Tooling the lexicon acquisition process for large-scale KBMT

Leavitt, J.R.R.; Lonsdale, Deryle; Keck, Kevin; Nyberg, Eric

doi:10.1109/tai.1994.346479

Cited by 3 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3 As a result, it is quite different from most interactive knowledge acquisition tools used in NLP (e.g. Leavitt et al (1994), Nirenburg (1996)). …”

Section: Overview Of Boasmentioning

confidence: 99%

Mood and modality: out of theory and into the fray

2004

View full text Add to dashboard Cite

The topic of mood and modality (MOD) is a difficult aspect of language description because, among other reasons, the inventory of modal meanings is not stable across languages, moods do not map neatly from one language to another, modality may be realised morphologically or by free-standing words, and modality interacts in complex ways with other modules of the grammar, like tense and aspect. Describing MOD is especially difficult if one attempts to develop a unified approach that not only provides cross-linguistic coverage, but is also useful in practical natural language processing systems. This article discusses an approach to MOD that was developed for and implemented in the Boas Knowledge-Elicitation (KE) system. Boas elicits knowledge about any language, L, from an informant who need not be a trained linguist. That knowledge then serves as the static resources for an L-to-English translation system. The KE methodology used throughout Boas is driven by a resident inventory of parameters, value sets, and means of their realisation for a wide range of language phenomena. MOD is one of those parameters, whose values are the inventory of attested and not yet attested moods (e.g. indicative, conditional, imperative), and whose realisations include flective morphology, agglutinating morphology, isolating morphology, words, phrases and constructions. Developing the MOD elicitation procedures for Boas amounted to wedding the extensive theoretical and descriptive research on MOD with practical approaches to guiding an untrained informant through this non-trivial task. We believe that our experience in building the MOD module of Boas offers insights not only into cross-linguistic aspects of MOD that have not previously been detailed in the natural language processing literature, but also into KE methodologies that could be applied more broadly.

show abstract

“…3 As a result, it is quite different from most interactive knowledge acquisition tools used in NLP (e.g. Leavitt et al (1994), Nirenburg (1996)). …”

Section: Overview Of Boasmentioning

confidence: 99%

Mood and modality: out of theory and into the fray

2004

View full text Add to dashboard Cite

show abstract

“…Thus, the methodology of KE employed in Boas integrates the familiar graphical user interfaces with the (meta)knowledge about the typology and universals of human languages and a methodology of guiding the user through the acquisition process. 4 As a result, it is quite different from most interactive knowledge acquisition tools used in natural language processing (NLP) (e.g., Leavitt et al 1994;Nirenburg et al 1996).…”

Section: Introductionmentioning

confidence: 99%

Blasting Open a Choice Space: Learning Inflectional Morphology for NLP

McShane¹,

Ниренбург²

2003

Computational Intelligence

View full text Add to dashboard Cite

This article discusses the various aspects of designing a system for eliciting knowledge about language from informants. For each design aspect, various options for implementation are presented, along with their pros, cons, and repercussions for other parts of the knowledge elicitation system. A running example throughout the text is taken from the paradigmatic morphology elicitation module of a system called Boas, which elicits knowledge to support a machine translation system. The main point of the article is an argument about the necessity to analyze the design choice space for complex natural language processing (NLP) systems early, comprehensively, and overtly.

show abstract

Parameterizing and Eliciting Text Elements across Languages for Use in Natural Language Processing Systems

McShane

Ниренбург

2003

Machine Translation

View full text Add to dashboard Cite

Tooling the lexicon acquisition process for large-scale KBMT

Cited by 3 publications

References 4 publications

Mood and modality: out of theory and into the fray

Mood and modality: out of theory and into the fray

Blasting Open a Choice Space: Learning Inflectional Morphology for NLP

Parameterizing and Eliciting Text Elements across Languages for Use in Natural Language Processing Systems

Contact Info

Product

Resources

About