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New experiments in distributional representations of synonymy
2005
Proceedings of the Ninth Conference on Computational Natural Language Learning - CONLL '05
unpublished
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Recent work on the problem of detecting synonymy through corpus analysis has used the Test of English as a Foreign Language (TOEFL) as a benchmark. ...
We overcome this limitation by generating a TOEFL-like test using WordNet, containing thousands of questions and composed only of words occurring with sufficient corpus frequency to support sound distributional ...
Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors, and do not necessarily reflect the views of the U.S. Government. ...
doi:10.3115/1706543.1706548
fatcat:ykkngyhlnnaqtdszfqzyj3kpdu
Term representation with Generalized Latent Semantic Analysis
[chapter]
2007
Current issues in linguistic theory
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Our experiments demonstrate that GLSA term vectors efficiently capture semantic relations between terms and outperform related approaches on the synonymy test. ...
Document indexing and representation of termdocument relations are very important issues for document clustering and retrieval. ...
Landauer, Department of Psychology, University of Colorado. This research has been funded in part by contract #MDA904-03-C-0404 to Stuart K. ...
doi:10.1075/cilt.292.08mat
fatcat:feynf6rvdjau3b34sojl7xpxd4
Concurrent Learning of Semantic Relations
[article]
2018
arXiv
pre-print
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Preliminary results based on simple learning strategies and state-of-the-art distributional feature representations show that concurrent learning can lead to improvements in a vast majority of tested situations ...
multiple semantic relations (e.g. hypernymy vs. synonymy vs. random). ...
In distributional methods, the decision whether x is within a semantic relation with y is based on the distributional representation of these words following the distributional hypothesis (Harris, 1954 ...
arXiv:1807.10076v3
fatcat:hrqp3bkl45curfmxacuj3yuwra
Unraveling Antonym's Word Vectors through a Siamese-like Network
2019
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
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The approach makes use of the claim that the antonyms in common of a word tend to be synonyms. ...
Consequently, pairs of antonyms and synonyms may have similar word vectors. We present an approach to unravel antonymy and synonymy from word vectors based on a siamese network inspired approach. ...
Many research studies and experiments have focused on the construction of vector representations that deem antonymy. ...
doi:10.18653/v1/p19-1319
dblp:conf/acl/EtcheverryW19
fatcat:gogkuex7qfbyvdj6ya4asthmfe
Exploring the Representation of Word Meanings in Context: A Case Study on Homonymy and Synonymy
[article]
2021
arXiv
pre-print
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This paper presents a multilingual study of word meaning representations in context. ...
Experiments are performed in Galician, Portuguese, English, and Spanish, and both the dataset (with more than 3,000 evaluation items) and new models are freely released with this study. ...
Acknowledgments We would like to thank the anonymous reviewers for their valuable comments, and NVIDIA Corporation for the donation of a Titan Xp GPU. ...
arXiv:2106.13553v2
fatcat:fcuvoh6i7fcl3fhlpxyv2m2wny
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
[article]
2017
arXiv
pre-print
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The effectiveness of our approach is demonstrated with state-of-the-art results on semantic similarity datasets in six languages. ...
We next show that Attract-Repel-specialised vectors boost performance in the downstream task of dialogue state tracking (DST) across multiple languages. ...
In our experiments, the remaining six languages (HE, HR, SV, GA, VI, FA) serve as examples of lower-resource languages, as they have no monolingual synonymy constraints. ...
arXiv:1706.00374v1
fatcat:6ol3mrpjjrgb3bmzcwlzsz4oru
Counter-fitting Word Vectors to Linguistic Constraints
[article]
2016
arXiv
pre-print
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In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic ...
Applying this method to publicly available pre-trained word vectors leads to a new state of the art performance on the SimLex-999 dataset. ...
A new feature of this version is that it assigns relation types to its word pairs. We identify the Equivalence relation with synonymy and Exclusion with antonymy. ...
arXiv:1603.00892v1
fatcat:rhylbl3sfre7tgslno4b7msd2m
Counter-fitting Word Vectors to Linguistic Constraints
2016
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic ...
Applying this method to publicly available pre-trained word vectors leads to a new state of the art performance on the SimLex-999 dataset. ...
A new feature of this version is that it assigns relation types to its word pairs. We identify the Equivalence relation with synonymy and Exclusion with antonymy. ...
doi:10.18653/v1/n16-1018
dblp:conf/naacl/MrksicSTGRSVWY16
fatcat:tkgvcyo7m5ggjh2tcacjzbrbuq
Bag of Visual Words Method based on PLSA and Chi-Square Model for Object Category
2015
KSII Transactions on Internet and Information Systems
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The problem of visual words' synonymy and ambiguity always exist in the conventional bag of visual words (BoVW) model based object category methods. ...
Firstly, Probabilistic Latent Semantic Analysis (PLSA) is used to analyze the semantic co-occurrence probability of visual words, infer the latent semantic topics in images, and get the latent topic distributions ...
However, there exists some synonymy and ambiguity problems in visual words [13] [14] [15] as well as the seriously quantization error of compact histogram representation. ...
doi:10.3837/tiis.2015.07.017
fatcat:zivtlh3qvnaxrktymp4743or6e
Lexical semantic relatedness and online new event detection (poster session)
2000
Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '00
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Summary In this paper we propose a novel use for lexical chains as document representations within the Topic Detection domain.
Acknowledgments ...
Description of Experiment Our new approach to document representation is based on the idea of conceptual indexing using lexical chaining. ...
In particular, we are concerned with the huge increase in the availability of multiple news sources reporting essentially the same news. ...
doi:10.1145/345508.345623
dblp:conf/sigir/StokesHC00
fatcat:xffijz3ysvfynjobtuanlsuq7m
Using the Outlier Detection Task to Evaluate Distributional Semantic Models
2018
Machine Learning and Knowledge Extraction
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In this article, we define the outlier detection task and use it to compare neural-based word embeddings with transparent count-based distributional representations. ...
In general, syntax-based models tend to perform better than those based on bag-of-words for this specific task. Similar experiments were carried out for Portuguese with similar results. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/make1010013
fatcat:qrxf444cd5drbbk24dheolbobm
Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network
2017
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
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In addition to the lexical and syntactic information, we successfully integrate the distance between the related words along the syntactic path as a new pattern feature. ...
The results from classification experiments show that AntSyn-NET improves the performance over prior pattern-based methods. ...
The research was supported by the Ministry of Education and Training of the Socialist Republic of Vietnam (Scholarship 977/QD-BGDDT; Kim-Anh Nguyen), the DFG Collaborative Research Centre SFB 732 (Kim-Anh ...
doi:10.18653/v1/e17-1008
dblp:conf/eacl/NguyenWV17
fatcat:4ak2gw3yjrdlbkomgwxioudpve
A Neural Network Model for Efficient Antonymy-Synonymy Classification by Exploiting Co-occurrence Contexts and Word-Structure Patterns
2020
International Journal of Intelligent Engineering and Systems
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This task is hard because antonyms and synonyms tend to occur in highly similar contexts. Recent studies often focus on exploiting densevector representations of words to deal with this problem. ...
Antonymy and synonymy are basic semantic relations between words. Automatically distinguishing between antonymy and synonymy is an important task in natural language processing. ...
In the unsupervised approach, distributional measures are used to distinguish synonymy from antonymy in an unsupervised manner. ...
doi:10.22266/ijies2020.0229.15
fatcat:2h5wngxbf5axhb4z2jxoz4kl7a
Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network
[article]
2017
arXiv
pre-print
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In addition to the lexical and syntactic information, we successfully integrate the distance between the related words along the syntactic path as a new pattern feature. ...
The results from classification experiments show that AntSynNET improves the performance over prior pattern-based methods. ...
The research was supported by the Ministry of Education and Training of the Socialist Republic of Vietnam (Scholarship 977/QD-BGDDT; Kim-Anh Nguyen), the DFG Collaborative Research Centre SFB 732 (Kim-Anh ...
arXiv:1701.02962v1
fatcat:iswc47dgbbdito2vl2hz2u25ge
Entity Type Recognition Using an Ensemble of Distributional Semantic Models to Enhance Query Understanding
2016
2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC)
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2016 pre-print arXiv:1604.00933v1 fatcat:og6255pds5exnjlak2b4mxg6vm
Moreover, we achieve micro-averaged F 1 score of 97% using the proposed distributional representations ensemble. ...
We employ distributional semantic representations of query entities through two models: 1) contextual vectors generated from encyclopedic corpora like Wikipedia, and 2) high dimensional word embedding ...
After word2vec produces word distributional vectors, word synonymy vectors of search entities are generated as described in Section III.D. ...
doi:10.1109/compsac.2016.109
dblp:conf/compsac/ShalabyAKG16
fatcat:26qwomj5yrhf5fvkk5gs62v4ge
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