1,350 research outputs found

    The Many Functions of Discourse Particles: A Computational Model of Pragmatic Interpretation

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    We present a connectionist model for the interpretation of discourse\ud particles in real dialogues that is based on neuronal\ud principles of categorization (categorical perception, prototype\ud formation, contextual interpretation). It can be shown that\ud discourse particles operate just like other morphological and\ud lexical items with respect to interpretation processes. The description\ud proposed locates discourse particles in an elaborate\ud model of communication which incorporates many different\ud aspects of the communicative situation. We therefore also\ud attempt to explore the content of the category discourse particle.\ud We present a detailed analysis of the meaning assignment\ud problem and show that 80%ā€“ 90% correctness for unseen discourse\ud particles can be reached with the feature analysis provided.\ud Furthermore, we show that ā€˜analogical transferā€™ from\ud one discourse particle to another is facilitated if prototypes\ud are computed and used as the basis for generalization. We\ud conclude that the interpretation processes which are a part of\ud the human cognitive system are very similar with respect to\ud different linguistic items. However, the analysis of discourse\ud particles shows clearly that any explanatory theory of language\ud needs to incorporate a theory of communication processes

    Learning Language from a Large (Unannotated) Corpus

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    A novel approach to the fully automated, unsupervised extraction of dependency grammars and associated syntax-to-semantic-relationship mappings from large text corpora is described. The suggested approach builds on the authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well as on a number of prior papers and approaches from the statistical language learning literature. If successful, this approach would enable the mining of all the information needed to power a natural language comprehension and generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa

    Theory Format and Structured and SLA Theory

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    SLA theory development has reached the stage where a meta-understanding of the forms and structures is needed to facilitate theory development. This paper reviews work in the philosophy of science pertinent to SL theory formats and structures, relating it to recent SLA theories

    Abductive speech act recognition, corporate agents and the COSMA system

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    This chapter presents an overview of the DISCO project\u27s solutions to several problems in natural language pragmatics. Its central focus is on relating utterances to intentions through speech act recognition. Subproblems include the incorporation of linguistic cues into the speech act recognition process, precise and efficient multiagent belief attribution models (Corporate Agents), and speech act representation and processing using Corporate Agents. These ideas are being tested within the COSMA appointment scheduling system, one application of the DISCO natural language interface. Abductive speech act processing in this environment is not far from realizing its potential for fully bidirectional implementation

    Korean Grammar Using TAGs

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    This paper addresses various issues related to representing the Korean language using Tree Adjoining Grammars. Topics covered include Korean grammar using TAGs, Machine Translation between Korean and English using Synchronous Tree Adjoining Grammars (STAGs), handling scrambling using Multi Component TAGs (MC-TAGs), and recovering empty arguments. The data for the parsing is from US military communication messages

    The Computational Analysis of the Syntax and Interpretation of Free Word Order in Turkish

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    In this dissertation, I examine a language with ā€œfreeā€ word order, specifically Turkish, in order to develop a formalism that can capture the syntax and the context-dependent interpretation of ā€œfreeā€ word order within a computational framework. In ā€œfreeā€ word order languages, word order is used to convey distinctions in meaning that are not captured by traditional truth-conditional semantics. The word order indicates the ā€œinformation structureā€, e.g. what is the ā€œtopicā€ and the ā€œfocusā€ of the sentence. The context-appropriate use of ā€œfreeā€ word order is of considerable importance in developing practical applications in natural language interpretation, generation, and machine translation. I develop a formalism called Multiset-CCG, an extension of Combinatory Categorial Grammars, CCGs, (Ades/Steedman 1982, Steedman 1985), and demonstrate its advantages in an implementation of a data-base query system that interprets Turkish questions and generates answers with contextually appropriate word orders. Multiset-CCG is a context-sensitive and polynomially parsable grammar that captures the formal and descriptive properties of ā€œfreeā€ word order and restrictions on word order in simple and complex sentences (with discontinuous constituents and long distance dependencies). Multiset-CCG captures the context-dependent meaning of word order in Turkish by compositionally deriving the predicate-argument structure and the information structure of a sentence in parallel. The advantages of using such a formalism are that it is computationally attractive and that it provides a compositional and flexible surface structure that allows syntactic constituents to correspond to information structure constituents. A formalism that integrates information structure and syntax such as Multiset-CCG is essential to the computational tasks of interpreting and generating sentences with contextually appropriate word orders in ā€œfreeā€ word order languages

    Research in the Language, Information and Computation Laboratory of the University of Pennsylvania

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    This report takes its name from the Computational Linguistics Feedback Forum (CLiFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. Naturally, this introduction cannot spell out all the connections between these abstracts; we invite you to explore them on your own. In fact, with this issue itā€™s easier than ever to do so: this document is accessible on the ā€œinformation superhighwayā€. Just call up http://www.cis.upenn.edu/~cliff-group/94/cliffnotes.html In addition, you can find many of the papers referenced in the CLiFF Notes on the net. Most can be obtained by following links from the authorsā€™ abstracts in the web version of this report. The abstracts describe the researchersā€™ many areas of investigation, explain their shared concerns, and present some interesting work in Cognitive Science. We hope its new online format makes the CLiFF Notes a more useful and interesting guide to Computational Linguistics activity at Penn

    A conceptual investigation of the ontological commensurability of spatial data infrastructures among different cultures

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    Humans think and communicate in very flexible and schematic ways, and a Spatial Data Infrastructure (SDI) for the Amazon and associated information system ontologies should reflect this flexibility and the adaptive nature of human cognition in order to achieve semantic interoperability. In this paper I offer a conceptual investigation of SDI and explore the nature of cultural schemas as expressions of indigenous ontologies and the challenges of semantic interoperability across cultures. Cultural schemas are, in essence, our ontologies, but they are markedly different than classical formal ontologies. They shape our ontological commitments to what exists in the world as well as the ways in which we approach and engage the world. And while they help structure our understanding of the world in which we are embedded, they are associative and flexible. They help to focus our attention to particular details of our experiences and give them salience, yet they cannot be simply reduced to a series of extracted features. They allow us to make meaning of the contextualized, cultural experience in which we are always immersed. An SDI is a shared social-technological-informational structure that, if it is to be useful and successful for sustainability in the Amazon, must incorporate and use indigenous cultural schemas. Indigenous communities must have the ability to contribute to the collection of geospatial data and their contributions recognized as legitimate forms of knowledge. In order for the SDI to work, it must recognize the larger cultural landscape to which cultural schemas can connect to the ready-to-hand elements of salient cultural experiences
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