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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 Page: 1 2 Hits 1 – 20 of 26 1 Semi-Supervised and Unsupervised Sense Annotation via Translations ... Hauer, Bradley; Kondrak, Grzegorz; Luan, Yixing. - : arXiv, 2021 BASE Show details 2 WiC = TSV = WSD: On the Equivalence of Three Semantic Tasks ... Hauer, Bradley; Kondrak, Grzegorz. - : arXiv, 2021 BASE Show details 3 One Sense Per Translation ... Hauer, Bradley; Kondrak, Grzegorz. - : arXiv, 2021 BASE Show details 4 Synonymy = Translational Equivalence ... Hauer, Bradley; Kondrak, Grzegorz. - : arXiv, 2020 BASE Show details 5 Using Translations to Distinguish Between Homonymy and Polysemy Habibi, Amir Ahmad. - : University of Alberta. Department of Computing Science., 2020 BASE Show details 6 Leveraging Translations for Word Sense Disambiguation Luan, Yixing. - : University of Alberta. Department of Computing Science., 2020 BASE Show details 7 Assessing the importance of several acoustic properties to the perception of spontaneous speech Podlubny, Ryan G.; Nearey, Terrance M.; Kondrak, Grzegorz. - 2018 BASE Show details 8 Relation Extraction and its Application to Question Answering Xu, Ying. - : University of Alberta. Department of Computing Science., 2017 Abstract: Degree: Doctor of Philosophy ; Abstract: Information extraction, extracting structured information from text, is a vital component for many natural language tasks such as question answering. It generally consists of two components: (1) named entity recognition (NER), identifying noun phrases that are names of organizations, persons, or countries; and (2) relation extraction, extracting relations between entities. In this dissertation, we assume the entities are given, and concentrate on the relation extraction task. Traditional relation extraction task seeks to confirm a predefined set of relations in a text, such as the employment or family relation. These systems are difficult to extend by including additional relations. In contrast, the open information extraction (Open IE) task attempts to extract all relations, using words in sentences to represent the relations. My dissertation focuses on Open IE. We first proposed a tree kernel based-Open IE system that achieved state of the art performance. One advantage of the tree kernel model is that it exploits information in syntactic parse trees without feature engineering. After observing the importance of words in relation extraction, we then incorporated word embeddings into the tree kernel and improved the system’s performance. However, previous systems have not considered implicit relations, i.e., relations implied in noun phrase structures such as Germany’s people, Google Images, and Shakespeare’s book. We call this type of structure nested named entities. To study the implicit relation phenomenon, we automatically extracted thousands of instances of training data from Wikipedia. We demonstrate the feasibility of recovering implicit relations with a supervised classification model. Our data and model provides a baseline for future work on this task. Last but not least, to show the effect of our relation extraction systems, we built an Open IE-based question answering system and achieved promising results. Our analysis indicates the weakness of the current Open IE systems, the role of our information extraction results, and gives directions for improvement. Keyword: Implicit relation extraction; Information extraction; Question answering URL: http://hdl.handle.net/10402/era.43704 https://doi.org/10.7939/R3QB9VJ17 https://era.library.ualberta.ca/items/0b8dc433-f625-480f-accc-b4c825da4110 BASE Hide details 9 Identifying Cognate Sets Across Dictionaries of Related Languages St Arnaud, Adam, J.J.. - : University of Alberta. Department of Computing Science., 2017 BASE Show details 10 Modelling phonetic reduction in a corpus of spoken English using Random Forests and Mixed-Effects Regression Dilts, Philip C. - : University of Alberta. Department of Linguistics., 2013 BASE Show details 11 Modelling phonetic reduction in a corpus of spoken English using Random Forests and Mixed-Effects Regression Dilts, Philip C. - : University of Alberta. Department of Linguistics., 2013 BASE Show details 12 Indexing and Querying Natural Language Text Chubak, Pirooz. - : University of Alberta. Department of Computing Science., 2012 BASE Show details 13 Indexing and Querying Natural Language Text Chubak, Pirooz. - : University of Alberta. Department of Computing Science., 2012 BASE Show details 14 Totozoquean Kondrak, Grzegorz; Watters, James K.; Wichmann, Søren... In: International journal of American linguistics. - Chicago : Univ. of Chicago Press 77 (2011) 3, 323-372 BLLDB OLC Linguistik Show details 15 3 Totozoquean Brown, Cecil H; Beck, David; Kondrak, Grzegorz... In: International journal of American linguistics. - Chicago : Univ. of Chicago Press 77 (2011) 4, 323-372 OLC Linguistik Show details 16 Totozoquean Beck, David; Brown, Cecil H.; Kondrak, Grzegorz. - : The University of Chicago Press, 2011 BASE Show details 17 The Application of Chordal Graphs to Inferring Phylogenetic Trees of Languages Enright, Jessica; Kondrak, Grzegorz. - 2011 BASE Show details 18 Leveraging supplementary transcriptions and transliterations via re-ranking Bhargava, Aditya. - : University of Alberta. Department of Computing Science., 2011 BASE Show details 19 Leveraging supplementary transcriptions and transliterations via re-ranking Bhargava, Aditya. - : University of Alberta. Department of Computing Science., 2011 BASE Show details 20 Letter-phoneme alignment: an exploration Jiampojamarn, Sittichai; Kondrak, Grzegorz In: Association for Computational Linguistics. Proceedings of the conference. - Stroudsburg, Penn. : ACL 48 (2010) 1, 780-788 BLLDB Show details Page: 1 2

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