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- item-1 hasContent constantin-orasan.
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- 715 creator diana-trandabat.
- 715 creator maria-husarciuc.
- 715 type InProceedings.
- 715 label "Romanian Semantic Role Resource".
- 715 sameAs 715.
- 715 abstract "Semantic databases are a stable starting point in developing knowledge based systems. Since creating language resources demands many temporal, financial and human resources, a possible solution could be the import of a resource annotation from one language to another. This paper presents the creation of a semantic role database for Romanian, starting from the English FrameNet semantic resource. The intuition behind the importing program is that most of the frames defined in the English FN are likely to be valid cross-lingual, since semantic frames express conceptual structures, language independent at the deep structure level. The surface realization, the surface level, is realized according to each language syntactic constraints. In the paper we present the advantages of choosing to import the English FrameNet annotation, instead of annotating a new corpus. We also take into account the mismatches encountered in the validation process. The rules created to manage particular situations are used to improve the import program. We believe the information and argumentations in this paper could be of interest for those who wish develop FrameNet-like systems for other languages.".
- 715 hasAuthorList authorList.
- 715 hasTopic Linguistics.
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- 715 keyword "Corpus (creation, annotation, etc.)".
- 715 keyword "LR national/international projects, organizational/policy issues".
- 715 keyword "Semantics".
- 715 title "Romanian Semantic Role Resource".
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- 716 creator damir-boras.
- 716 creator nikola-ljubesic.
- 716 creator tomislava-lauc.
- 716 type InProceedings.
- 716 label "Generating a Morphological Lexicon of Organization Entity Names".
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- 716 abstract "This paper describes methods used for generating a morphological lexicon of organization entity names in Croatian. This resource is intended for two primary tasks: template-based natural language generation and named entity identification. The main problems concerning the lexicon generation are high level of inflection in Croatian and low linguistic quality of the primary resource containing named entities in normal form. The problem is divided into two subproblems concerning single-word and multi-word expressions. The single-word problem is solved by training a supervised learning algorithm called linear successive abstraction. With existing common language morphological resources and two simple hand-crafted rules backing up the algorithm, accuracy of 98.70% on the test set is achieved. The multi-word problem is solved through a semi-automated process for multi-word entities occurring in the first 10,000 named entities. The generated multi-word lexicon will be used for natural language generation only while named entity identification will be solved algorithmically in forthcoming research. The single-word lexicon is capable of handling both tasks.".
- 716 hasAuthorList authorList.
- 716 hasTopic Linguistics.
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- 716 keyword "Generation".
- 716 keyword "Information Extraction, Information Retrieval".
- 716 keyword "Lexicon, lexical database".
- 716 title "Generating a Morphological Lexicon of Organization Entity Names".
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- item-1 hasContent nikola-ljubesic.
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- item-2 hasContent tomislava-lauc.
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- 717 creator fabio-ciravegna.
- 717 creator jonathan-butters.
- 717 type InProceedings.
- 717 label "Using Similarity Metrics For Terminology Recognition".
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- 717 abstract "In this paper we present an approach to terminology recognition whereby a sublanguage term (e.g. an aircraft engine component term extracted from a maintenance log) is matched to its corresponding term from a pre-defined list (such as a taxonomy representing the official break-down of the engine). Terminology recognition is addressed as a classification task whereby the extracted term is associated to one or more potential terms in the official description list via the application of string similarity metrics. The solution described in the paper uses dynamically computed similarity cut-off thresholds calculated on the basis of modeling a noise curve. Dissimilar string matches form a Gaussian distributed noise curve that can be identified and extracted leaving only mostly similar string matches. Dynamically calculated thresholds are preferable over fixed similarity thresholds as fixed thresholds are inherently imprecise, that is, there is no similarity boundary beyond which any two strings always describe the same concept.".
- 717 hasAuthorList authorList.
- 717 hasTopic Linguistics.
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- 717 keyword "Information Extraction, Information Retrieval".
- 717 keyword "Statistical methods".
- 717 keyword "Tools, systems, applications".
- 717 title "Using Similarity Metrics For Terminology Recognition".
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- item-1 hasContent jonathan-butters.
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- item-2 hasContent fabio-ciravegna.
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- 718 creator ralf-krestel.
- 718 creator rene-witte.