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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 3 4 5 6...11 Hits 21 – 40 of 220 21 Cross-lingual parsing with polyglot training and multi-treebank learning: a Faroese case study Barry, James; Wagner, Joachim; Foster, Jennifer In: Barry, James orcid:0000-0003-3051-585X , Wagner, Joachim orcid:0000-0002-8290-3849 and Foster, Jennifer orcid:0000-0002-7789-4853 (2019) Cross-lingual parsing with polyglot training and multi-treebank learning: a Faroese case study. In: The 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019), 3 - 5 Nov 2019, Hong Kong, China. ISBN 978-1-950737-78-9 (2019) BASE Show details 22 Recovering Chinese Nonlocal Dependencies with a Generalized Categorial Grammar Duan, Manjuan In: http://rave.ohiolink.edu/etdc/view?acc_num=osu154622673336324 (2019) BASE Show details 23 Training corpus ssj500k 2.2 Krek, Simon; Dobrovoljc, Kaja; Erjavec, Tomaž. - : Centre for Language Resources and Technologies, University of Ljubljana, 2019 BASE Show details 24 Automatic syntactic analysis of learner English ... Huang, Yan. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 25 Parsimonious Vole : a Systemic Functional Parser for English ... Costetchi, Eugeniu. - : Universität Bremen, 2019 BASE Show details 26 GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks Može, Sara; Rohanian, Omid; Taslimipoor, Shiva. - : Association for Computational Linguistics, 2019 BASE Show details 27 Empty Categories Help Parse the Overt Sun, Weiwei In: Proceedings of the Society for Computation in Linguistics (2019) BASE Show details 28 Automatically Selecting the Best Dependency Annotation Design with Dynamic Oracles Wisniewski, Guillaume; Lacroix, Ophélie; Yvon, François In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; https://hal.archives-ouvertes.fr/hal-01813395 ; Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, Jun 2018, New Orleans, United States. pp.401 - 406, ⟨10.18653/v1/N18-2064⟩ (2018) BASE Show details 29 Multilingual Dependency Parsing for Low-Resource Languages: Case Studies on North Saami and Komi-Zyrian Lim, KyungTae; Partanen, Niko; Poibeau, Thierry In: LREC 2018 Proceedings ; Language Resource and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-01856178 ; Language Resource and Evaluation Conference, ELRA, May 2018, Miyazaki, Japan ; http://www.lrec-conf.org/proceedings/lrec2018/pdf/600.pdf (2018) BASE Show details 30 Quantifying training challenges of dependency parsers Aufrant, Lauriane; Wisniewski, Guillaume; Yvon, François In: Proceedings of the 27th International Conference on Computational Linguistics, ; International Conference on Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01907772 ; International Conference on Computational Linguistics, Aug 2018, Santa Fe, New Mexico, United States. pp.3191 - 3202 (2018) BASE Show details 31 Exploiting Dynamic Oracles to Train Projective Dependency Parsers on Non-Projective Trees Aufrant, Lauriane; Wisniewski, Guillaume; Yvon, François In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; https://hal.archives-ouvertes.fr/hal-01813394 ; Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, ACL, Jun 2018, New Orleans, United States. pp.413 - 419 (2018) BASE Show details 32 Training corpus SETimes.SR 1.0 Batanović, Vuk; Ljubešić, Nikola; Samardžić, Tanja. - : Regional Linguistic Data Initiative Centre ReLDI, 2018 BASE Show details 33 Training corpus ssj500k 2.1 Krek, Simon; Dobrovoljc, Kaja; Erjavec, Tomaž. - : Centre for Language Resources and Technologies, University of Ljubljana, 2018 BASE Show details 34 Training corpus hr500k 1.0 Ljubešić, Nikola; Agić, Željko; Klubička, Filip. - : Jožef Stefan Institute, 2018 BASE Show details 35 The role of approximate negators in modeling the automatic detection of negation in tweets Palomino, Norma E In: Dissertations - ALL (2018) Abstract: Although improvements have been made in the performance of sentiment analysis tools, the automatic detection of negated text (which affects negative sentiment prediction) still presents challenges. More research is needed on new forms of negation beyond prototypical negation cues such as “not” or “never.” The present research reports findings on the role of a set of words called “approximate negators,” namely “barely,” “hardly,” “rarely,” “scarcely,” and “seldom,” which, in specific occasions (such as attached to a word from the non-affirmative adverb “any” family), can operationalize negation styles not yet explored. Using a corpus of 6,500 tweets, human annotation allowed for the identification of 17 recurrent usages of these words as negatives (such as “very seldom”) which, along with findings from the literature, helped engineer specific features that guided a machine learning classifier in predicting negated tweets. The machine learning experiments also modeled negation scope (i.e. in which specific words are negated in the text) by employing lexical and dependency graph information. Promising results included F1 values for negation detection ranging from 0.71 to 0.89 and scope detection from 0.79 to 0.88. Future work will be directed to the application of these findings in automatic sentiment classification, further exploration of patterns in data (such as part-of-speech recurrences for these new types of negation), and the investigation of sarcasm, formal language, and exaggeration as themes that emerged from observations during corpus annotation. Keyword: dependency parsing; natural language processing; negation detection; sentiment analysis; Social and Behavioral Sciences; social media data; Twitter URL: https://surface.syr.edu/etd/867 https://surface.syr.edu/cgi/viewcontent.cgi?article=1868&context=etd BASE Hide details 36 Increasing NLP Parsing Efficiency with Chunking Anderson, Mark Dáibhidh; Vilares, David. - : M D P I AG, 2018 BASE Show details 37 CINTIL DependencyBank PREMIUM. A corpus of grammatical dependencies for Portuguese Carvalho, Rita de; Querido, Andreia; Campos, Marisa. - : European Language Resources Association, 2018 BASE Show details 38 Detecting Discourse-Independent Negated Forms of Public Textual Cyberbullying Power, Aurelia; Keane, Anthony; Nolan, Brian. - : Universitat Politècnica de València, 2018 BASE Show details 39 Utilizing Typed Dependency Subtree Patterns for Answer Sentence Generation in Question Answering Systems Naeem, A; Perera, R; Nand, P. - : Springer, 2017 BASE Show details 40 Delexicalized Word Embeddings for Cross-lingual Dependency Parsing Dehouck, Mathieu; Denis, Pascal In: EACL ; https://hal.inria.fr/hal-01590639 ; EACL, Apr 2017, Valencia, Spain. pp.241 - 250, ⟨10.18653/v1/E17-1023⟩ ; http://eacl2017.org/ (2017) BASE Show details Page: 1 2 3 4 5 6...11

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