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Hits 1 – 17 of 17

1
Enhancing Descriptive Image Captioning with Natural Language Inference ...
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2
Detecting Speaker Personas from Conversational Texts ...
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3
Unsupervised Conversation Disentanglement through Co-Training ...
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4
Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialogue State Tracking ...
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5
SemEval-2021 Task 12: Learning with Disagreements
Uma, Alexandra; Fornaciari, Tommaso; Dumitrache, Anca. - : Association for Computational Linguistics, 2021
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6
Exploring End-to-End Differentiable Natural Logic Modeling ...
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7
SemEval-2020 Task 5: Counterfactual Recognition ...
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8
SemEval-2020 Task 2: Predicting multilingual and cross-lingual (graded) lexical entailment
Glavaš, Goran; Vulić, Ivan; Korhonen, Anna. - : Association for Computational Linguistics, 2020
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9
Knowledge graph and semantic computing : knowledge computing and language understanding : 4th China Conference, CCKS 2019, Hangzhou, China, August 24-27, 2019 : revised selected papers
Zhu, Xiaoyan (Herausgeber); Qin, Bing (Herausgeber); Qian, LongHua (Herausgeber). - Singapore : Springer, 2019
BLLDB
UB Frankfurt Linguistik
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10
Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge ...
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11
Learning Cross-modal Context Graph for Visual Grounding ...
Liu, Yongfei; Wan, Bo; Zhu, Xiaodan. - : arXiv, 2019
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12
Logographic Subword Model for Neural Machine Translation ...
Fang, Yihao; Zheng, Rong; Zhu, Xiaodan. - : arXiv, 2018
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13
Commonsense Knowledge Enhanced Embeddings for Solving Pronoun Disambiguation Problems in Winograd Schema Challenge ...
Liu, Quan; Jiang, Hui; Ling, Zhen-Hua. - : arXiv, 2016
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14
NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets ...
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15
Summarizing Spoken Documents Through Utterance Selection
Zhu, Xiaodan. - 2010
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16
Analysis of Polarity Information in Medical Text
Niu, Yun; Zhu, Xiaodan; Li, Jianhua; Hirst, Graeme. - : American Medical Informatics Association, 2005
Abstract: Knowing the polarity of clinical outcomes is important in answering questions posed by clinicians in patient treatment. We treat analysis of this information as a classification problem. Natural language processing and machine learning techniques are applied to detect four possibilities in medical text: no outcome, positive outcome, negative outcome, and neutral outcome. A supervised learning method is used to perform the classification at the sentence level. Five feature sets are constructed: unigrams, bigrams, change phrases, negations, and categories. The performance of different combinations of feature sets is compared. The results show that generalization using the category information in the domain knowledge base Unified Medical Language System is effective in the task. The effect of context information is significant. Combining linguistic features and domain knowledge leads to the highest accuracy.
Keyword: Article
URL: http://www.ncbi.nlm.nih.gov/pubmed/16779104
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1560818
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17
Summarizing Spoken Documents Through Utterance Selection
Zhu, Xiaodan. - NO_RESTRICTION
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