Learning unification-based grammars using the Spoken English Corpus

This paper describes a grammar learning system that combines model-based and data-driven learning within a single framework. Our results from learning grammars using the Spoken English Corpus (SEC) suggest that combined model-based and data-driven learning can produce a more plausible grammar than is the case when using either learning style isolation.Comment: 10 page

Hybrid language processing in the Spoken Language Translator

The paper presents an overview of the Spoken Language Translator (SLT) system's hybrid language-processing architecture, focussing on the way in which rule-based and statistical methods are combined to achieve robust and efficient performance within a linguistically motivated framework. In general, we argue that rules are desirable in order to encode domain-independent linguistic constraints and achieve high-quality grammatical output, while corpus-derived statistics are needed if systems are to be efficient and robust; further, that hybrid architectures are superior from the point of view of portability to architectures which only make use of one type of information. We address the topics of ``multi-engine'' strategies for robust translation; robust bottom-up parsing using pruning and grammar specialization; rational development of linguistic rule-sets using balanced domain corpora; and efficient supervised training by interactive disambiguation. All work described is fully implemented in the current version of the SLT-2 system.Comment: 4 pages, uses icassp97.sty; to appear in ICASSP-97; see http://www.cam.sri.com for related materia

Open Microphone Speech Understanding: Correct Discrimination Of In Domain Speech

An ideal spoken dialogue system listens continually and determines which utterances were spoken to it, understands them and responds appropriately while ignoring the rest This paper outlines a simple method for achieving this goal which involves trading a slightly higher false rejection rate of in domain utterances for a higher correct rejection rate of Out of Domain (OOD) utterances. The system recognizes semantic entities specified by a unification grammar which is specialized by Explanation Based Learning (EBL). so that it only uses rules which are seen in the training data. The resulting grammar has probabilities assigned to each construct so that overgeneralizations are not a problem. The resulting system only recognizes utterances which reduce to a valid logical form which has meaning for the system and rejects the rest. A class N-gram grammar has been trained on the same training data. This system gives good recognition performance and offers good Out of Domain discrimination when combined with the semantic analysis. The resulting systems were tested on a Space Station Robot Dialogue Speech Database and a subset of the OGI conversational speech database. Both systems run in real time on a PC laptop and the present performance allows continuous listening with an acceptably low false acceptance rate. This type of open microphone system has been used in the Clarissa procedure reading and navigation spoken dialogue system which is being tested on the International Space Station

Data-Oriented Language Processing. An Overview

During the last few years, a new approach to language processing has started to emerge, which has become known under various labels such as "data-oriented parsing", "corpus-based interpretation", and "tree-bank grammar" (cf. van den Berg et al. 1994; Bod 1992-96; Bod et al. 1996a/b; Bonnema 1996; Charniak 1996a/b; Goodman 1996; Kaplan 1996; Rajman 1995a/b; Scha 1990-92; Sekine & Grishman 1995; Sima'an et al. 1994; Sima'an 1995-96; Tugwell 1995). This approach, which we will call "data-oriented processing" or "DOP", embodies the assumption that human language perception and production works with representations of concrete past language experiences, rather than with abstract linguistic rules. The models that instantiate this approach therefore maintain large corpora of linguistic representations of previously occurring utterances. When processing a new input utterance, analyses of this utterance are constructed by combining fragments from the corpus; the occurrence-frequencies of the fragments are used to estimate which analysis is the most probable one. In this paper we give an in-depth discussion of a data-oriented processing model which employs a corpus of labelled phrase-structure trees. Then we review some other models that instantiate the DOP approach. Many of these models also employ labelled phrase-structure trees, but use different criteria for extracting fragments from the corpus or employ different disambiguation strategies (Bod 1996b; Charniak 1996a/b; Goodman 1996; Rajman 1995a/b; Sekine & Grishman 1995; Sima'an 1995-96); other models use richer formalisms for their corpus annotations (van den Berg et al. 1994; Bod et al., 1996a/b; Bonnema 1996; Kaplan 1996; Tugwell 1995).Comment: 34 pages, Postscrip

Automatic Extraction of Subcategorization from Corpora

We describe a novel technique and implemented system for constructing a subcategorization dictionary from textual corpora. Each dictionary entry encodes the relative frequency of occurrence of a comprehensive set of subcategorization classes for English. An initial experiment, on a sample of 14 verbs which exhibit multiple complementation patterns, demonstrates that the technique achieves accuracy comparable to previous approaches, which are all limited to a highly restricted set of subcategorization classes. We also demonstrate that a subcategorization dictionary built with the system improves the accuracy of a parser by an appreciable amount.Comment: 8 pages; requires aclap.sty. To appear in ANLP-9