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Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations ...
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Mapping probability word problems to executable representations ...
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Interlocutors’ Age Impacts Teenagers’ Online Writing Style: Accommodation in Intra- and Intergenerational Online Conversations
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In: Front Artif Intell (2021)
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The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
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In: Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-02892154 ; Language Resources and Evaluation Conference, ELDA/ELRA, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/en/ (2020)
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The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ...
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The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ...
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The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ...
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Neural Machine Translation of Artwork Titles Using Iconclass Codes ...
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A deep generative approach to native language identification ...
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Effective weakly supervised semantic frame induction using expression sharing in hierarchical hidden Markov models ...
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What makes a distributional context useful? Lexical diversity is more important than frequency ...
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Children Probably Store Short Rather Than Frequent or Predictable Chunks: Quantitative Evidence From a Corpus Study
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Overview of PAN 2019: Bots and Gender Profiling, Celebrity Profiling, Cross-domain Authorship Attribution and Style Change Detection
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Multilingual Cross-domain Perspectives on Online Hate Speech ...
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Patient representation learning and interpretable evaluation using clinical notes ...
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Lexical category acquisition is facilitated by uncertainty in distributional co-occurrences
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Abstract:
This paper analyzes distributional properties that facilitate the categorization of words into lexical categories. First, word-context co-occurrence counts were collected using corpora of transcribed English child-directed speech. Then, an unsupervised k-nearest neighbor algorithm was used to categorize words into lexical categories. The categorization outcome was regressed over three main distributional predictors computed for each word, including frequency, contextual diversity, and average conditional probability given all the co-occurring contexts. Results show that both contextual diversity and frequency have a positive effect while the average conditional probability has a negative effect. This indicates that words are easier to categorize in the face of uncertainty: categorization works best for words which are frequent, diverse, and hard to predict given the co-occurring contexts. This shows how, in order for the learner to see an opportunity to form a category, there needs to be a certain degree of uncertainty in the co-occurrence pattern.
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Keyword:
Research Article
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URL: https://doi.org/10.1371/journal.pone.0209449 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6310260/ http://www.ncbi.nlm.nih.gov/pubmed/30592738
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