WorldCat Identities

Ayuso, Damaris

Overview
Works: 16 works in 28 publications in 1 language and 28 library holdings
Publication Timeline
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Most widely held works by Damaris Ayuso
BBN: Description of the PLUM System as Used for MUC-5( Book )

2 editions published in 1993 in English and held by 2 WorldCat member libraries worldwide

Traditional approaches to the problem of extracting data from texts have emphasized hand-crafted linguistic knowledge. In contrast, BBN's PLUM system (Probabilistic Language Understanding Model) was developed as part of an ARPA-funded research effort on integrating probabilistic language models with more traditional linguistic techniques. Our research and development goals are: * more rapid development of new applications, * the ability to train (and re-train) systems based on user markings of correct and incorrect output, * more accurate selection among interpretations when more than one is found, and * more robust partial interpretation when no complete interpretation can be found. We began this research agenda approximately three years ago. During the past two years, we have evaluated much of our effort in porting our data extraction system (PLUM) to a new language (Japanese) and to two new domains. Three key design features distinguish PLUM: statistical language modeling, learning algorithms and partial understanding. The first key feature is the use of statistical modeling to guide processing. For the version of PLUM used in MUC-5, part of speech information was determined by using well-known Markov modeling techniques embodied in BBN's part-of-speech tagger POST [5]. We also used a correction model, AMED [3], for improving Japanese segmentation and part-of-speech tags assigned by JUMAN. For the microelectronics domain, we used a probabilistic model to help identify the role of a company in a capability (whether it is a developer, user, etc.). Statistical modeling in PLUM contributes to portability, robustness, and trainability. The second key feature is our use of learning algorithms both to obtain the knowledge bases used by PLUM's processing modules and to train the probabilistic algorithms. A third key feature is partial understanding. All components of PLUM are designed to operate on partially interpretable input
BBN PLUM: MUC-4 Test Results and Analysis( Book )

2 editions published in 1992 in English and held by 2 WorldCat member libraries worldwide

Our mid-term to long-term goals in data extraction from text for the next one to three years are to achieve much greater portability to new languages and new domains, greater robustness, and greater scalability. The novel aspect to our approach is the use of learning algorithms and probabilistic models to learn the domain-specific and language. specific knowledge necessary for a new domain and new language. Learning algorithms should contribute to scalability by making it feasible to deal with domains where it would be infeasible to invest sufficient human effort to bring a system up. Probabilistic models can contribute to robustness by allowing for words, constructions, and forms not anticipated ahead of time and by looking for the most likely interpretation in context. We began this research agenda approximately two years ago. During the last twelve months, we have focused much of our effort on porting our data extraction system (PLUM) to a new language (Japanese) and to two new domains. During the next twelve months, we anticipate porting PLUM to two or three additional domains. For any group to participate in MUC is a significant investment. To be consistent with our mid-term and long- term goals, we imposed the following constraints on ourselves in participating in MUC-4: * We would focus our effort on semi-automatically acquired knowledge. * We would minimize effort on handcrafted knowledge, and most generally. * We would minimize MUC-specific effort. Though the three self-imposed constraints meant our overall scores on the objective evaluation were not as high as if we had focused on handtuning and handcrafting the knowledge bases, MUC-4 became a vehicle for evaluating our progress on the long-term goals
BBN's PLUM Probabilistic Language Understanding System( Book )

2 editions published in 1993 in English and held by 2 WorldCat member libraries worldwide

Traditional approaches to the problem of extracting data from texts have emphasized hand-crafted linguistic knowledge In contrast, BBN's PLUM system (Probabilistic Language Understanding Model) was developed as part of an ARPA-funded research effort on integrating probabilistic language models with more traditional linguistic techniques. Our research and development goals are: * Achieving high performance in objective evaluations, such as the Tipster evaluations. * Reducing human effort in porting the natural language algorithms to new domains and to new languages. * Providing technology that is scalable to realistic applications. We began this research agenda approximately three years ago. During the past two years, we have ported our data extraction system (PLUM) to a new language (Japanese) and to two new domains
Out of the Laboratory: A Case Study with the IRUS Natural Language Interface( Book )

2 editions published in 1986 in English and held by 2 WorldCat member libraries worldwide

DARPA's Strategic Computing Program in the application area of Navy Battle Management has provided us several challenges and opportunities in natural language processing research and development. At the beginning of the effort, a set of domain-independent software components, developed through fundamental research efforts dating back as much as seven years, existed. The IRUS software [1] consists of two subsystems: one for linguistic processing and one for adding specifics of the back end. The first subsystem is linguistic in nature, while the second subsystem is not. Linguistic processing includes morphological, syntactic, semantic, and discourse analysis to generate a formula in logic corresponding to the meaning of an English input. The linguistic subsystem is application-independent and also independent of data base interfaces. (This is achieved by factoring all application specifics into the back end processor or into knowledge bases such as dictionary entries and case frame rules, that are domain-specific.) The non-linguistic components convert the logical form to the code necessary for a given underlying system, such as a relational data base
A Proposal for Incremental Dialogue Evaluation( Book )

2 editions published in 1991 in English and held by 2 WorldCat member libraries worldwide

The SLs community has made progress recently toward evaluating SLs systems that deal with dialogu, but there is still considerable work that needs to be done in this area. Our goal is to develop incremental ways to evaluate dialogue processing, not just going from Class Dl (dialogue pairs) to Class D2 (dialogue triples), but measuring aspects of dialogue processing other than length. We present two suggestions; one for extending the common evaluation procedures for dialogues. and one for modifying the scoring metric
BBN: Description of the PLUM System as Used for MUC-3( Book )

2 editions published in 1991 in English and held by 2 WorldCat member libraries worldwide

Traditional approaches to the problem of extracting data from texts have emphasized handcrafted linguistic knowledge. In contrast, BBN's PLUM system (Probabilistic Language Understanding Model) was developed as part of a DARPA-funded research effort on integrating probabilistic language models with more traditional linguistic techniques. Our research and development goals are * more rapid development of new applications, * the ability to train (and re-train) systems based on user markings of correct and incorrect output, * more accurate selection among interpretations when more than one is found, and * more robust partial interpretation when no complete interpretation can be found. We have previously performed experiments on components of the system with texts from the Wall Street Journal, however, the MUC-3 task is the first end-to-end application of PLUM. MI components except parsing were developed in the last 5 months, and cannot therefore be considered fully mature. The parsing component, the MIT Fast Parser [4], originated outside BBN and has a more extensive history prior to MUC-3. A central assumption of our approach is that in processing unrestricted text for data extraction, a non-trivial amount of the text will not be understood. As a result, all components of PLUM are designed to operate on partially understood input, taking advantage of information when available, and not failing when information is unavailable
A New Approach to Text Understanding( Book )

2 editions published in 1992 in English and held by 2 WorldCat member libraries worldwide

This paper first briefly describes the architecture of PLUM, BBN's text processing system, and then reports on some experiments evaluating the effectiveness of the design at the component level. Three features are unusual in PLUM's architecture: a domain independent deterministic parser, processing of (the resulting) fragments at the semantic and discourse level, and probabilistic models
BBN PLUM: MUC-3 Test Results and Analysis( Book )

2 editions published in 1991 in English and held by 2 WorldCat member libraries worldwide

Perhaps the most important facts about our participation in MUC-3 reflect our starting point and goals. In March, 1990, we initiated a pilot study on the feasibility and impact of applying statistical algorithms in natural language processing. The experiments were concluded in March, 1991 and lead us to believe that statistical approaches can effectively improve knowledge-based approaches [Weishedel, et al., 1991a, Weischedel, Meteer, and Schwartz, 1991]. Due to nature of that effort, we had focused on many well-defined algorithm experiments. We did not have a complete message processing system; nor was the pilot study designed to create an application system. For the Phase I evaluation, we supplied a module to New York University. At the time of the Phase I Workshop (12-14 February 1991) we decided to participate in MUC with our own entry. The Phase I Workshop provided invaluable insight into what other sites were finding successful in this particular application. On 25 February, we started an intense effort not just to be evaluated on the FBIS articles, but also to create essential components (e.g., discourse component and template generator) and to integrate all components into a complete message processing system. Although the timing of the Phase II test (6-12 May) was hardly ideal for evaluating our site's capabilities, it was ideally timed to serve as a benchmark prior to starting a four year plan for research and development in message understanding. Because of this, we were determined to try alternatives that we believed would be different than those employed by other groups, wherever time permitted. These are covered in the next section. Our results were quite positive, given these circumstances. Our max-tradeoff version achieved 45% recall and 52% precision with 22% overgenerating (See Figure 2). PLUM can be run in several modes, trading off recall versus precision and overgeneration
BBN: Description of the PLUM System as Used for MUC-6( Book )

2 editions published in 1995 in English and held by 2 WorldCat member libraries worldwide

This paper provides a quick summary of our technical approach, which has been developing since 1991 and was first fielded in MUC-3. First a quick review of what is new is provided, then a walk through of system components. Perhaps most interesting is out analysis, following the walk through, of what we learned through MUC-6 and of what directions we would take now to break the performance barriers of current information extraction technology
BBN: Description of the PLUM System as Used for MUC-4( Book )

2 editions published in 1992 in English and held by 2 WorldCat member libraries worldwide

Traditional approaches to the problem of extracting data from texts have emphasized hand-rafted linguistic knowledge. In contrast, BBN's PLUM system (Probabilistic Language Understanding Model) was developed as part of a DARPA-funded research effort on integrating probabilistic language models with more traditional linguistic techniques. Our research and development goals are * more rapid development of new applications, * the ability to train (and re-train) systems based on user markings of correct and incorrect output, * more accurate selection among interpretations when more than one is found, and * more robust partial interpretation when no complete interpretation can be found. A central assumption of our approach is that in processing unrestricted text for data extraction, a non-trivial amount of the text will not be understood. As a result, all components of PLUM are designed to operate on partially understood input, taking advantage of information when available, and not failing when information is unavailable. We had previously performed experiments on components of the system with texts from the Wall Street Journal, however, the MUC-3 task was the first end-to-end application of PLUM. Very little hand-tuning of knowledge bases was done for MUC-4; since MUC-3, the system architecture as depicted in figure 1 has remained essentially the same. In addition to participating in MUC-4, since MUC-3 we focused on porting to new domains and a new language, and on performing various experiments designed to control recall/precision tradeoffs. To support these goals, the preprocessing component and the fragment combiner were made declarative; the semantics component was generalized to use probabilities on word senses; we expanded our treatment of reference; we enlarged the set of system parameters at all levels; and we created a new probabilistic classifier for text relevance which filters discourse events
Portability in the Janus Natural Language Interface( Book )

2 editions published in 1989 in English and held by 2 WorldCat member libraries worldwide

Although natural language technology has achieved a high degree of domain independence through separating domain-independent modules from domain-dependent knowledge bases, portability, as measured by effort to move from one application to another, is still a problem. Here we describe a knowledge acquisition tool (KNACQ) that has sharply decreased our effort in building knowledge bases. The knowledge bases acquired with KNACQ are used by both the understanding components and the generation components of Janus
Research and Development in Natural Language Understanding as Part of the Strategic Computing Program( Book )

2 editions published between 1987 and 1988 in English and held by 2 WorldCat member libraries worldwide

The first chapter summarizes the direction in this work, including not only the areas of our own research and development but also possible integration of components and techniques from other sites. The second chapter reviews our activities in technology transfer, namely moving a natural language interface developed over a period of years of basic research out of the laboratory. The software is being used by The Naval Ocean Systems Center in conjunction with BBN to provide a prototype natural language interface at The Pacific Fleet Command Center. The third chapter describes a simple inference procedure which has proved quite effective in reducing the semantic representation of the user's input to terms of the underlying system (s)
Research and Development in Natural Language Understanding as Part of the Strategic Computing Program. Volume 3. A Guide to IRUS-II Application Development. Revision( Book )

1 edition published in 1990 in English and held by 1 WorldCat member library worldwide

IRUS-II is the understanding subsystem of the Janus natural language interface. IRUS-II is natural language understanding (NLU) shell. That is, it contains domain-independent algorithms, a large grammar of English, domain-independent semantic interpretation rules, and a domain-independent discourse component. In addition, several software aids are provided to customize the system to particular application domains. These software aids output the four knowledge bases necessary for IRUS-II to correctly interpret English utterances and generate appropriate code for simultaneous access to multiple application systems. This document describes the four knowledge bases and how to construct them. This is the third volume of a three volume final report. (kr)
A Guide to IRUS-II Application Development( Book )

1 edition published in 1989 in English and held by 1 WorldCat member library worldwide

IRUS-II is the understanding subsystem of the Janus natural language interface. IRUS-II is a natural language understanding (NLU) shell. That is, it contains domain-independent algorithms, a large grammar of English, domain-independent semantic interpretation rules, and a domain-independent discourse component. In addition, several software aids are provided to customize the system to particular application domains. These software aids output the four knowledge bases necessary for IRUS-II to correctly interpret English utterances and generate appropriate code for simultaneous access to multiple application systems. Natural language interfaces, User interfaces, Knowledge bases. (jes)
Research and Development in Natural Language Understanding as Part of the Strategic Computing Program. Volume 1. Overview of Technical Results. Revision( Book )

1 edition published in 1990 in English and held by 1 WorldCat member library worldwide

This is volume one of a three volume final report. This volume, Volume 1, provides a technical overview of the effort. Chapter 1 is the Executive Summary. Chapter 2 overviews the effort as a whole reporting on (1) Our Integration of software and results from other Strategic Computing contractors, (2) Our participation in Fleet Command Center Battle Management Program, (3) Distribution of the resulting software to other sites, and efforts, and (4) Contributions of the work to the state of the art. The problem of acquiring linguistic knowledge is the chief obstacle to widespread use of natural language technology. Chapters 3 and 6 report results of five to tenfold increase in our productivity in moving the natural language shells to new application domains. The ability of natural language systems to cooperatively handle novel, errorful, or incomplete forms is also critical; chapters 5 and 7 report new techniques to intelligently and gracefully respond to such forms. Chapter 4 reports on an implementation of a discourse module for understanding definite reference. (Author) (kr)
Adaptive natural language processing( Book )

1 edition published in 1991 in English and held by 1 WorldCat member library worldwide

A handful of special purpose systems have been successfully deployed to extract prespecified kinds of data from text. The limitation to widespread deployment of such systems is their assumption of a large volume of handcrafted, domain-dependent, and language-dependent knowledge in the form of rules. A new approach is to add automatically trainable probabilistic language models to linguistically based analysis. This offers several potential advantages: (1) Trainability by finding patterns in a large corpus, rather than handcrafting such patterns. (2) Improvability be re-estimating probabilities based on a user marking correct and incorrect output on a test set. (3) More accurate selection among interpretations when more than one is produced. (4) Robustness by finding the most likely partial interpretation when no complete interpretation can be found
 
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English (28)