Anaphor resolution and the scope of syntactic constraints

An anaphor resolution algorithm is presented which relies on a combination of strategies for narrowing down and selecting from antecedent sets for re exive pronouns, nonre exive pronouns, and common nouns. The work focuses on syntactic restrictions which are derived from Chomsky's Binding Theory. It is discussed how these constraints can be incorporated adequately in an anaphor resolution algorithm. Moreover, by showing that pragmatic inferences may be necessary, the limits of syntactic restrictions are elucidated

Machine-learning-based vs. manually designed approaches to anaphor resolution: the best of two worlds

In the last years, much effort went into the design of robust anaphor resolution algorithms. Many algorithms are based on antecedent filtering and preference strategies that are manually designed. Along a different line of research, corpus-based approaches have been investigated that employ machine-learning techniques for deriving strategies automatically. Since the knowledge-engineering effort for designing and optimizing the strategies is reduced, the latter approaches are considered particularly attractive. Since, however, the hand-coding of robust antecedent filtering strategies such as syntactic disjoint reference and agreement in person, number, and gender constitutes a once-for-all effort, the question arises whether at all they should be derived automatically. In this paper, it is investigated what might be gained by combining the best of two worlds: designing the universally valid antecedent filtering strategies manually, in a once-for-all fashion, and deriving the (potentially genre-specific) antecedent selection strategies automatically by applying machine-learning techniques. An anaphor resolution system ROSANA-ML, which follows this paradigm, is designed and implemented. Through a series of formal evaluations, it is shown that, while exhibiting additional advantages, ROSANAML reaches a performance level that compares with the performance of its manually designed ancestor ROSANA

Three Algorithms for Competence-Oriented Anaphor Resolution

In the last decade, much effort went into the design of robust third-person pronominal anaphor resolution algorithms. Typical approaches are reported to achieve an accuracy of 60-85%. Recent research addresses the question of how to deal with the remaining difficult-toresolve anaphors. Lappin (2004) proposes a sequenced model of anaphor resolution according to which a cascade of processing modules employing knowledge and inferencing techniques of increasing complexity should be applied. The individual modules should only deal with and, hence, recognize the subset of anaphors for which they are competent. It will be shown that the problem of focusing on the competence cases is equivalent to the problem of giving precision precedence over recall. Three systems for high precision robust knowledge-poor anaphor resolution will be designed and compared: a ruleset-based approach, a salience threshold approach, and a machine-learning-based approach. According to corpus-based evaluation, there is no unique best approach. Which approach scores highest depends upon type of pronominal anaphor as well as upon text genre

Resolving anaphoric references on deficient syntactic descriptions

Syntactic coindexing restrictions are by now known to be of central importance to practical anaphor resolution approaches. Since, in particular due to structural ambiguity, the assumption of the availability of a unique syntactic reading proves to be unrealistic, robust anaphor resolution relies on techniques to overcome this deficiency. In this paper, two approaches are presented which generalize the verification of coindexing constraints to de cient descriptions. At first, a partly heuristic method is described, which has been implemented. Secondly, a provable complete method is specified. It provides the means to exploit the results of anaphor resolution for a further structural disambiguation. By rendering possible a parallel processing model, this method exhibits, in a general sense, a higher degree of robustness. As a practically optimal solution, a combination of the two approaches is suggested

Coreference-Based Summarization and Question Answering: a Case for High Precision Anaphor Resolution

Approaches to Text Summarization and Question Answering are known to benefit from the availability of coreference information. Based on an analysis of its contributions, a more detailed look at coreference processing for these applications will be proposed: it should be considered as a task of anaphor resolution rather than coreference resolution. It will be further argued that high precision approaches to anaphor resolution optimally match the specific requirements. Three such approaches will be described and empirically evaluated, and the implications for Text Summarization and Question Answering will be discussed

Towards Best Practice Standards for Enhanced Knowledge Discovery Systems

Assessing enhanced knowledge discovery systems (eKDSs) constitutes an intricate issue that is understood merely to a certain extent by now. Based upon an analysis of why it is difficult to formally evaluate eKDSs, it is argued for a change of perspective: eKDSs should be understood as intelligent tools for qualitative analysis that support, rather than substitute, the user in the exploration of the data; a qualitative gap will be identified as the main reason why the evaluation of enhanced knowledge discovery systems is difficult. In order to deal with this problem, the construction of a best practice model for eKDSs is advocated. Based on a brief recapitulation of similar work on spoken language dialogue systems, first steps towards achieving this goal are performed, and directions of future research are outlined

Design and enhanced evaluation of a robust anaphor resolution algorithm

Syntactic coindexing restrictions are by now known to be of central importance to practical anaphor resolution approaches. Since, in particular due to structural ambiguity, the assumption of the availability of a unique syntactic reading proves to be unrealistic, robust anaphor resolution relies on techniques to overcome this deficiency. This paper describes the ROSANA approach, which generalizes the verification of coindexing restrictions in order to make it applicable to the deficient syntactic descriptions that are provided by a robust state-of-the-art parser. By a formal evaluation on two corpora that differ with respect to text genre and domain, it is shown that ROSANA achieves high-quality robust coreference resolution. Moreover, by an in-depth analysis, it is proven that the robust implementation of syntactic disjoint reference is nearly optimal. The study reveals that, compared with approaches that rely on shallow preprocessing, the largely nonheuristic disjoint reference algorithmization opens up the possibility/or a slight improvement. Furthermore, it is shown that more significant gains are to be expected elsewhere, particularly from a text-genre-specific choice of preference strategies. The performance study of the ROSANA system crucially rests on an enhanced evaluation methodology for coreference resolution systems, the development of which constitutes the second major contribution o/the paper. As a supplement to the model-theoretic scoring scheme that was developed for the Message Understanding Conference (MUC) evaluations, additional evaluation measures are defined that, on one hand, support the developer of anaphor resolution systems, and, on the other hand, shed light on application aspects of pronoun interpretation

VoiceXML - Technologie der Wahl für telefonbasierte Sprachdialogportale?

Im Zeitalter der ständig wachsenden Mobilitätsanforderungen kommt dem flexiblen, dezentralen Zugriff auf Datenbestände aller Art eine immer größere Bedeutung zu. Steht ein Zugang via Internet nicht zur Verfügung, so bietet sich als Alternative die Verwendung eines Mobiltelefons an. Auf der Grundlage des WAP-Protokolls konnen elementare grafische Zugriffsschnittstellen geschaffen werden; deren Möglichkeiten sind jedoch begrenzt: Im Vergleich zu stationären Computerterminals ist die Displaygröße i.d.R. gering; entsprchend aufwändig verlauft das Browsing. Die gegenwärtige Technologie verfügt über eine geringe Bandbreite. die Navigation über Tasten wird vom Benutzer als umständlich empfunden. Es gibt Einsatzkontexte, die eine tastaturbasierte Interaktion a priori ausschließen. Als Alternative bieten sich gesprochensprachige Schnittstellen an, in denen der Benutzer einen Mensch-Maschine-Dialog mit einem telefonbasierten Sprachportal führt. Die Grundlage derartiger Anwendungen bietet Hardware- bzw. Software-Technologie zu Computer-Telefonie-Integration, Spracherkennung, Sprachsynthese. Mit diesen technologischen Basiskomponenten alleine ist es jedoch noch nicht getan: In Abhängigkeit von den spezifischen Erfordernissen der jeweiligen Anwendung sind geeignete Vorgaben zu spezifizieren, die den Computer in die Lage versetzen, den Dialog mit seinem menschlichen Gegenüber in problemadaquater Weise zu führen. Wichtige Anforderungen sind: Natürlichkeit: Ausgestaltung der sprachlichen Interaktion in einer Weise, die den Erwartungen des Anwenders hinsichtlich des jeweiligen Anwendungsfalls entsprechen; Flexibilität: Anpassung an die Eigenarten des jeweiligen Nutzers (Novize oder geübter Anwender etc.); 2 Robustheit: geeignetes Handling von Missverständnissen, unvollständigem Benutzer-Input sowie Unzulänglichkeiten der maschinellen Sprachverarbeitung (insbesondere Fehler in der Spracherkennung) etc. Formale Spezifikationen des maschinellen Dialogverhaltens werden als Dialogmodelle bezeichnet. Hinsichtlich der generischen Wiederverwendbarkeit der Dialogsoftware ist es sinnvoll, derartige Beschreibungen in einem standardisierten Formalismus, einer Dialogmodellierungssprache abzufassen, die sich somit in erster Näherung als eine "Programmiersprache" für eine generische Dialogmaschine auffassen lässt. Folglich stellt sich die Frage, wie eine geeignete Dialogmodellierungssprache aussehen könnte. In Bezug auf webbasierte Sprachportale wurde vom W3C die XML-basierte Dialogmodellierungssprache VoiceXML als Standardisierungsvorschlag erarbeitet ([7]). Im vorliegenden Dokument sollen zunächst Reichweite und Grenzen der Sprache VoiceXML evaluiert werden. Auf der Grundlage der Evaluation sollen strategischen Empfehlungen fur Unternehmen abgeleitet werden, die sich als Anwendungsentwickler auf dem Innovationsmarkt der telefonbasierten Sprachportale betätigen wollen. Die zentralen Fragen lauten: 1. Welches sind die zentralen Probleme der Entwicklung telefonbasierter Sprachportale? 2. Inwieweit löst VoiceXML diese Probleme? 3. Inwiefern lohnt es sich somit, (z.B. zwecks Herausbildung eines Alleinstellungsmerkmals) auf die Technologie VoiceXML zu setzen? 4. Welche Alternativen existieren? In welchen anderen Bereichen sollte man ggf. Kernkompetenzen herausbilden

A machine learning approach to preference strategies for anaphor resolution

In the last years, much effort went into the design of robust anaphor resolution algorithms. Many algorithms are based on antecedent filtering and preference strategies that are manually designed. Along a different line of research, corpus-based approaches have been investigated that employ machine-learning techniques for deriving strategies automatically. Since the knowledge-engineering effort for designing and optimizing the strategies is reduced, the latter approaches are considered particularly attractive. Since, however, the hand-coding of robust antecedent filtering strategies such as syntactic disjoint reference and agreement in person, number, and gender constitutes a once-for-all effort, the question arises whether at all they should be derived automatically. In this paper, it is investigated what might be gained by combining the best of two worlds: designing the universally valid antecedent filtering strategies manually, in a once-for-all fashion, and deriving the (potentially genre-specific) antecedent selection strategies automatically by applying machine-learning techniques. An anaphor resolution system ROSANA-ML, which follows this paradigm, is designed and implemented. Through a series of formal evaluations, it is shown that, while exhibiting additional advantages, ROSANAML reaches a performance level that compares with the performance of its manually designed ancestor ROSANA