• Refine your search:
  • 
    Keyword:
    
    • Computation and Language cs.CL (5)
    • FOS Computer and information sciences (5)
    • Machine Learning cs.LG (5)
    • Artificial Intelligence cs.AI (3)
    • Neural and Evolutionary Computing cs.NE (1)
  • 
    Creator / Publisher
  • 
    Year
  • 
    Medium
  • 
    Type:
    
    • Miscellaneous (3)
    • Article (2)
  • 
    BLLDB-Access:
    • free (5)
    • subject to license (0)

Search in the Catalogues and Directories

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 – 5 of 5 1 Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings ... Bollegala, Danushka. - : arXiv, 2022 BASE Show details 2 I Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product Reviews ... O'Neill, James; Rozenshtein, Polina; Kiryo, Ryuichi. - : arXiv, 2021 BASE Show details 3 Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction ... Bollegala, Danushka; Kiryo, Ryuichi; Tsujino, Kosuke; Yukawa, Haruki. - : arXiv, 2020 Abstract: Language-independent tokenisation (LIT) methods that do not require labelled language resources or lexicons have recently gained popularity because of their applicability in resource-poor languages. Moreover, they compactly represent a language using a fixed size vocabulary and can efficiently handle unseen or rare words. On the other hand, language-specific tokenisation (LST) methods have a long and established history, and are developed using carefully created lexicons and training resources. Unlike subtokens produced by LIT methods, LST methods produce valid morphological subwords. Despite the contrasting trade-offs between LIT vs. LST methods, their performance on downstream NLP tasks remain unclear. In this paper, we empirically compare the two approaches using semantic similarity measurement as an evaluation task across a diverse set of languages. Our experimental results covering eight languages show that LST consistently outperforms LIT when the vocabulary size is large, but LIT can produce ... : To appear in the 12th Language Resources and Evaluation (LREC 2020) Conference ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://arxiv.org/abs/2002.11004 https://dx.doi.org/10.48550/arxiv.2002.11004 BASE Hide details 4 Gender-preserving Debiasing for Pre-trained Word Embeddings ... Kaneko, Masahiro; Bollegala, Danushka. - : arXiv, 2019 BASE Show details 5 Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words ... Bollegala, Danushka; Hayashi, Kohei; Kawarabayashi, Ken-ichi. - : arXiv, 2017 BASE Show details

Mobile view All Catalogues UB Frankfurt Linguistik 0 IDS Mannheim 0 OLC Linguistik 0 UB Frankfurt Retrokatalog 0 DNB Subject Category Language 0 Institut für Empirische Sprachwissenschaft 0 Leibniz-Centre General Linguistics (ZAS) 0 Bibliographies BLLDB 0 BDSL 0 IDS Bibliografie zur deutschen Grammatik 0 IDS Bibliografie zur Gesprächsforschung 0 IDS Konnektoren im Deutschen 0 IDS Präpositionen im Deutschen 0 IDS OBELEX meta 0 MPI-SHH Linguistics Collection 0 MPI for Psycholinguistics 0 Linked Open Data catalogues Annohub 0 Online resources Link directory 0 Journal directory 0 Database directory 0 Dictionary directory 0 Open access documents BASE 5 Linguistik-Repository 0 IDS Publikationsserver 0 Online dissertations 0 Language Description Heritage 0