We introduce a methodology for automating the maintenance of domain-specific taxonomies based on natural language text understanding. A given ontology is incrementally updated as new concepts are acquired from real-world texts. The acquisition process is centered around the linguistic and conceptual “quality” of various forms of evidence underlying the generation and refinement of concept hypotheses. On the basis of the quality of evidence, concept hypotheses are ranked according to credibility and the most credible ones are selected for assimilation into the domain knowledge base.

this paper gives a comprehensive picture of a complex, yet not explicitly spelled-out theory of discourse coherence, the centering model (Grosz, Joshi, and Weinstein, 1983, 1995) marked a major step in clarifying the relationship between attentional states and (local) discourse segment structure. More precisely, the centering model accounts for the interactions between local coherence and preferential choices of referring expressions. It relates differences in coherence (in part) to varying demands on inferences as required by different types of referring expressions, given a particular attentional state of the hearer in a discourse setting (Grosz, Joshi, and Weinstein 1995, 204-205). The claim is made then that the lower the inference load put on the hearer, the more coherent the underlying discourse appears. The centering model as formulated by Grosz, Joshi, and Weinstein (1995) refines the structure of "centers" of discourse, which are conceived as the representational device for the attentional state at the local level of discourse. They distinguish two basic types of centers, which can be assigned to each utterance Ui--a single backward- looking center, Cb(Ui), and a partially ordered set of discourse entities, the forward- looking centers, Cf(Ui). The ordering on Cf is relevant for determining the Cb. It can be viewed as a salience ranking that reflects the assumption that the higher the ranking of a discourse entity in Cf, the more likely it will be mentioned again in the immediately following utterance. Thus, given an adequate ordering of the discourse entities in Cf, the costs of computations necessary to establish local coherence are minimized

We introduce a methodology for automating the maintenance of domain-specific ontologies based on natural language text understanding. A given taxonomy is incrementally updated as new concepts are acquired from real-world texts. The acquisition process is centered around the linguistic and conceptual "quality" of various forms of evidence underlying the generation and refinement of concept hypotheses. On the basis of the quality of evidence, concept hypotheses are ranked according to credibility and the most credible ones are selected for assimilation into the domain knowledge base.

We present an inference-based text understanding methodology for the resolution of functional anaphora in the context of the centering model. A set of heuristic realization constraints is proposed, which incorporate language-independent conceptual criteria (based on the well-formedness and conceptual strength of role chains in a terminological knowledge base) and language-dependent information structure constraints (based on topic/comment or theme/rheme orderings). We state

. We present a hybrid methodology for the resolution of functional anaphora. It incorporates conceptual proximity criteria applied to ontologically well-engineered domain knowledge bases and an approach to centering based on functional topic/comment patterns. We state grammatical predicates for functional anaphora and then turn to the procedural aspects of their evaluation within the framework of an actor-based implementation of a lexically distributed parser. 1 Introduction Text phenomena, e.g., textual forms of pronominal, nominal and functional anaphora, are a particularly challenging issue for the design of natural language parsers, since lacking recognition facilities either result in referentially incohesive or invalid text knowledge representations. At the conceptual level, functional anaphora relates an anaphoric expression to its antecedent by conceptual attributes (or roles) associated with that antecedent (see, e.g., the relation between "Zugriffszeit" (access time) and "S...

We introduce a formal model and a corresponding system architecture for the acquisition of new concepts from real-world natural language texts. Our approach is centered around the linguistic and conceptual "quality" of various forms of evidence underlying the generation and refinement of concept hypotheses. Based on a terminological (meta)reasoning platform, hypotheses are continuously annotated by a stream of linguistic and conceptual evidence, preferentially ranked and, finally, selected according to their overall credibility. We discuss the results of an empirical evaluation study, concentrating on the system's learning rate and learning accuracy. Appeared in: EKAW'97 - Advances in Knowledge Acquisition. Proceedings of the 10th European Knowledge Acquisition Workshop, 1997, Berlin etc: Springer, 1997 An Empirical Evaluation of a System for Text Knowledge Acquisition Udo Hahn & Klemens Schnattinger L F Text Knowledge Engineering Lab, Freiburg University Werthmannplatz 1, D-79085 ...

As a response to the requirements imposed by real-world natural language processing, we argue for a design of natural language grammars and their associated parsers in which declarative knowledge about language structure and procedural knowledge about language use are equally balanced within a strictly object-oriented specication and implementation framework. In particular, we introduce fundamental messagepassing protocols for object-oriented parsing, which include, besides the one for basic dependency parsing, protocols for ambiguity handling, robustness, backtracking, preferential and predictive parsing, as well as textual reference resolution. Based on an empirical evaluation, we also provide reasons for sacricing completeness of the parse in favor of eOEciency gains. 1 2 1. INTRODUCTION Over the past decades the design of natural language grammars and their parsers was almost entirely based on competence considerations (Chomsky, 1965). These hailed pure declarativism (Shieber,...

We argue for a performance-based design of natural language grammars and their associated parsers in order to meet the constraints imposed by real-world NLP. Our approach incorporates declarative and procedural knowledge about language and language use within an object-oriented specification framework. We discuss several messagepassing protocols for parsing and provide reasons for sacrificing completeness of the parse in favor of efficiency based on a preliminary empirical evaluation. 1

This paper explores commonalities and differences between Dachs, a variant of Dependency Grammar, and Lexical-Functional Grammar. Dachs is based on traditional linguistic insights, but on modern mathematical tools, aiming to integrate different knowledge systems (from syntax and semantics) via their coupling to an abstract syntactic primitive, the dependency relation. These knowledge systems correspond rather closely to projections in LFG. We will investigate commonalities arising from the usage of the projection approach in both theories, and point out differences due to the incompatible linguistic premises. The main difference to LFG lies in the motivation and status of the dimensions, and the information coded there. We will argue that LFG confounds dioeerent information in one projection, preventing it to achieve a good separation of alternatives and calling the motivation of the projection into question.

This report explores the space of this problem and develops some steps towards its solutions. Our experience with knowledge representation languages and dynamic hyperlinking of HTML documents using conceptual graphs seems complementary to the multiple source knowledge fusion task [40]. The reasons are that conceptual graphs are based on lexical, hierarchically structured ontologies of semantically related terms and these structures lend themselves to traditional information retrival engines, at least so far as term extraction from text is concerned. Furthermore, conceptual graphs provide for typical and necessary conditions, logical inference and term signatures that aid with disambiguiation of semantic intent. Conceptual graphs also permit knowledge fusion by way of the maximal join operation. Conceptual graphs are not without their research challenges however. Who or what can automatically generate the CGs and how the approach scales to real-world multiple source fusion are open questions. To address the first of these issues. There are two schools of thought on who or what generates the CGs needed for knowledge fusion from multiple sources. The first of these is that index expressions generated by a meta-level information retrival engine called the Hyperindex Browser (the HiB) [5, 6, 7, 4] can be used to automatically generate knowledge annotations as input to an inference engine (such as the one used by our research group's WebKB software [38, 39, 40]). The idea here is that we can efficiently extract and fuse knowledge structures from example documents to construct a knowledge exemplar and that these exemplars are subsequently used to compute the closure of the knowledge base and generate pattern recognition classifiers (see Figure 1.1). Domain background knowled...