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Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss ...
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QED: A Framework and Dataset for Explanations in Question Answering ...
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TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages ...
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QED: A Framework and Dataset for Explanations in Question Answering ...
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Natural Questions: A Benchmark for Question Answering Research
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In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 453-466 (2019) (2019)
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Predicting the impact of scientific concepts using full‐text features
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Learning Dictionaries for Named Entity Recognition using Minimal Supervision ...
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Experiments With Spectral Learning of Latent-Variable PCFGs
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In: Statistics Papers (2013)
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Cognitive Perspectives On English Word Order
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In: http://rave.ohiolink.edu/etdc/view?acc_num=osu1343315752 (2012)
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They do be anxious about their speech: Performance and Perceptions of Authenticity in Irish-Newfoundland English
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In: The English Languages: History, Diaspora, Culture; Vol 3 (2012) ; 1929-5855 (2012)
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Depth and distance perception of dentists and dental students
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Dialect Recognition Using a Phone-GMM-Supervector-Based SVM Kernel: Presentation Slides
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Dialect Recognition Using a Phone-GMM-Supervector-Based SVM Kernel
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Abstract:
In this paper, we introduce a new approach to dialect recognition which relies on the hypothesis that certain phones are realized differently across dialects. Given a speaker’s utterance, we first obtain the most likely phone sequence using a phone recognizer. We then extract GMM Supervectors for each phone instance. Using these vectors, we design a kernel function that computes the similarities of phones between pairs of utterances. We employ this kernel to train SVM classifiers that estimate posterior probabilities, used during recognition. Testing our approach on four Arabic dialects from 30s cuts, we compare our performance to five approaches: PRLM; GMM-UBM; our own improved version of GMM-UBM which employs fMLLR adaptation; our recent discriminative phonotactic approach; and a state-of-the-art system: SDC-based GMM-UBM discriminatively trained. Our kernel-based technique outperforms all these previous approaches; the overall EER of our system is 4.9%.
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Keyword:
Computer science; Information technology; Linguistics
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URL: https://doi.org/10.7916/D81J9K8W
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On dual decomposition and linear programming relaxations for natural language processing
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In: MIT web domain (2010)
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Dual decomposition for parsing with non-projective head automata
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In: MIT web domain (2010)
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