Exploiting World Knowledge inDiscourse Processing – A Comparison of Feature -Based and Inference-Based Approaches–

Tohoku University乾健太郎課

Multi-agent system for consumer-oriented electronic commerce

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.With the advent of the information superhighway and the exponential growth of the Internet usage, the importance of multi-agent systems is proliferating. The central theme of this thesis is to demonstrate the benefits of adopting multi-agent system (MAS) paradigm to implement consumer oriented electronic commerce system. The discipline of computational science is exploited to provide insights into the behaviour of a model of consumer behaviour that reflect the cognitive notion that the thesis has developed. For this, a multi-agent system computational environment is used to model and investigate the consumer purchase over the Internet. The MAS is developed based on a presented taxonomy, that is most relevant to the thesis application. The thesis also presents a novel approach to negotiation. Results of empirical evaluations provide a strong support that agents using the proposed approach would achieve higher payoff than human subjects. An empirical evaluation for the usability of the prototype system is also presented. Reported results are very encouraging to implement a fieldable system. To complement the perspective for a complete consumer-oriented EC system, the thesis addresses and develops approaches for searching and extracting relevant information. Example experiments are also reported to act as indicators for the effectiveness of the developed approaches

Knowledge acquisition for coreference resolution

Diese Arbeit befasst sich mit dem Problem der statistischen Koreferenzauflösung. Theoretische Studien bezeichnen Koreferenz als ein vielseitiges linguistisches Phänomen, das von verschiedenen Faktoren beeinflusst wird. Moderne statistiche Algorithmen dagegen basieren sich typischerweise auf einfache wissensarme Modelle. Ziel dieser Arbeit ist das Schließen der Lücke zwischen Theorie und Praxis. Ausgehend von den Erkentnissen der theoretischen Studien erfolgt die Bestimmung der linguistischen Faktoren die fuer die Koreferenz besonders relevant erscheinen. Unterschiedliche Informationsquellen werden betrachtet: von der Oberflächenübereinstimmung bis zu den tieferen syntaktischen, semantischen und pragmatischen Merkmalen. Die Präzision der untersuchten Faktoren wird mit korpus-basierten Methoden evaluiert. Die Ergebnisse beweisen, dass die Koreferenz mit den linguistischen, in den theoretischen Studien eingebrachten Merkmalen interagiert. Die Arbeit zeigt aber auch, dass die Abdeckung der untersuchten theoretischen Aussagen verbessert werden kann. Die Merkmale stellen die Grundlage für den Aufbau eines einerseits linguistisch gesehen reichen andererseits auf dem Machinellen Lerner basierten, d.h. eines flexiblen und robusten Systems zur Koreferenzauflösung. Die aufgestellten Untersuchungen weisen darauf hin dass das wissensreiche Model erfolgversprechende Leistung zeigt und im Vergleich mit den Algorithmen, die sich auf eine einzelne Informationsquelle verlassen, sowie mit anderen existierenden Anwendungen herausragt. Das System erreicht einen F-wert von 65.4% auf dem MUC-7 Korpus. In den bereits veröffentlichen Studien ist kein besseres Ergebnis verzeichnet. Die Lernkurven zeigen keine Konvergenzzeichen. Somit kann der Ansatz eine gute Basis fuer weitere Experimente bilden: eine noch bessere Leistung kann dadurch erreicht werden, dass man entweder mehr Texte annotiert oder die bereits existierende Daten effizienter einsetzt. Diese Arbeit beweist, dass statistiche Algorithmen fuer Koreferenzauflösung stark von den theoretischen linguistischen Studien profitiern können und sollen: auch unvollständige Informationen, die automatische fehleranfällige Sprachmodule liefern, können die Leistung der Anwendung signifikant verbessern.This thesis addresses the problem of statistical coreference resolution. Theoretical studies describe coreference as a complex linguistic phenomenon, affected by various different factors. State-of-the-art statistical approaches, on the contrary, rely on rather simple knowledge-poor modeling. This thesis aims at bridging the gap between the theory and the practice. We use insights from linguistic theory to identify relevant linguistic parameters of co-referring descriptions. We consider different types of information, from the most shallow name-matching measures to deeper syntactic, semantic, and discourse knowledge. We empirically assess the validity of the investigated theoretic predictions for the corpus data. Our data-driven evaluation experiments confirm that various linguistic parameters, suggested by theoretical studies, interact with coreference and may therefore provide valuable information for resolution systems. At the same time, our study raises several issues concerning the coverage of theoretic claims. It thus brings feedback to linguistic theory. We use the investigated knowledge sources to build a linguistically informed statistical coreference resolution engine. This framework allows us to combine the flexibility and robustness of a machine learning-based approach with wide variety of data from different levels of linguistic description. Our evaluation experiments with different machine learners show that our linguistically informed model, on the one side, outperforms algorithms, based on a single knowledge source and, on the other side, yields the best result on the MUC-7 data, reported in the literature (F-score of 65.4% with the SVM-light learning algorithm). The learning curves for our classifiers show no signs of convergence. This suggests that our approach makes a good basis for further experimentation: one can obtain even better results by annotating more material or by using the existing data more intelligently. Our study proves that statistical approaches to the coreference resolution task may and should benefit from linguistic theories: even imperfect knowledge, extracted from raw text data with off-the-shelf error-prone NLP modules, helps achieve significant improvements

Satellite Workshop On Language, Artificial Intelligence and Computer Science for Natural Language Processing Applications (LAICS-NLP): Discovery of Meaning from Text

This paper proposes a novel method to disambiguate important words from a collection of documents. The hypothesis that underlies this approach is that there is a minimal set of senses that are significant in characterizing a context. We extend Yarowsky’s one sense per discourse [13] further to a collection of related documents rather than a single document. We perform distributed clustering on a set of features representing each of the top ten categories of documents in the Reuters-21578 dataset. Groups of terms that have a similar term distributional pattern across documents were identified. WordNet-based similarity measurement was then computed for terms within each cluster. An aggregation of the associations in WordNet that was employed to ascertain term similarity within clusters has provided a means of identifying clusters’ root senses

LIPIcs, Volume 261, ICALP 2023, Complete Volume

LIPIcs, Volume 261, ICALP 2023, Complete Volum

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Meaning versus Grammar

This volume investigates the complicated relationship between grammar, computation, and meaning in natural languages. It details conditions under which meaning-driven processing of natural language is feasible, discusses an operational and accessible implementation of the grammatical cycle for Dutch, and offers analyses of a number of further conjectures about constituency and entailment in natural language