Applying Tools and Techniques of Natural Language Processing to the Creation of Resources for Less Commonly Taught Languages

This paper proposes that research results from the area of naturallanguage processing could effectively be applied to creating softwareto facilitate the development oflanguage learning materials foranynaturallanguage. We will suggest that a knowledge-elicitationsystem called Boas, which was originally created to support amachine-translation application, could be modified to supportlanguage-learning ends. Boas leads a speaker of any natural Ianguage,who is not necessarily trained in linguistics, through a seriesof pedagogically-supported questionnaires, the responses to whichconstitute a" profile" of the language. This profile includes morphological,lexical and syntactic information. Once this structuredprofile is created, it can feed into virtually any type of system,including one to support language learning. Creating languagelearningsoftware using a system like this would be efficient in twoways: first, it would exploit extant cutting-edge research and technologiesin naturallanguage processin~ and second, it would permita single tool to be used for all languages, including less commonlytaught ones, for which limited funding for resource development isa bottleneck

Linguistics for the Age of AI

A human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems. One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning—the deep, context-sensitive meaning that a person derives from spoken or written language. With Linguistics for the Age of AI, McShane and Nirenburg offer a roadmap for creating language-endowed intelligent agents (LEIAs) that can understand,explain, and learn. They describe the language-understanding capabilities of LEIAs from the perspectives of cognitive modeling and system building, emphasizing “actionability”—which involves achieving interpretations that are sufficiently deep, precise, and confident to support reasoning about action. After detailing their microtheories for topics such as semantic analysis, basic coreference, and situational reasoning, McShane and Nirenburg turn to agent applications developed using those microtheories and evaluations of a LEIA's language understanding capabilities. McShane and Nirenburg argue that the only way to achieve human-level language understanding by machines is to place linguistics front and center, using statistics and big data as contributing resources. They lay out a long-term research program that addresses linguistics and real-world reasoning together, within a comprehensive cognitive architecture

Learning Components of Computational Models from Texts

The mental models of experts can be encoded in computational cognitive models that can support the functioning of intelligent agents. This paper compares human mental models to computational cognitive models, and explores the extent to which the latter can be acquired automatically from published sources via automatic learning by reading. It suggests that although model components can be automatically learned, published sources lack sufficient information for the compilation of fully specified models that can support sophisticated agent capabilities, such as physiological simulation and reasoning. Such models require hypotheses and educated guessing about unattested phenomena, which can be provided only by humans and are best recorded using knowledge engineering strategies. This work merges past work on cognitive modeling, agent simulation, learning by reading, and narrative structure, and draws examples from the domain of clinical medicine

Automatic Ellipsis Resolution: Recovering Covert Information from Text

Ellipsis is a linguistic process that makes certain aspects of text meaning not directly traceable to surface text elements and, therefore, inaccessible to most language processing technologies. However, detecting and resolving ellipsis is an indispensable capability for language-enabled intelligent agents. The key insight of the work presented here is that not all cases of ellipsis are equally difficult: some can be detected and resolved with high confidence even before we are able to build agents with full human-level semantic and pragmatic understanding of text. This paper describes a fully automatic, implemented and evaluated method of treating one class of ellipsis: elided scopes of modality. Our cognitively-inspired approach, which centrally leverages linguistic principles, has also been applied to overt referring expressions with equally promising results

User-extensible on-line lexicons for language learning. Under review at CALICO

Abstract. This article describes a lexicon development and editing tool that we believe could profitably be applied to the teaching and learning of any language. Although originally developed as part of a machine translation system, the tool fills the same desiderata as one might posit for a language-learning aid: it has a fast, convenient interface, anticipates the needs of users of various profiles, incorporates means of expediting work, can monitor new submissions for completeness, is indefinitely extensible, and can be used for any language with no language-specific modifications necessary. Although the most economical application of this research and development effort would be to modify this particular tool to cater to language-learning needs, one could also exploit the research findings, design aspects and general motivation to the creation of other language-learning tools and technologies. In short, we present a proposal for incorporating cutting-edge languageprocessing technologies into the classroom at relatively low cost. 1. The Need for User-Extensible Lexicons The teaching and learning of foreign languages is a fruitful avenue for the application of educational technologies. For the dozen or so more commonly-studied languages, much is already available, like sophisticated CD-ROMs to supplement textbooks, interactive exercises for learning vocabulary and wor

Blasting Open a Choice Space: Learning Inflectional Morphology for NLP

Abstract: 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 paper is an argument about the necessity to analyze the design choice space for complex NLP systems early, comprehensively and overtly. key words: knowledge elicitation, system architectures, morphology, natural language processing, machine translation 2 Building novel complex applications in computational linguistics is commonly influenced by countless decisions that derive from an intangible combination of knowledge, assumptions and beliefs that the system designers hold about the content of the nascent system as well as its desired final functionalities. Some of these decisions are made consciously while some others should be qualified as tacit assumptions. This is understandable, at least in part, because experimental research differs i