Matches in ScholarlyData for { ?s ?p ?o. }
- 537 keyword "Syntax".
- 537 title "Evaluating a German Sketch Grammar: A Case Study on Noun Phrase Case".
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- item-1 hasContent kremena-ivanova.
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- item-2 hasContent ulrich-heid.
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- item-3 hasContent sabine-schulte-im-walde.
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- item-4 hasContent adam-kilgarriff.
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- item-5 hasContent jan-pomikalek.
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- 538 creator christopher-brewster.
- 538 creator fabio-ciravegna.
- 538 creator jose-iria.
- 538 creator ziqi-zhang.
- 538 type InProceedings.
- 538 label "A Comparative Evaluation of Term Recognition Algorithms".
- 538 sameAs 538.
- 538 abstract "Automatic Term recognition (ATR) is a fundamental processing step preceding more complex tasks such as semantic search and ontology learning. From a large number of methodologies available in the literature only a few are able to handle both single and multi-word terms. In this paper we present a comparison of five such algorithms and propose a combined approach us¬ing a voting mechanism. We evaluated the six approaches using two different corpora and show how the voting algo¬rithm performs best on one corpus (a collection of texts from Wikipedia) and less well using the Genia corpus (a standard life science corpus). This indicates that choice and design of corpus has a major impact on the evaluation of term recog¬nition algorithms. Our experiments also showed that single-word terms can be equally important and occupy a fairly large proportion in certain domains. As a result, algorithms that ignore single-word terms may cause problems to tasks built on top of ATR. Effective ATR systems also need to take into account both the unstructured text and the structured aspects and this means information extraction techniques need to be integrated into the term recognition process.".
- 538 hasAuthorList authorList.
- 538 hasTopic Linguistics.
- 538 isPartOf proceedings.
- 538 keyword "Evaluation methodologies".
- 538 keyword "MultiWord Expressions & Collocations".
- 538 keyword "Text mining".
- 538 title "A Comparative Evaluation of Term Recognition Algorithms".
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- item-1 hasContent ziqi-zhang.
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- item-2 hasContent jose-iria.
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- item-3 hasContent christopher-brewster.
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- item-4 hasContent fabio-ciravegna.
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- 539 creator constantin-orasan.
- 539 creator oana-andreea-chiorean.
- 539 type InProceedings.
- 539 label "Evaluation of a Cross-lingual Romanian-English Multi-document Summariser".
- 539 sameAs 539.
- 539 abstract "The rapid growth of the Internet means that more information is available than ever before. Multilingual multi-document summarisation offers a way to access this information even when it is not in a language spoken by the reader by extracting the gist from related documents and translating it automatically. This paper presents an experiment in which Maximal Marginal Relevance (MMR), a well known multi-document summarisation method, is used to produce summaries from Romanian news articles. A task-based evaluation performed on both the original summaries and on their automatically translated versions reveals that they still contain a significant portion of the important information from the original texts. However, direct evaluation of the automatically translated summaries shows that they are not very legible and this can put off some readers who want to find out more about a topic.".
- 539 hasAuthorList authorList.
- 539 hasTopic Linguistics.
- 539 isPartOf proceedings.
- 539 keyword "Machine Translation, SpeechToSpeech Translation".
- 539 keyword "Summarisation".
- 539 title "Evaluation of a Cross-lingual Romanian-English Multi-document Summariser".
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- item-1 hasContent constantin-orasan.
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- item-2 hasContent oana-andreea-chiorean.
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- 54 creator alexandru-ceausu.
- 54 creator dan-tufis.
- 54 type InProceedings.
- 54 label "DIAC+: a Professional Diacritics Recovering System".
- 54 sameAs 54.
- 54 abstract "In languages that use diacritical characters, if these special signs are stripped-off from a word, the resulted string of characters may not exist in the language, and therefore its normative form is, in general, easy to recover. However, this is not always the case, as presence or absence of a diacritical sign attached to a base letter of a word which exists in both variants, may change its grammatical properties or even the meaning, making the recovery of the missing diacritics a difficult task, not only for a program but sometimes even for a human reader. We describe and evaluate an accurate knowledge-based system for automatic recovery of the missing diacritics in MS-Office documents written in Romanian. For the rare cases when the system is not able to make a reliable decision, it either provides the user a list of words with their recovery suggestions, or probabilistically chooses one of the possible changes, but leaves a trace (a highlighted comment) on each word the modification of which was uncertain.".
- 54 hasAuthorList authorList.
- 54 hasTopic Linguistics.
- 54 isPartOf proceedings.
- 54 keyword "Authoring tools, proofing".
- 54 keyword "Language modelling".
- 54 keyword "Lexicon, lexical database".
- 54 title "DIAC+: a Professional Diacritics Recovering System".