David Stallard

A Fully Statistical Approach to Natural Language Interfaces

We present a natural language interface system which is based entirely on trained statistical models. The system consists of three stages of processing: parsing, semantic interpretation, and discourse. Each of these stages is modeled as a statistical process. The models are fully integrated, resulting in an end-to-end system that maps input utterances into meaning representation frames.

Language understanding using hidden understanding models

Describes a sentence understanding system that is completely based on learned methods both for understanding individual sentences and for determining their meaning in the context of the preceding sentences. We describe the models used for each of three stages in the understanding: semantic parsing, semantic classification and discourse modeling. When we ran this system on the December 1994 test of the ARPA Air Travel Information System (ATIS) task, we achieved a 14.5% error rate. The error rate for those sentences that are context-independent (class A) was 9.5%.

A COMPUTATIONAL MECHANISM FOR PRONOMINAL REFERENCE

This paper describes an implemented mechanism for handling bound anaphora, disjoint reference, and pronominal reference. The algorith maps over every node in a parse tree in a left-to-right, depth first manner. Forward and backwards coreference, and disjoint reference are assigned during this tree walk. A semantic interpretation procedure is used to deal with multiple antecedents.

The BBN/HARC spoken language understanding system

The design and performance of a complete spoken language understanding system under development at BBN are described. The system, dubbed HARC (Hear And Respond to Continuous speech), successfully integrates state-of-the-art speech recognition and natural language understanding subsystems. The system has been tested extensively on a restricted airline travel information (ATIS) domain with a vocabulary of about 2000 words. HARC is implemented in portable, high-level software that runs in real time on todays workstations to support interactive online human-machine dialogs. No special-purpose hardware is required other than an A/D (analog-to-digital) converter to digitize the speech. The system works well for any native speaker of American English and does not require any enrollment data from the users. Results of formal DARPA tests in Feb. and Nov. 1992 are presented.<<ETX>>

Hidden understanding models for statistical sentence understanding

We describe the first sentence understanding system that is completely based on learned methods both for understanding individual sentences, and determining their meaning in the context of preceding sentences. We divide the problem into three stages: semantic parsing, semantic classification, and discourse modeling. Each of these stages requires a different model. When we ran this system on the last test (December, 1994) of the ARPA Air Travel Information System (ATIS) task, we achieved a 13.7% error rate. The error rate for those sentences that are context-independent (class A) was 9.7%.

Fragment processing in the DELPHI system

This paper presents the fallback understanding component of BBNs DELPHI NL sysystem. This component is invoked when the core DELPHI system is unable to understand an input. It incorporates both syntax- and frame-based fragment combination sub-components, in an attempt to provide a smoother path from accurate but fragile conventional parsers on the one hand to the robust but less accurate schema-based methods on the other. The frame-based sub-component is fully integrated with the DELPHIs core grammar and parser, and represents an advance over previous proposals.The complete fallback understanding component, incorporating both sub-components, was used in the February 1992 NL and SLS evaluations of the DELPHI system and we report on its contribution to these results, and those of its two separate sub-components. For SLS, use of the frame-based sub-component alone resulted in a figure 39.2% Weighted Error---signifigantly lower than our lowest official score of 43.7% Weighted Error.

Building and Using a Knowledge Graph to Combat Human Trafficking

There is a huge amount of data spread across the web and stored in databases that we can use to build knowledge graphs. However, exploiting this data to build knowledge graphs is difficult due to the heterogeneity of the sources, scale of the amount of data, and noise in the data. In this paper we present an approach to building knowledge graphs by exploiting semantic technologies to reconcile the data continuously crawled from diverse sources, to scale to billions of triples extracted from the crawled content, and to support interactive queries on the data. We applied our approach, implemented in the DIG system, to the problem of combating human trafficking and deployed it to six law enforcement agencies and several non-governmental organizations to assist them with finding traffickers and helping victims.

Batch-mode semi-supervised active learning for statistical machine translation

The development of high-performance statistical machine translation (SMT) systems is contingent on the availability of substantial, in-domain parallel training corpora. The latter, however, are expensive to produce due to the labor-intensive nature of manual translation. We propose to alleviate this problem with a novel, semi-supervised, batch-mode active learning strategy that attempts to maximize in-domain coverage by selecting sentences, which represent a balance between domain match, translation difficulty, and batch diversity. Simulation experiments on an English-to-Pashto translation task show that the proposed strategy not only outperforms the random selection baseline, but also traditional active selection techniques based on dissimilarity to existing training data.

Syntactic and semantic knowledge in the DELPHI unification grammar

This paper presents recent natural language work on HARC, the BBN Spoken Language System. The HARC system incorporates the Byblos system [6] as its speech recognition component and the natural language system Delphi, which consists of a bottom-up parser paired with an integrated syntax/semantics unification grammar, a discourse module, and a database question-answering backend. The paper focuses on the syntactic and semantic analyses made in the grammar.

THE LOGICAL ANALYSIS OF LEXICAL AMBIGUITY

Theories of semantic interpretation which wish to capture as many generalizations as possible must face up to the manifoldly ambiguous and contextually dependent nature of word meaning. In this paper I present a two-level scheme of semantic interpretation in which the first level deals with the semantic consequences of syntactic structure and the second with the choice of word meaning. On the first level the meanings of ambiguous words, pronominal references, nominal compounds and metonomies are not treated as fixed, but are instead represented by free variables which range over predicates and functions. The context-dependence of lexical meaning is dealt with by the second level, a constraint propagation process which attempts to assign values to these variables on the basis of the logical coherence of the overall result. In so doing it makes use of a set of polysemy operators which map between lexical senses, thus making a potentially indefinite number of related senses available.