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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 Learning to Selectively Learn for Weakly-supervised Paraphrase Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ding , Kaize; Fan, Xing. - : Underline Science Inc., 2021 BASE Show details 2 MfeCNN: Mixture Feature Embedding Convolutional Neural Network for Data Mapping Li, Dingcheng; Huang, Ming; Li, Xiaodi. - 2018 BASE Show details 3 Assessing the Need of Discourse-Level Analysis in Identifying Evidence of Drug-Disease Relations in Scientific Literature Rastegar-Mojarad, Majid; Elayavilli, Ravikumar Komandur; Li, Dingcheng. - 2015 BASE Show details 4 Comprehensive temporal information detection from clinical text: medical events, time, and TLINK identification Sohn, Sunghwan; Wagholikar, Kavishwar B; Li, Dingcheng. - : BMJ Publishing Group Ltd, 2013 BASE Show details 5 ERD-MedLDA: Entity relation detection using supervised topic models with maximum margin learning LI, DINGCHENG; SOMASUNDARAN, SWAPNA; CHAKRABORTY, AMIT In: Natural language engineering. - Cambridge : Cambridge University Press 18 (2012) 2, 263-289 OLC Linguistik Show details 6 Unified Medical Language System term occurrences in clinical notes: a large-scale corpus analysis Wu, Stephen T; Liu, Hongfang; Li, Dingcheng. - : BMJ Publishing Group Ltd, 2012 BASE Show details 7 Towards a semantic lexicon for clinical natural language processing Liu, Hongfang; Wu, Stephen T.; Li, Dingcheng. - : American Medical Informatics Association, 2012 BASE Show details 8 Entity relation detection with Factorial Hidden Markov Models and Maximum Entropy Discriminant Latent Dirichlet Allocations. Li, Dingcheng. - 2012 Abstract: University of Minnesota Ph.D. dissertation. January 2012. Major: Linguistics. Advisors: Jeanette Gundel,WilliamSchuler. 1 computer file (PDF); xi, 124 pages. ; Coreference resolution (CR) and entity relation detection (ERD) aim at finding predefined relations between pairs of entities in text. CR focuses on resolving identity relations while ERD focuses on detecting non-identity relations. Both CR and ERD are important as they can potentially improve other natural language processing (NLP) related tasks such information retrieval and extraction, web-searching, and question answering and also enhance non-NLP tasks such as computer vision, database constructions or ontologies. In this thesis, I propose models to handle both coreference resolution (CR) and entity relation detection (ERD). Both systems are built onmachine learningmodels. The CR system is based on Factorial Hidden Markov Models (FHMMs). The ERD is based on Maximum Entropy Discriminant Latent Dirichlet Allocation (MEDLDA). The work on CR only resolves pronouns. It is a supervised system trained on annotated corpus. The basic idea is that the hidden states of FHMMs are an explicit short-term memory with an antecedent buffer containing recently described referents. Thus an observed pronoun can find its antecedent from the hidden buffer, or in terms of a generative model, the entries in the hidden buffer generate the corresponding pronouns. In the hidden buffer, all references are expressed as diverse features. In this work, besides the common gender, number, person and animacy, I convertedGivennessHierarchy and Centering Theories to probabilistic features, thus greatly improving the accuracy. A system implementing this model is evaluated on the ACE corpus and I2B2 medical corpus with promising performance. For ERD, a novel application of topic models is proposed to do this task. In order to make use of the latent semantics of text, the task of relation detection is reformulated as a topic modeling problem. Themotivation is to find underlying topics which are indicative of relations between named entities. The approach considers pairs of named entities and features associated with them as mini documents. The system, called ERD-MEDLDA, adapts Maximum Entropy Discriminant Latent Dirichlet Allocation (MedLDA) with mixed membership for relation detection. By using supervision, ERD-MedLDA is able to learn topic distributions indicative of relation types. Further, ERD-MEDLDA is a topicmodel that combines the benefits of both Maximum Likelihood Estimation (MLE) and Maximum Margin Estimation (MME), and themixed membership formulation enables the system to incorporate heterogeneous features. We incorporate diverse features into the system and perform experiments on the ACE 2005 corpus. Our approach achieves better overall performance for precision, recall and Fmeasuremetrics as compared to SVM-based and LDA-basedmodels. ERD-MedLDA also shows better overall performance than state-of-the-art kernels used previously for relation detection. Keyword: Coreference Resolution; Entity Relation Detection; Factorial Hidden Markov Models; Givenness Hierarchy and Centering Theory; Linguistics; Maximum Margin Estimation and Maximum Likelihood Estimation URL: http://purl.umn.edu/120893 BASE Hide details

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