Matches in ScholarlyData for { ?s ?p ?o. }
- 89 type InProceedings.
- 89 label "The JOS Morphosyntactically Tagged Corpus of Slovene".
- 89 sameAs 89.
- 89 abstract "The JOSmorphosyntactic resources for Slovene consist of the specifications, lexicon, and two corpora: jos100k, a 100,000 word balanced monolingual sampled corpus annotated with hand validated morphosyntactic descriptions (MSDs) and lemmas, and jos1M, the 1 million-word partially hand validated corpus. The two corpora have been sampled from the 600M-word Slovene reference corpus FidaPLUS. The JOS resources have a standardised encoding, with the MULTEXT-East-type morphosyntactic specifications and the corpora encoded according to the Text Encoding Initiative Guidelines P5. JOS resources are available as a dataset for research under the Creative Commons licence and are meant to facilitate developments of HLT for Slovene.".
- 89 hasAuthorList authorList.
- 89 hasTopic Linguistics.
- 89 isPartOf proceedings.
- 89 keyword "Corpus (creation, annotation, etc.)".
- 89 keyword "Standards for LRs".
- 89 keyword "Tagging".
- 89 title "The JOS Morphosyntactically Tagged Corpus of Slovene".
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- item-1 hasContent tomas-erjavec.
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- item-2 hasContent simon-krek.
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- 892 creator amiya-nayak.
- 892 creator diana-inkpen.
- 892 creator leanne-spracklin.
- 892 type InProceedings.
- 892 label "Using the Complexity of the Distribution of Lexical Elements as a Feature in Authorship Attribution".
- 892 sameAs 892.
- 892 abstract "Traditional Authorship Attribution models extract normalized counts of lexical elements such as nouns, common words and punctuation and use these normalized counts or ratios as features for author fingerprinting. The text is viewed as a bag-of-words and the order of words and their position relative to other words is largely ignored. We propose a new method of feature extraction which quantifies the distribution of lexical elements within the text using Kolmogorov complexity estimates. Testing carried out on blog corpora indicates that such measures outperform ratios when used as features in an SVM authorship attribution model. Moreover, by adding complexity estimates to a model using ratios, we were able to increase the F-measure by 5.2-11.8%".
- 892 hasAuthorList authorList.
- 892 hasTopic Linguistics.
- 892 isPartOf proceedings.
- 892 keyword "Document Classification, Text categorisation".
- 892 keyword "Information Extraction, Information Retrieval".
- 892 keyword "Text mining".
- 892 title "Using the Complexity of the Distribution of Lexical Elements as a Feature in Authorship Attribution".
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- item-1 type ListItem.
- item-1 hasContent leanne-spracklin.
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- item-2 type ListItem.
- item-2 hasContent diana-inkpen.
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- item-3 hasContent amiya-nayak.
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- 894 creator anil-kumar-singh.
- 894 creator harshit-surana.
- 894 creator kiran-pala.
- 894 type InProceedings.
- 894 label "Estimating the Resource Adaption Cost from a Resource Rich Language to a Similar Resource Poor Language".
- 894 sameAs 894.
- 894 abstract "Developing resources which can be used for Natural Language Processing is an extremely difficult task for any language, but is even more so for less privileged (or less computerized) languages. One way to overcome this difficulty is to adapt the resources of a linguistically close resource rich language. In this paper we discuss how the cost of such adaption can be estimated using subjective and objective measures of linguistic similarity for allocating financial resources, time, manpower etc. Since this is the first work of its kind, the method described in this paper should be seen as only a preliminary method, indicative of how better methods can be developed. Corpora of several less computerized languages had to be collected for the work described in the paper, which was difficult because for many of these varieties there is not much electronic data available. Even if it is, it is in non-standard encodings, which means that we had to build encoding converters for these varieties. The varieties we have focused on are some of the varieties spoken in the South Asian region.".
- 894 hasAuthorList authorList.
- 894 hasTopic Linguistics.
- 894 isPartOf proceedings.
- 894 keyword "LR national/international projects, organizational/policy issues".
- 894 keyword "Multilinguality".
- 894 keyword "Typological databases".
- 894 title "Estimating the Resource Adaption Cost from a Resource Rich Language to a Similar Resource Poor Language".
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- item-1 hasContent anil-kumar-singh.
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- item-2 hasContent kiran-pala.
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- item-3 type ListItem.
- item-3 hasContent harshit-surana.
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- 896 creator dekai-wu.
- 896 creator marine-carpuat.
- 896 type InProceedings.
- 896 label "Evaluation of Context-Dependent Phrasal Translation Lexicons for Statistical Machine Translation".
- 896 sameAs 896.
- 896 abstract "We present new direct data analysis showing that dynamically-built context-dependent phrasal translation lexicons are more useful resources for phrase-based statistical machine translation (SMT) than conventional static phrasal translation lexicons, which ignore all contextual information. After several years of surprising negative results, recent work suggests that context-dependent phrasal translation lexicons are an appropriate framework to successfully incorporate Word Sense Disambiguation (WSD) modeling into SMT. However, this approach has so far only been evaluated using automatic translation quality metrics, which are important, but aggregate many different factors. A direct analysis is still needed to understand how context-dependent phrasal translation lexicons impact translation quality, and whether the additional complexity they introduce is really necessary. In this paper, we focus on the impact of context-dependent translation lexicons on lexical choice in phrase-based SMT and show that context-dependent lexicons are more useful to a phrase-based SMT system than a conventional lexicon. A typical phrase-based SMT system makes use of more and longer phrases with context modeling, including phrases that were not seen very frequently in training. Even when the segmentation is identical, the context-dependent lexicons yield translations that match references more often than conventional lexicons.".
- 896 hasAuthorList authorList.
- 896 hasTopic Linguistics.
- 896 isPartOf proceedings.
- 896 keyword "Lexicon, lexical database".
- 896 keyword "Machine Translation, SpeechToSpeech Translation".
- 896 title "Evaluation of Context-Dependent Phrasal Translation Lexicons for Statistical Machine Translation".
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