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- 616 creator edouard-geoffrois.
- 616 type InProceedings.
- 616 label "An Economic View on Human Language Technology Evaluation".
- 616 sameAs 616.
- 616 abstract "This paper analyses some general issues about human language technology evaluation, focusing on economic aspects. It first provides a scientific rationale for the need to organize evaluation in the form of campaigns, by relating this need to some basic characteristics of human language technologies, namely that they involve learning to process information in a way which reproduces human capabilities. It then reviews the benefits and constraints of these evaluation campaigns. Borrowing concepts from the field of economics, it also provides an analysis of the economic incentives to organize evaluation campaigns. It entails from this analysis that fitting evaluation campaigns to the needs of scientific research requires a strong implication in term of research policy and public funding.".
- 616 hasAuthorList authorList.
- 616 hasTopic Linguistics.
- 616 isPartOf proceedings.
- 616 keyword "Evaluation methodologies".
- 616 keyword "LR national/international projects, organizational/policy issues".
- 616 title "An Economic View on Human Language Technology Evaluation".
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- 617 creator charles-fillmore.
- 617 creator christiane-fellbaum.
- 617 creator collin-baker.
- 617 creator nancy-ide.
- 617 creator rebecca-passonneau.
- 617 type InProceedings.
- 617 label "MASC: the Manually Annotated Sub-Corpus of American English".
- 617 sameAs 617.
- 617 abstract "To answer the critical need for sharable, reusable annotated resources with rich linguistic annotations, we are developing a Manually Annotated Sub-Corpus (MASC) including texts from diverse genres and manual annotations or manually-validated annotations for multiple levels, including WordNet senses and FrameNet frames and frame elements, both of which have become significant resources in the international computational linguistics community. To derive maximal benefit from the semantic information provided by these resources, the MASC will also include manually-validated shallow parses and named entities, which will enable linking WordNet senses and FrameNet frames within the same sentences into more complex semantic structures and, because named entities will often be the role fillers of FrameNet frames, enrich the semantic and pragmatic information derivable from the sub-corpus. All MASC annotations will be published with detailed inter-annotator agreement measures. The MASC and its annotations will be freely downloadable from the ANC website, thus providing maximum accessibility for researchers from around the globe.".
- 617 hasAuthorList authorList.
- 617 hasTopic Linguistics.
- 617 isPartOf proceedings.
- 617 keyword "Acquisition, Machine Learning".
- 617 keyword "Corpus (creation, annotation, etc.)".
- 617 keyword "Lexicon, lexical database".
- 617 title "MASC: the Manually Annotated Sub-Corpus of American English".
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- 618 creator asuka-sumida.
- 618 creator kentaro-torisawa.
- 618 creator naoki-yoshinaga.
- 618 type InProceedings.
- 618 label "Boosting Precision and Recall of Hyponymy Relation Acquisition from Hierarchical Layouts in Wikipedia".
- 618 sameAs 618.
- 618 abstract "This paper proposes an extension of Sumida and Torisawa s method of acquiring hyponymy relations from hierachical layouts in Wikipedia (Sumida and Torisawa, 2008). We extract hyponymy relation candidates (HRCs) from the hierachical layouts in Wikipedia by regarding all subordinate items of an item x in the hierachical layouts as x s hyponym candidates, while Sumida and Torisawa (2008) extracted only direct subordinate items of an item x as x s hyponym candidates. We then select plausible hyponymy relations from the acquired HRCs by running a filter based on machine learning with novel features, which even improve the precision of the resulting hyponymy relations. Experimental results show that we acquired more than 1.34 million hyponymy relations with a precision of 90.1%.".
- 618 hasAuthorList authorList.
- 618 hasTopic Linguistics.
- 618 isPartOf proceedings.
- 618 keyword "Acquisition, Machine Learning".
- 618 keyword "Lexicon, lexical database".
- 618 keyword "Question Answering".
- 618 title "Boosting Precision and Recall of Hyponymy Relation Acquisition from Hierarchical Layouts in Wikipedia".
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- item-1 hasContent asuka-sumida.
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- 619 creator pushpak-bhattacharyya.
- 619 creator rajat-mohanty.
- 619 type InProceedings.
- 619 label "Lexical Resources for Semantics Extraction".
- 619 sameAs 619.
- 619 abstract "In this paper, we report our work on the creation of a number of lexical resources that are crucial for an interlingua based MT from English to other languages. These lexical resources are in the form of sub-categorization frames, verb knowledge bases and rule templates for establishing semantic relations and speech act like attributes. We have created these resources over a long period of time from Oxford Advanced Learners Dictionary (OALD) [1], VerbNet [2], Princeton WordNet 2.1 [3], LCS database [4], Penn Tree Bank [5], and XTAG lexicon [6]. On the challenging problem of generating interlingua from domain and structure unrestricted English sentences, we are able to demonstrate that the use of these lexical resources makes a difference in terms of accuracy figures.".
- 619 hasAuthorList authorList.
- 619 hasTopic Linguistics.
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- 619 keyword "Lexicon, lexical database".