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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 – 12 of 12 1 Political analytics on election candidates and their parties in context of the US Presidential elections 2020 Sorathiya, Rakshit. - : Laurentian University of Sudbury, 2021 BASE Show details 2 Twitter sentiment analysis of the 2019 Indian election Motisariya, Jaydeep. - : Laurentian University of Sudbury, 2020 BASE Show details 3 MiNgMatch—A Fast N-gram Model for Word Segmentation of the Ainu Language Karol Nowakowski; Michal Ptaszynski; and Fumito Masui In: Information ; Volume 10 ; Issue 10 (2019) Abstract: Word segmentation is an essential task in automatic language processing for languages where there are no explicit word boundary markers, or where space-delimited orthographic words are too coarse-grained. In this paper we introduce the MiNgMatch Segmenter&mdash ; a fast word segmentation algorithm, which reduces the problem of identifying word boundaries to finding the shortest sequence of lexical n-grams matching the input text. In order to validate our method in a low-resource scenario involving extremely sparse data, we tested it with a small corpus of text in the critically endangered language of the Ainu people living in northern parts of Japan. Furthermore, we performed a series of experiments comparing our algorithm with systems utilizing state-of-the-art lexical n-gram-based language modelling techniques (namely, Stupid Backoff model and a model with modified Kneser-Ney smoothing), as well as a neural model performing word segmentation as character sequence labelling. The experimental results we obtained demonstrate the high performance of our algorithm, comparable with the other best-performing models. Given its low computational cost and competitive results, we believe that the proposed approach could be extended to other languages, and possibly also to other Natural Language Processing tasks, such as speech recognition. Keyword: Ainu language; endangered languages; language modelling; n-gram models; tokenization; under-resourced languages; word segmentation URL: https://doi.org/10.3390/info10100317 BASE Hide details 4 Multilingual Word Segmentation: Training Many Language-Specific Tokenizers Smoothly Thanks to the Universal Dependencies Corpus Moreau, Erwan; Vogel, Carl In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) ; https://hal.archives-ouvertes.fr/hal-01822151 ; Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), May 2018, Miyazaki, Japan (2018) BASE Show details 5 CoNLL 2017 and 2018 Shared Task Blind and Preprocessed Test Data Zeman, Daniel; Straka, Milan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2018 BASE Show details 6 Sentiment analysis on Twitter data using machine learning Patel, Ravikumar. - : Laurentian University of Sudbury, 2017 BASE Show details 7 Multi-word tokenization for natural language processing ... : Mehrworttokenisierung für maschinelle Sprachverarbeitung ... Michelbacher, Lukas. - : Universität Stuttgart, 2013 BASE Show details 8 Multi-word tokenization for natural language processing ; Mehrworttokenisierung für maschinelle Sprachverarbeitung Michelbacher, Lukas. - 2013 BASE Show details 9 JADT 2010: 10 th International Conference on Statistical Analysis of Textual Data Unsupervised learning of word separators with MDL Aris Xanthos; François Bavaud In: http://lexicometrica.univ-paris3.fr/jadt/jadt2010/allegati/JADT-2010-1123-1134_007-Xanthos.pdf BASE Show details 10 An Enhancement of Thai Text Retrieval Efficiency by Automatic Backward Transliteration Navapat Khantonthong; Asanee Kawtrakul; Yuen Poovarawan In: http://naist.cpe.ku.ac.th/downloads/publications/2000/An_Enhancement_of_Thai_Text_Retrieval_Efficiency_by_Automatic_Backward_Transliteration.pdf BASE Show details 11 A Word-Finding Automaton for Chinese Sentence Tokenization Hong-I Ng; Kim-teng Lua In: http://cslp.comp.nus.edu.sg/luakt/paper/SUB01.ps BASE Show details 12 An Empirical Study of Tokenization Strategies for Biomedical Information Retrieval Jing Jiang; Chengxiang Zhai In: http://sifaka.cs.uiuc.edu/czhai/pub/ir-tok.pdf BASE Show details

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