Matches in ScholarlyData for { ?s ?p ?o. }
- 308 title "Toward Active Learning in Data Selection: Automatic Discovery of Language Features During Elicitation".
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- item-1 hasContent jonathan-clark.
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- item-2 hasContent robert-frederking.
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- item-3 hasContent lori-levin.
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- 309 creator animesh-mukherjee.
- 309 creator christian-biemann.
- 309 creator joydeep-nath.
- 309 creator monojit-choudhury.
- 309 creator niloy-ganguly.
- 309 type InProceedings.
- 309 label "Unsupervised Parts-of-Speech Induction for Bengali".
- 309 sameAs 309.
- 309 abstract "We present a study of the word interaction networks of Bengali in the framework of complex networks. The topological properties of these networks reveal interesting insights into the morpho-syntax of the language, whereas clustering helps in the induction of the natural word classes leading to a principled way of designing POS tagsets. We compare different network construction techniques and clustering algorithms based on the cohesiveness of the word clusters. Cohesiveness is measured against two gold-standard tagsets by means of the novel metric of tag-entropy. The approach presented here is a generic one that can be easily extended to any language.".
- 309 hasAuthorList authorList.
- 309 hasTopic Linguistics.
- 309 isPartOf proceedings.
- 309 keyword "Acquisition, Machine Learning".
- 309 keyword "Morphology".
- 309 keyword "Tagging".
- 309 title "Unsupervised Parts-of-Speech Induction for Bengali".
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- item-1 hasContent joydeep-nath.
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- item-2 hasContent monojit-choudhury.
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- item-3 hasContent animesh-mukherjee.
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- item-4 hasContent christian-biemann.
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- item-5 hasContent niloy-ganguly.
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- 313 creator michael-mohler.
- 313 creator rada-mihalcea.
- 313 type InProceedings.
- 313 label "Babylon Parallel Text Builder: Gathering Parallel Texts for Low-Density Languages".
- 313 sameAs 313.
- 313 abstract "This paper describes Babylon, a system that attempts to overcome the shortage of parallel texts in low-density languages by supplementing existing parallel texts with texts gathered automatically from the Web. In addition to the identification of entire Web pages, we also propose a new feature specifically designed to find parallel text chunks within a single document. Experiments carried out on the Quechua-Spanish language pair show that the system is successful in automatically identifying a significant amount of parallel texts on the Web. Evaluations of a machine translation system trained on this corpus indicate that the Web-gathered parallel texts can supplement manually compiled parallel texts and perform significantly better than the manually compiled texts when tested on other Web-gathered data.".
- 313 hasAuthorList authorList.
- 313 hasTopic Linguistics.
- 313 isPartOf proceedings.
- 313 keyword "Endangered languages".
- 313 keyword "LR Infrastructures and Architectures".
- 313 keyword "Multilinguality".
- 313 title "Babylon Parallel Text Builder: Gathering Parallel Texts for Low-Density Languages".
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- item-1 hasContent michael-mohler.
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- item-2 hasContent rada-mihalcea.
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- 315 creator alessandra-zarcone.
- 315 creator alessandro-lenci.
- 315 type InProceedings.
- 315 label "Computational Models for Event Type Classification in Context".
- 315 sameAs 315.
- 315 abstract "Verb lexical semantic properties are only one of the factors that contribute to the determination of the event type expressed by a sentence, which is instead the result of a complex interplay between the verb meaning and its linguistic context. We report on two computational models for the automatic identification of event type in Italian. Both models use linguistically-motivated features extracted from Italian corpora. The main goal of our experiments is to evaluate the contribution of different types of linguistic indicators to identify the event type of a sentence, as well as to model various cases of context-driven event type shift. In the first model, event type identification has been modelled as a supervised classification task, performed with Maximum Entropy classifiers. In the second model, Self-Organizing Maps have been used to define and identify event types in an unsupervised way. The interaction of various contextual factors in determining the event type expressed by a sentence makes event type identification a highly challenging task. Computational models can help us to shed new light on the real structure of event type classes as well as to gain a better understanding of context-driven semantic shifts.".
- 315 hasAuthorList authorList.
- 315 hasTopic Linguistics.
- 315 isPartOf proceedings.
- 315 keyword "Acquisition, Machine Learning".
- 315 keyword "Semantics".
- 315 keyword "Word Sense Disambiguation".
- 315 title "Computational Models for Event Type Classification in Context".
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