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- item-1 hasContent johan-bos.
- 722 creator judith-klavans.
- 722 creator rebecca-passonneau.
- 722 creator tae-yano.
- 722 creator tom-lippincott.
- 722 type InProceedings.
- 722 label "Relation between Agreement Measures on Human Labeling and Machine Learning Performance: Results from an Art History Domain".
- 722 sameAs 722.
- 722 abstract "We discuss factors that affect human agreement on a semantic labeling task in the art history domain, based on the results of four experiments where we varied the number of labels annotators could assign, the number of annotators, the type and amount of training they received, and the size of the text span being labeled. Using the labelings from one experiment involving seven annotators, we investigate the relation between interannotator agreement and machine learning performance. We construct binary classifiers and vary the training and test data by swapping the labelings from the seven annotators. First, we find performance is often quite good despite lower than recommended interannotator agreement. Second, we find that on average, learning performance for a given functional semantic category correlates with the overall agreement among the seven annotators for that category. Third, we find that learning performance on the data from a given annotator does not correlate with the quality of that annotator s labeling. We offer recommendations for the use of labeled data in machine learning, and argue that learners should attempt to accommodate human variation. We also note implications for large scale corpus annotation projects that deal with similarly subjective phenomena.".
- 722 hasAuthorList authorList.
- 722 hasTopic Linguistics.
- 722 isPartOf proceedings.
- 722 keyword "Acquisition, Machine Learning".
- 722 keyword "Document Classification, Text categorisation".
- 722 keyword "Semantics".
- 722 title "Relation between Agreement Measures on Human Labeling and Machine Learning Performance: Results from an Art History Domain".
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- item-1 hasContent rebecca-passonneau.
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- item-2 hasContent tom-lippincott.
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- item-4 hasContent judith-klavans.
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- 724 creator claire-brierley.
- 724 creator eric-atwell.
- 724 type InProceedings.
- 724 label "ProPOSEL: A Prosody and POS English Lexicon for Language Engineering".
- 724 sameAs 724.
- 724 abstract "ProPOSEL is a prototype prosody and PoS (part-of-speech) English lexicon for Language Engineering, derived from the following language resources: the computer-usable dictionary CUVPlus, the CELEX-2 database, the Carnegie-Mellon Pronouncing Dictionary, and the BNC, LOB and Penn Treebank PoS-tagged corpora. The lexicon is designed for the target application of prosodic phrase break prediction but is also relevant to other machine learning and language engineering tasks. It supplements the existing record structure for wordform entries in CUVPlus with syntactic annotations from rival PoS-tagging schemes, mapped to fields for default closed and open-class word categories and for lexical stress patterns representing the rhythmic structure of wordforms and interpreted as potential new text-based features for automatic phrase break classifiers. The current version of the lexicon comes as a textfile of 104052 separate entries and is intended for distribution with the Natural Language ToolKit; it is therefore accompanied by supporting Python software for manipulating the data so that it can be used for Natural Language Processing (NLP) and corpus-based research in speech synthesis and speech recognition.".
- 724 hasAuthorList authorList.
- 724 hasTopic Linguistics.
- 724 isPartOf proceedings.
- 724 keyword "Lexicon, lexical database".
- 724 keyword "Prosody".
- 724 keyword "Tagging".
- 724 title "ProPOSEL: A Prosody and POS English Lexicon for Language Engineering".
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- item-1 hasContent claire-brierley.
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- item-2 hasContent eric-atwell.
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- 725 creator alison-alvarez.
- 725 creator alon-lavie.
- 725 creator ariadna-font-llitjos.
- 725 creator christian-monson.
- 725 creator erik-peterson.
- 725 creator jaime-carbonell.
- 725 creator katharina-probst.
- 725 creator lori-levin.
- 725 creator robert-frederking.
- 725 creator roberto-aranovich.
- 725 creator vamshi-ambati.
- 725 type InProceedings.
- 725 label "Linguistic Structure and Bilingual Informants Help Induce Machine Translation of Lesser-Resourced Languages".
- 725 sameAs 725.
- 725 abstract "Producing machine translation (MT) for the many minority languages in the world is a serious challenge. Minority languages typically have few resources for building MT systems. For many minor languages there is little machine readable text, few knowledgeable linguists, and little money available for MT development. For these reasons, our research programs on minority language MT have focused on leveraging to the maximum extent two resources that are available for minority languages: linguistic structure and bilingual informants. All natural languages contain linguistic structure. And although the details of that linguistic structure vary from language to language, language universals such as context-free syntactic structure and the paradigmatic structure of inflectional morphology, allow us to learn the specific details of a minority language. Similarly, most minority languages possess speakers who are bilingual with the major language of the area. This paper discusses our efforts to utilize linguistic structure and the translation information that bilingual informants can provide in three sub-areas of our rapid development MT program: morphology induction, syntactic transfer rule learning, and refinement of imperfect learned rules.".
- 725 hasAuthorList authorList.
- 725 hasTopic Linguistics.
- 725 isPartOf proceedings.
- 725 keyword "Acquisition, Machine Learning".
- 725 keyword "Endangered languages".
- 725 keyword "Machine Translation, SpeechToSpeech Translation".
- 725 title "Linguistic Structure and Bilingual Informants Help Induce Machine Translation of Lesser-Resourced Languages".
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