Complexity of Lexical Descriptions and its Relevance to Partial Parsing

In this dissertation, we have proposed novel methods for robust parsing that integrate the flexibility of linguistically motivated lexical descriptions with the robustness of statistical techniques. Our thesis is that the computation of linguistic structure can be localized if lexical items are associated with rich descriptions (supertags) that impose complex constraints in a local context. However, increasing the complexity of descriptions makes the number of different descriptions for each lexical item much larger and hence increases the local ambiguity for a parser. This local ambiguity can be resolved by using supertag co-occurrence statistics collected from parsed corpora. We have explored these ideas in the context of Lexicalized Tree-Adjoining Grammar (LTAG) framework wherein supertag disambiguation provides a representation that is an almost parse. We have used the disambiguated supertag sequence in conjunction with a lightweight dependency analyzer to compute noun groups, verb groups, dependency linkages and even partial parses. We have shown that a trigram-based supertagger achieves an accuracy of 92.1‰ on Wall Street Journal (WSJ) texts. Furthermore, we have shown that the lightweight dependency analysis on the output of the supertagger identifies 83‰ of the dependency links accurately. We have exploited the representation of supertags with Explanation-Based Learning to improve parsing effciency. In this approach, parsing in limited domains can be modeled as a Finite-State Transduction. We have implemented such a system for the ATIS domain which improves parsing eciency by a factor of 15. We have used the supertagger in a variety of applications to provide lexical descriptions at an appropriate granularity. In an information retrieval application, we show that the supertag based system performs at higher levels of precision compared to a system based on part-of-speech tags. In an information extraction task, supertags are used in specifying extraction patterns. For language modeling applications, we view supertags as syntactically motivated class labels in a class-based language model. The distinction between recursive and non-recursive supertags is exploited in a sentence simplification application

Towards an automatic speech recognition system for use by deaf students in lectures

According to the Royal National Institute for Deaf people there are nearly 7.5 million hearing-impaired people in Great Britain. Human-operated machine transcription systems, such as Palantype, achieve low word error rates in real-time. The disadvantage is that they are very expensive to use because of the difficulty in training operators, making them impractical for everyday use in higher education. Existing automatic speech recognition systems also achieve low word error rates, the disadvantages being that they work for read speech in a restricted domain. Moving a system to a new domain requires a large amount of relevant data, for training acoustic and language models. The adopted solution makes use of an existing continuous speech phoneme recognition system as a front-end to a word recognition sub-system. The subsystem generates a lattice of word hypotheses using dynamic programming with robust parameter estimation obtained using evolutionary programming. Sentence hypotheses are obtained by parsing the word lattice using a beam search and contributing knowledge consisting of anti-grammar rules, that check the syntactic incorrectness’ of word sequences, and word frequency information. On an unseen spontaneous lecture taken from the Lund Corpus and using a dictionary containing "2637 words, the system achieved 815% words correct with 15% simulated phoneme error, and 73.1% words correct with 25% simulated phoneme error. The system was also evaluated on 113 Wall Street Journal sentences. The achievements of the work are a domain independent method, using the anti- grammar, to reduce the word lattice search space whilst allowing normal spontaneous English to be spoken; a system designed to allow integration with new sources of knowledge, such as semantics or prosody, providing a test-bench for determining the impact of different knowledge upon word lattice parsing without the need for the underlying speech recognition hardware; the robustness of the word lattice generation using parameters that withstand changes in vocabulary and domain

An investigation of grammar design in natural-language speech-recognition.

With the growing interest and demand for human-machine interaction, much work concerning speech-recognition has been carried out over the past three decades. Although a variety of approaches have been proposed to address speech-recognition issues, such as stochastic (statistical) techniques, grammar-based techniques, techniques integrated with linguistic features, and other approaches, recognition accuracy and robustness remain among the major problems that need to be addressed. At the state of the art, most commercial speech products are constructed using grammar-based speech-recognition technology. In this thesis, we investigate a number of features involved in grammar design in natural-language speech-recognition technology. We hypothesize that: with the same domain, a semantic grammar, which directly encodes some semantic constraints into the recognition grammar, achieves better accuracy, but less robustness; a syntactic grammar defines a language with a larger size, thereby it has better robustness, but less accuracy; a word-sequence grammar, which includes neither semantics nor syntax, defines the largest language, therefore, is the most robust, but has very poor recognition accuracy. In this Master\u27s thesis, we claim that proper grammar design can achieve the appropriate compromise between recognition accuracy and robustness. The thesis has been proven by experiments using the IBM Voice-Server SDK, which consists of a VoiceXML browser, IBM ViaVoice Speech Recognition and Text-To-Speech (TTS) engines, sample applications, and other tools for developing and testing VoiceXML applications. The experimental grammars are written in the Java Speech Grammar Format (JSGF), and the testing applications are written in VoiceXML. The tentative experimental results suggest that grammar design is a good area for further study. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .S555. Source: Masters Abstracts International, Volume: 43-01, page: 0244. Adviser: Richard A. Frost. Thesis (M.Sc.)--University of Windsor (Canada), 2004

An investigation of the electrolytic plasma oxidation process for corrosion protection of pure magnesium and magnesium alloy AM50.

In this study, silicate and phosphate EPO coatings were produced on pure magnesium using an AC power source. It was found that the silicate coatings possess good wear resistance, while the phosphate coatings provide better corrosion protection. A Design of Experiment (DOE) technique, the Taguchi method, was used to systematically investigate the effect of the EPO process parameters on the corrosion protection properties of a coated magnesium alloy AM50 using a DC power. The experimental design consisted of four factors (treatment time, current density, and KOH and NaAlO2 concentrations), with three levels of each factor. Potentiodynamic polarization measurements were conducted to determine the corrosion resistance of the coated samples. The optimized processing parameters are 12 minutes, 12 mA/cm2 current density, 0.9 g/l KOH, 15.0 g/l NaAlO2. The results of the percentage contribution of each factor determined by the analysis of variance (ANOVA) imply that the KOH concentration is the most significant factor affecting the corrosion resistance of the coatings, while treatment time is a major factor affecting the thickness of the coatings. (Abstract shortened by UMI.)Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .M323. Source: Masters Abstracts International, Volume: 44-03, page: 1479. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005

Integrating deep and shallow natural language processing components : representations and hybrid architectures

We describe basic concepts and software architectures for the integration of shallow and deep (linguistics-based, semantics-oriented) natural language processing (NLP) components. The main goal of this novel, hybrid integration paradigm is improving robustness of deep processing. After an introduction to constraint-based natural language parsing, we give an overview of typical shallow processing tasks. We introduce XML standoff markup as an additional abstraction layer that eases integration of NLP components, and propose the use of XSLT as a standardized and efficient transformation language for online NLP integration. In the main part of the thesis, we describe our contributions to three hybrid architecture frameworks that make use of these fundamentals. SProUT is a shallow system that uses elements of deep constraint-based processing, namely type hierarchy and typed feature structures. WHITEBOARD is the first hybrid architecture to integrate not only part-of-speech tagging, but also named entity recognition and topological parsing, with deep parsing. Finally, we present Heart of Gold, a middleware architecture that generalizes WHITEBOARD into various dimensions such as configurability, multilinguality and flexible processing strategies. We describe various applications that have been implemented using the hybrid frameworks such as structured named entity recognition, information extraction, creative document authoring support, deep question analysis, as well as evaluations. In WHITEBOARD, e.g., it could be shown that shallow pre-processing increases both coverage and efficiency of deep parsing by a factor of more than two. Heart of Gold not only forms the basis for applications that utilize semanticsoriented natural language analysis, but also constitutes a complex research instrument for experimenting with novel processing strategies combining deep and shallow methods, and eases replication and comparability of results.Diese Arbeit beschreibt Grundlagen und Software-Architekturen für die Integration von flachen mit tiefen (linguistikbasierten und semantikorientierten) Verarbeitungskomponenten für natürliche Sprache. Das Hauptziel dieses neuartigen, hybriden Integrationparadigmas ist die Verbesserung der Robustheit der tiefen Verarbeitung. Nach einer Einführung in constraintbasierte Analyse natürlicher Sprache geben wir einen Überblick über typische Aufgaben flacher Sprachverarbeitungskomponenten. Wir führen XML Standoff-Markup als zusätzliche Abstraktionsebene ein, mit deren Hilfe sich Sprachverarbeitungskomponenten einfacher integrieren lassen. Ferner schlagen wir XSLT als standardisierte und effiziente Transformationssprache für die Online-Integration vor. Im Hauptteil der Arbeit stellen wir unsere Beiträge zu drei hybriden Architekturen vor, welche auf den beschriebenen Grundlagen aufbauen. SProUT ist ein flaches System, das Elemente tiefer Verarbeitung wie Typhierarchie und getypte Merkmalsstrukturen nutzt. WHITEBOARD ist das erste System, welches nicht nur Part-of-speech-Tagging, sondern auch Eigennamenerkennung und flaches topologisches Parsing mit tiefer Verarbeitung kombiniert. Schließlich wird Heart of Gold vorgestellt, eine Middleware-Architektur, welche WHITEBOARD hinsichtlich verschiedener Dimensionen wie Konfigurierbarkeit, Mehrsprachigkeit und Unterstützung flexibler Verarbeitungsstrategien generalisiert. Wir beschreiben verschiedene, mit Hilfe der hybriden Architekturen implementierte Anwendungen wie strukturierte Eigennamenerkennung, Informationsextraktion, Kreativitätsunterstützung bei der Dokumenterstellung, tiefe Frageanalyse, sowie Evaluationen. So konnte z.B. in WHITEBOARD gezeigt werden, dass durch flache Vorverarbeitung sowohl Abdeckung als auch Effizienz des tiefen Parsers mehr als verdoppelt werden. Heart of Gold bildet nicht nur Grundlage für semantikorientierte Sprachanwendungen, sondern stellt auch eine wissenschaftliche Experimentierplattform für weitere, neuartige Kombinationsstrategien dar, welche zudem die Replizierbarkeit und Vergleichbarkeit von Ergebnissen erleichtert

Extracting Information from Spoken User Input:A Machine Learning Approach

We propose a module that performs automatic analysis of user input in spoken dialogue systems using machine learning algorithms. The input to the module is material received from the speech recogniser and the dialogue manager of the spoken dialogue system, the output is a four-level pragmatic-semantic representation of the user utterance. Our investigation shows that when the four interpretation levels are combined in a complex machine learning task, the performance of the module is significantly better than the score of an informed baseline strategy. However, via a systematic, automatised search for the optimal subtask combinations we can gain substantial improvement produced by both classifiers for all four interpretation subtasks. A case study is conducted on dialogues between an automatised, experimental system that gives information on the phone about train connections in the Netherlands, and its users who speak in Dutch. We find that drawing on unsophisticated, potentially noisy features that characterise the dialogue situation, and by performing automatic optimisation of the formulated machine learning task it is possible to extract sophisticated information of practical pragmatic-semantic value from spoken user input with robust performance. This means that our module can with a good score interpret whether the user of the system is giving slot-filling information, and for which query slots (e.g., departure station, departure time, etc.), whether the user gave a positive or a negative answer to the system, or whether the user signals that there are problems in the interaction.

Proceedings

Proceedings of the Ninth International Workshop on Treebanks and Linguistic Theories. Editors: Markus Dickinson, Kaili Müürisep and Marco Passarotti. NEALT Proceedings Series, Vol. 9 (2010), 268 pages. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15891

Hybrid discourse modeling and summarization for a speech-to-speech translation system

The thesis discusses two parts of the speech-to-speech translation system VerbMobil: the dialogue model and one of its applications, multilingual summary generation. In connection with the dialogue model, two topics are of special interest: (a) the use of a default unification operation called overlay as the fundamental operation for dialogue management; and (b) an intentional model that is able to describe intentions in dialogue on five levels in a language-independent way. Besides the actual generation algorithm developed, we present a comprehensive evaluation of the summarization functionality. In addition to precision and recall, a new characterization - confabulation - is defined that provides a more precise understanding of the performance of complex natural language processing systems.Die vorliegende Arbeit behandelt hauptsächlich zwei Themen, die für das VerbMobil-System, ein Übersetzungssystem gesprochener Spontansprache, entwickelt wurden: das Dialogmodell und als Applikation die multilinguale Generierung von Ergebnissprotokollen. Für die Dialogmodellierung sind zwei Themen von besonderem Interesse. Das erste behandelt eine in der vorliegenden Arbeit formalisierte Default-Unifikations-Operation namens Overlay, die als fundamentale Operation für Diskursverarbeitung dient. Das zweite besteht aus einem intentionalen Modell, das Intentionen eines Dialogs auf fünf Ebenen in einer sprachunabhängigen Repräsentation darstellt. Neben dem für die Protokollgenerierung entwickelten Generierungsalgorithmus wird eine umfassende Evaluation zur Protokollgenerierungsfunktionalität vorgestellt. Zusätzlich zu "precision" und "recall" wird ein neues Maß - Konfabulation (Engl.: "confabulation") - vorgestellt, das eine präzisere Charakterisierung der Qualität eines komplexen Sprachverarbeitungssystems ermöglicht

Can humain association norm evaluate latent semantic analysis?

This paper presents the comparison of word association norm created by a psycholinguistic experiment to association lists generated by algorithms operating on text corpora. We compare lists generated by Church and Hanks algorithm and lists generated by LSA algorithm. An argument is presented on how those automatically generated lists reflect real semantic relations