Matches in ScholarlyData for { ?s ?p ?o. }
- 863 creator christophe-veaux.
- 863 creator gregory-beller.
- 863 creator xavier-rodet.
- 863 type InProceedings.
- 863 label "IrcamCorpusTools: an Extensible Platform for Spoken Corpora Exploitation".
- 863 sameAs 863.
- 863 abstract "Corpus based methods are increasingly used for speech technology applications and for the development of theoretical or computer models of spoken languages. These usages range from unit selection speech synthesis to statistical modeling of speech phenomena like prosody or expressivity. In all cases, these usages require a wide range of tools for corpus creation, labeling, symbolic and acoustic analysis, storage and query. However, if a variety of tools exists for each of these individual tasks, they are rarely integrated into a single platform made available to a large community of researchers. In this paper, we propose IrcamCorpusTools, an open and easily extensible platform for analysis, query and visualization of speech corpora. It is already used for unit selection speech synthesis, for prosody and expressivity studies, and to exploit various corpora of spoken French or other languages.".
- 863 hasAuthorList authorList.
- 863 hasTopic Linguistics.
- 863 isPartOf proceedings.
- 863 keyword "Corpus (creation, annotation, etc.)".
- 863 keyword "Speech resource/database".
- 863 keyword "Tools, systems, applications".
- 863 title "IrcamCorpusTools: an Extensible Platform for Spoken Corpora Exploitation".
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- item-1 hasContent christophe-veaux.
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- item-2 hasContent gregory-beller.
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- item-3 hasContent xavier-rodet.
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- 864 creator bodil-nistrup-madsen.
- 864 creator hanne-erdman-thomsen.
- 864 type InProceedings.
- 864 label "A Taxonomy of Lexical Metadata Categories".
- 864 sameAs 864.
- 864 abstract "Metadata registries comprising sets of categories to be used in data collections exist in many fields. The purpose of a metadata registry is to facilitate data exchange and interoperability within a domain, and registries often contain definitions and examples. In this paper we will argue that in order to ensure completeness, consistency, user-friendliness and extensibility, metadata registries should be structured as taxonomies. Furthermore we will illustrate the usefulness of using terminological ontologies as the basis for developing metadata taxonomies. In this connection we will discuss the principles of developing ontologies and the differences between taxonomies and ontologies. The paper includes examples of initiatives for developing metadata standards within the field of language resources, more specifically lexical data categories, elaborated at international and national level. However, the principles that we introduce for the development of data category registries are relevant not only for metadata registries for lexical resources, but for all kinds of metadata registries.".
- 864 hasAuthorList authorList.
- 864 hasTopic Linguistics.
- 864 isPartOf proceedings.
- 864 keyword "Metadata".
- 864 keyword "Ontologies".
- 864 keyword "Standards for LRs".
- 864 title "A Taxonomy of Lexical Metadata Categories".
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- item-1 hasContent bodil-nistrup-madsen.
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- item-2 hasContent hanne-erdman-thomsen.
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- 866 creator david-guthrie.
- 866 creator louise-guthrie.
- 866 creator yorick-wilks.
- 866 type InProceedings.
- 866 label "An Unsupervised Probabilistic Approach for the Detection of Outliers in Corpora".
- 866 sameAs 866.
- 866 abstract "Many applications of computational linguistics are greatly influenced by the quality of corpora available and as automatically generated corpora continue to play an increasingly common role, it is essential that we not overlook the importance of well-constructed and homogeneous corpora. This paper describes an automatic approach to improving the homogeneity of corpora using an unsupervised method of statistical outlier detection to find documents and segments that do not belong in a corpus. We consider collections of corpora that are homogeneous with respect to topic (i.e. about the same subject), or genre (written for the same audience or from the same source) and use a combination of stylistic and lexical features of the texts to automatically identify pieces of text in these collections that break the homogeneity. These pieces of text that are significantly different from the rest of the corpus are likely to be errors that are out of place and should be removed from the corpus before it is used for other tasks. We evaluate our techniques by running extensive experiments over large artificially constructed corpora that each contain single pieces of text from a different topic, author, or genre than the rest of the collection and measure the accuracy of identifying these pieces of text without the use of training data. We show that when these pieces of text are reasonably large (1,000 words) we can reliably identify them in a corpus.".
- 866 hasAuthorList authorList.
- 866 hasTopic Linguistics.
- 866 isPartOf proceedings.
- 866 keyword "Corpus (creation, annotation, etc.)".
- 866 keyword "Document Classification, Text categorisation".
- 866 keyword "Statistical methods".
- 866 title "An Unsupervised Probabilistic Approach for the Detection of Outliers in Corpora".
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- item-1 hasContent david-guthrie.
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- item-2 hasContent louise-guthrie.
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- item-3 hasContent yorick-wilks.
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- 867 creator peter-adolphs.
- 867 type InProceedings.
- 867 label "Acquiring a Poor Man s Inflectional Lexicon for German".
- 867 sameAs 867.
- 867 abstract "Many NLP modules and applications require the availability of a module for wide-coverage inflectional analysis. One way to obtain such analyses is to use a morphological analyser in combination with an inflectional lexicon. Since large text corpora nowadays are easily available and inflectional systems are in general well understood, it seems feasible to acquire lexical data from raw texts, guided by our knowledge of inflection. I present an acquisition method along these lines for German. The general idea can be roughly summarised as follows: first, generate a set of lexical entry hypotheses for each word-form in the corpus; then, select hypotheses that explain the word-forms found in the corpus best . To this end, I have turned an existing morphological grammar, cast in finite-state technology (Schmid et al. 2004), into a hypothesiser for lexical entries. Irregular forms are simply listed so that they do not interfere with the regular rules used in the hypothesiser. Running the hypothesiser on a text corpus yields a large number of lexical entry hypotheses. These are then ranked according to their validity with the help of a statistical model that is based on the number of attested and predicted word forms for each hypothesis.".
- 867 hasAuthorList authorList.
- 867 hasTopic Linguistics.
- 867 isPartOf proceedings.
- 867 keyword "Acquisition, Machine Learning".
- 867 keyword "Lexicon, lexical database".
- 867 keyword "Morphology".
- 867 title "Acquiring a Poor Man s Inflectional Lexicon for German".
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