construction with a tokeniser; rule-based processing of numeral strings; and N-gram-based classification. We extracted numeral strings from 1,111 online newspaper articles. For numeral strings interpretation, we chose 112 (10%) of 1,111 articles to provide unseen test data (1,278 numeral strings), and used the remaining 999 articles to provide 11,525 numeral strings for use in extracting N-gram-based constraints to disambiguate meanings of the numeral strings. The word trigrams method resulted in 83.8% precision, 81.2% recall ratio, and 82.5% in F-measurement ratio. The word pentagrams method resulted in 86.6% precision, 82.9% recall ratio, and 84.7% in F-measurement ratio.

Lexical text correction systems are typically based on a central step: when finding a malformed token in the input text, a set of correction candidates for the token is retrieved from the given background dictionary. In previous work we introduced a method for the selection of correction candidates which is fast and leads to small candidate sets with high recall. As a prerequisite, ground truth data were used to find a set of important substitutions, merges and splits that represent characteristic errors found in the text. This prior knowledge was then used to fine-tune the meaningful selection of correction candidates. Here we show that an appropriate set of possible substitutions, merges and splits for the input text can be retrieved . In the new approach, we compute an error profile of the erroneous input text in a fully automated way, using so-called error dictionaries. From this profile, suitable sets of substitutions, merges and splits are derived. Error profiling with error dictionaries is simple and very fast. As an overall result we obtain an adaptive form of candidate selection which is very efficient, does not need ground truth data and leads to small candidate sets with high recall.

The DORIS project (Dialogue Oriented Roaming Interactive System) aims to develop a spoken dialogue module for an autonomous robotic agent. This paper examines the techniques used by ?, the speech interpretation component of DORIS, to postulate and assess hypotheses regarding the meaning of a spoken utterance. The results of our evaluation are encouraging, yielding good interpretation performance for utterances of different types and lengths.

In this paper, we extend Defeasible Logic (a computationally-oriented non-monotonic logic) in order to deal with temporalised rules. In particular, we extend the logic to cope with durative facts, as well as with delays between the antecedent and the consequent of rules. We showed that the extended temporalised framework is suitable to model different types of causal relations which have been identified by the specialised literature. We also prove that the computational properties of the original logic are still retained by the extended approach.

We provide a conceptual analysis of several kinds of deadlines, represented in Temporal Modal Defeasible Logic. The paper presents a typology of deadlines, based on the following parameters: deontic operator, maintenance or achievement, presence or absence of sanctions, and persistence after the deadline. The deadline types are illustrated by a set of examples.