The analysis of spoken language is widely considered to be a more challenging task than the analysis of written text. All of the difficulties of written language can generally be found in spoken language as well. Parsing spontaneous speech must, however, also deal with problems such as speech disfluencies, the looser notion of grammaticality, and the lack of clearly marked sentence boundaries. The contamination of the input with errors of a speech recognizer can further exacerbate these problems. Most natural language parsing algorithms are designed to analyze "clean" grammatical input. Because they reject any input which is found to be ungrammatical in even the slightest way, such parsers are unsuitable for parsing spontaneous speech, where completely grammatical input is the exception more than the rule. This thesis describes GLR*, a parsing system based on Tomita's Generalized LR parsing algorithm, that was designed to be robust to two particular types of extra-grammaticality: noise...

Large practical NLP applications require robust analysis components that can effectively handle input that is disfluent or extra-grammatical. The effectiveness and efficiency of any robust parser are a direct function of three main factors: (1) Flexibility: what types of disfluencies and deviations from the grammar can the parser handle?; (2) Search: How does the parser search the space of possible interpretations, and what techniques are applied to prune the search space?; and (3) Parse Selection and Disambiguation: What methods and resources are used to evaluate and rank potential parses and sub-parses, and how does the parser cope with the extreme levels of ambiguity introduced by its flexibility parameters? In this chapter we describe our investigations on how to balance flexibility and efficiency in the context of two different robust parsers - a GLR parser and a left corner Chart parser - both based on a unification-augmented context-free grammar formalism. We demonstrate how the...

Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 B.2 Portions of the Interlingua Representation . . . . . . . . . . . . . . . . . . . 236 B.3 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 viii List of Tables 4.1 The Three Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 ix List of Figures 1.1 Parse Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.2 Combination Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3 Repair Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.1 Combination Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.1 Sample Partial Parse . . . . . . . . . . . . . . . . . . . . . . . . . . .

. Cumulative error limits the usefulness of context in applications utilizing contextual information. It is especially a problem in spontaneous speech systems where unexpected input, out-of-domain utterances and missing information are hard to fit into the standard structure of the contextual model. In this paper we discuss how our approaches to recognizing speech acts address the problem of cumulative error. We demonstrate the advantage of the proposed approaches over those that do not address the problem of cumulative error. The experiments are conducted in the context of Enthusiast, a large Spanish-to-English speech-to-speech translation system in the appointment scheduling domain [13, 12, 11, 5]. 1 The Cumulative Error Problem To interpret natural language, it is necessary to take context into account. However, taking context into account can also generate new problems, such as those arising because of cumulative error. Cumulative error is introduced when an incorrect hypothesis i...

The goal of the Atlas project is to increase the opportunities for students to construct their own knowledge by conversing (in typed form) with a natural language-based ITS. In this paper we describe two components of Atlas|APE, the integrated planning and execution system at the heart of Atlas, and CARMEL, the natural language understanding component. These components have been designed as domainindependent rule-based software, with the goal of making them both extensible and reusable. We illustrate the use of CARMEL and APE by describing Atlas-Andes, a prototype ITS built with Atlas using the Andes physics tutor as the host.

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Although Minimum Distance Parsing (MDP) offers a theoretically attractive solution to the problem of extragrammaticality, it is often computationally infeasible in large scale practical applications. In this paper we present an alternative approach where the labor is distributed between a more restrictive partial parser and a repair module. Though two stage approaches have grown in popularity in recent years because of their efficiency, they have done so at the cost of requiring hand coded repair heuristics (Ehrlich and Hanrieder, 1996; Danieli and Gerbino, 1995). In contrast, our two stage approach does not require any hand coded knowledge sources dedicated to repair, thus making it possible to achieve a similar run time advantage over MDP without losing the quality of domain independence. 1 Introduction The correct interpretation of spontaneous spoken language poses challenges that continue to fall outside of the reach of state-of-the-art technology. The first essential task of a na...

Introduction AUTOSEM is a lexicon driven approach to semantic interpretation built on top of the LCFlex robust parser (Ros'e and Lavie, to appear). Its domain independent knowledge sources include a broad coverage English syntactic parsing grammar and a large scale lexicon built on top of the COMLEX lexicon [Grishman et al., 1994] and containing both word entries as well as idiomatic construction entries. Domain specific semantic knowledge is encoded declaratively within a meaning representation specification. Semantic constructor functions are compiled automatically from this specification and then linked into lexical entries. Thus, the lexicon provides a clean interface between syntactic and semantic knowledge as in the Glue Language Semantics approach [Dalrymple, 1999, Dalrymple et al., 1993]. The grammar allows the parser to assign deep syntactic roles to constituents. Based on these head/argument relationships, the constructor functions enforce semantic se

This paper presents a brief overview of the bidirectional (Japanese and English) TransferDriven Machine Translation system, currently being developed at ATR. The aim of this development is to achieve bidirectional spoken dialogue translation using a new translation technique, TDMT, in which an example-based framework is fully utilized to translate the whole sentence. Although the translation coverage is presently restricted to conference registration, the system meets requirements for spoken dialogue translation, such as two-way translation, high speed, and high accuracy with robust processing.

This document contains draft information from a section of a preliminary version of a VERBMOBIL deliverable (TP 5.3-P1). It is distributed in this form to assist partners in advance planning. This document contains a simple version of the core DATR inference engine in Prolog in order to illustrate the principles of DATR inference to Prolog programmers. Note that in minor details it departs slightly from DATR conventions: - nonstandard nodenames are permitted; - the knowledge base must be pre-sorted to permit 'longest path first' inference; - queries include the theory name. Note also that this is not a directly usable implementation: there is no user interface, no DATR-Prolog interpreter, no DATR-specific trace or debugging, no attention paide to efficiency, etc. The aim is to provide a minimal 'core DATR standard inference' interpreter in logical style. 1 Illustration of a DATR theory: a 'microlexicon' MINILEX.DTR Tablecloth: !? == Compound !ilex? == lemma !relation? == (for covering) !modifier? == "Table:!?" !head? == "Cloth:!?". Table: !? == Simplex !ilex? == lemma !meaning? == (horizontal surface to put things on) !orthography? == (t a b l e). Cloth: !? == Simplex !ilex? == lemma 32 Dafydd Gibbon !meaning? == (variety of textile) !orthography? == (c l o t h). Compound: !? == Word !ilex? == generalisation !type? == compound !meaning? == ("!head meaning?" "!relation?" "!modifier meaning?") !orthography? == ("!modifier orthography?" "!head orthography?"). Simplex: !? == Word !ilex? == generalisation !type? == simplex. Word: !ilex? == generalisation !type? == word. Theorems: Tablecloth:!relation?=(for covering). Tablecloth:!meaning?=(variety of textile for covering horizontal surface to put things on). Tablecloth:!orthography?=(t a b l e c l o t h). Table:!orthography...