DE eng

Search in the Catalogues and Directories

Page: 1 2 3
Hits 21 – 40 of 49

21
How much does a word weigh? Weighting word embeddings for word sense induction ...
BASE
Show details
22
Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction ...
BASE
Show details
23
RUSSE: The First Workshop on Russian Semantic Similarity ...
BASE
Show details
24
An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages ...
BASE
Show details
25
Enriching Frame Representations with Distributionally Induced Senses ...
BASE
Show details
26
Unsupervised semantic frame induction using triclustering
Panchenko, Alexander; Kutuzov, Andrei; Ponzetto, Simone Paolo. - : Association for Computational Linguistics, 2018
BASE
Show details
27
An unsupervised word sense disambiguation system for under-resourced languages
Ustalov, Dmitry; Teslenko, Denis; Panchenko, Alexander. - : European Language Resources Association, ELRA-ELDA, 2018
BASE
Show details
28
RUSSE'2018 : a shared task on word sense induction for the Russian language
BASE
Show details
29
Negative Sampling Improves Hypernymy Extraction Based on Projection Learning ...
BASE
Show details
30
Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation ...
BASE
Show details
31
Negative Sampling Improves Hypernymy Extraction Based on Projection Learning ...
BASE
Show details
32
A Framework for Enriching Lexical Semantic Resources with Distributional Semantics ...
BASE
Show details
33
Making Sense of Word Embeddings ...
BASE
Show details
34
Watset: Automatic Induction of Synsets from a Graph of Synonyms ...
Abstract: This paper presents a new graph-based approach that induces synsets using synonymy dictionaries and word embeddings. First, we build a weighted graph of synonyms extracted from commonly available resources, such as Wiktionary. Second, we apply word sense induction to deal with ambiguous words. Finally, we cluster the disambiguated version of the ambiguous input graph into synsets. Our meta-clustering approach lets us use an efficient hard clustering algorithm to perform a fuzzy clustering of the graph. Despite its simplicity, our approach shows excellent results, outperforming five competitive state-of-the-art methods in terms of F-score on three gold standard datasets for English and Russian derived from large-scale manually constructed lexical resources. ... : 12 pages, 3 figures, 6 tables, accepted to ACL 2017 ...
Keyword: 68T50; Computation and Language cs.CL; FOS Computer and information sciences; I.2.6; I.5.3; I.2.4
URL: https://arxiv.org/abs/1704.07157
https://dx.doi.org/10.48550/arxiv.1704.07157
BASE
Hide details
35
Human and Machine Judgements for Russian Semantic Relatedness ...
BASE
Show details
36
Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl ...
BASE
Show details
37
Unsupervised does not mean uninterpretable : the case for word sense induction and disambiguation
Biemann, Chris; Ponzetto, Simone Paolo; Ruppert, Eugen. - : Association for Computational Linguistics, 2017
BASE
Show details
38
Unsupervised, knowledge-free, and interpretable word sense disambiguation
Faralli, Stefano; Panchenko, Alexander; Marten, Fide. - : Association for Computational Linguistics, 2017
BASE
Show details
39
The ContrastMedium algorithm : taxonomy induction from noisy knowledge graphs with just a few links
Faralli, Stefano; Panchenko, Alexander; Biemann, Chris. - : Association for Computational Linguistics, 2017
BASE
Show details
40
Negative sampling improves hypernymy extraction based on projection learning
Panchenko, Alexander; Arefyev, Nikolay; Ustalov, Dmitry. - : Association for Computational Linguistics, 2017
BASE
Show details

Page: 1 2 3

Catalogues
0
0
0
0
1
0
0
Bibliographies
0
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
48
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern