Matches in ScholarlyData for { ?s ?p ?o. }
- 283 sameAs 283.
- 283 abstract "In this paper we present our recent work to develop phonemic and syllabic inventories for Castilian Spanish based on the C-ORAL-ROM corpus, a spontaneous spoken resource with varying degrees of naturalness and in different communicative contexts. These inventories have been developed by means of a phonemic and syllabic automatic transcriptor whose output has been assessed by manually reviewing most of the transcriptions. The inventories include absolute frequencies of occurrence of the different phones and syllables. These frequencies have been contrasted against an inventory extracted from a comparable textual corpus, finding evidence that the available inventories, based mainly on text, do not provide an accurate description of spontaneously spoken Castilian Spanish.".
- 283 hasAuthorList authorList.
- 283 hasTopic Linguistics.
- 283 isPartOf proceedings.
- 283 keyword "Corpus (creation, annotation, etc.)".
- 283 keyword "Phonetic Databases, Phonology".
- 283 keyword "Speech resource/database".
- 283 title "Developing a Phonemic and Syllabic Frequency Inventory for Spontaneous Spoken Castilian Spanish and their Comparison to Text-Based Inventories".
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- item-1 hasContent antonio-moreno-sandoval.
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- item-2 hasContent doroteo-torre-toledano.
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- item-3 hasContent raul-de-la-torre.
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- item-4 hasContent marta-garrote.
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- item-5 hasContent jose-m-guirao.
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- 284 creator fabio-massimo-zanzotto.
- 284 creator francesca-fallucchi.
- 284 type InProceedings.
- 284 label "Yet another Platform for Extracting Knowledge from Corpora".
- 284 sameAs 284.
- 284 abstract "The research field of extracting knowledge bases from text collections seems to be mature: its target and its working hypotheses are clear. In this paper we propose a platform, YAPEK, i.e., Yet Another Platform for Extracting Knowledge from corpora, that wants to be the base to collect the majority of algorithms for extracting knowledge bases from corpora. The idea is that, when many knowledge extraction algorithms are collected under the same platform, relative comparisons are clearer and many algorithms can be leveraged to extract more valuable knowledge for final tasks such as Textual Entailment Recognition. As we want to collect many knowledge extraction algorithms, YAPEK is based on the three working hypotheses of the area: the basic hypothesis, the distributional hypothesis, and the point-wise assertion patterns. In YAPEK, these three hypotheses define two spaces: the space of the target textual forms and the space of the contexts. This platform guarantees the possibility of rapidly implementing many models for extracting knowledge from corpora as the platform gives clear entry points to model what is really different in the different algorithms: the feature spaces, the distances in these spaces, and the actual algorithm.".
- 284 hasAuthorList authorList.
- 284 hasTopic Linguistics.
- 284 isPartOf proceedings.
- 284 keyword "Acquisition, Machine Learning".
- 284 keyword "Paraphrasing".
- 284 keyword "Tools, systems, applications".
- 284 title "Yet another Platform for Extracting Knowledge from Corpora".
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- item-1 hasContent francesca-fallucchi.
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- item-2 hasContent fabio-massimo-zanzotto.
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- 286 creator christian-haenig.
- 286 creator stefan-bordag.
- 286 creator uwe-quasthoff.
- 286 type InProceedings.
- 286 label "UnsuParse: unsupervised Parsing with unsupervised Part of Speech Tagging".
- 286 sameAs 286.
- 286 abstract "Based on simple methods such as observing word and part of speech tag co-occurrence and clustering, we generate syntactic parses of sentences in an entirely unsupervised and self-inducing manner. The parser learns the structure of the language in question based on measuring breaking points within sentences. The learning process is divided into two phases, learning and application of learned knowledge. The basic learning works in an iterative manner which results in a hierarchical constituent representation of the sentence. Part-of-Speech tags are used to circumvent the data sparseness problem for rare words. The algorithm is applied on untagged data, on manually assigned tags and on tags produced by an unsupervised part of speech tagger. The results are unsurpassed by any self-induced parser and challenge the quality of trained parsers with respect to finding certain structures such as noun phrases.".
- 286 hasAuthorList authorList.
- 286 hasTopic Linguistics.
- 286 isPartOf proceedings.
- 286 keyword "Acquisition, Machine Learning".
- 286 keyword "Parsing Systems".
- 286 keyword "Statistical methods".
- 286 title "UnsuParse: unsupervised Parsing with unsupervised Part of Speech Tagging".
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- item-1 hasContent christian-haenig.
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- item-2 hasContent stefan-bordag.
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- item-3 hasContent uwe-quasthoff.
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- 287 creator carol-neidle.
- 287 creator hermann-ney.
- 287 creator philippe-dreuw.
- 287 creator stan-sclaroff.
- 287 creator vassilis-athitsos.
- 287 type InProceedings.
- 287 label "Benchmark Databases for Video-Based Automatic Sign Language Recognition".
- 287 sameAs 287.
- 287 abstract "A new, linguistically annotated, video database for automatic sign language recognition is presented. The new RWTH-BOSTON-400 corpus, which consists of 843 sentences, several speakers and separate subsets for training, development, and testing is described in detail. For evaluation and benchmarking of automatic sign language recognition, large corpora are needed. Recent research has focused mainly on isolated sign language recognition methods using video sequences that have been recorded under lab conditions using special hardware like data gloves. Such databases have often consisted generally of only one speaker and thus have been speaker-dependent, and have had only small vocabularies. A new database access interface, which was designed and created to provide fast access to the database statistics and content, makes it possible to easily browse and retrieve particular subsets of the video database. Preliminary baseline results on the new corpora are presented. In contradistinction to other research in this area, all databases presented in this paper will be publicly available.".
- 287 hasAuthorList authorList.
- 287 hasTopic Linguistics.