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Evaluating semantic models with word-sentence relatedness
[article]
2017
arXiv
pre-print
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A data set for evaluating semantic models was developed consisting of 775 English word-sentence pairs, each annotated for semantic relatedness by human raters engaged in a Maximum Difference Scaling (MDS ...
Semantic textual similarity (STS) systems are designed to encode and evaluate the semantic similarity between words, phrases, sentences, and documents. ...
Here we present a new data set for evaluating models of conceptual knowledge based on the relatedness between words and sentences. ...
arXiv:1603.07253v2
fatcat:2wrspxwbu5d5xngoammrvlclpe
FBK-TR: SVM for Semantic Relatedeness and Corpus Patterns for RTE
2014
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)
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This paper reports the description and scores of our system, FBK-TR, which participated at the SemEval 2014 task #1 "Evaluation of Compositional Distributional Semantic Models on Full Sentences through ...
Semantic Relatedness and Entailment". ...
Licence details: http://creativecommons.org/licenses/by/4.0/ At SemEval 2014, the Task #1 "Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and ...
doi:10.3115/v1/s14-2047
dblp:conf/semeval/VoPC14
fatcat:sd3fh55mr5ew7ifhxkdovxu5uu
Unsupervised Text Segmentation Using Semantic Relatedness Graphs
2016
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics
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In this paper, we present a novel unsupervised algorithm for linear text segmentation (TS) that exploits word embeddings and a measure of semantic relatedness of short texts to construct a semantic relatedness ...
Semantically coherent segments are then derived from maximal cliques of the relatedness graph. ...
GRAPHSEG employs word embeddings and extends a measure of semantic relatedness to construct a relatedness graph with edges established between semantically related sentences. ...
doi:10.18653/v1/s16-2016
dblp:conf/starsem/GlavasNP16
fatcat:m6l6oboshnd4vh3qp3twm73s2q
UoW: NLP techniques developed at the University of Wolverhampton for Semantic Similarity and Textual Entailment
2014
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)
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Our system performed satisfactorily and obtained 0.711 Pearson correlation for the semantic relatedness task and 78.52% accuracy for the textual entailment task. ...
We proposed a machine learning approach which is based on features extracted using Typed Dependencies, Paraphrasing, Machine Translation evaluation metrics, Quality Estimation metrics and Corpus Pattern ...
Additionally, we would like to combine our techniques for measuring relatedness and entailment with MT evaluation techniques. ...
doi:10.3115/v1/s14-2139
dblp:conf/semeval/GuptaBMO14
fatcat:7eihg6a5ufcjxmln3nsjdxigoa
BUAP: Evaluating Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment
2014
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)
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The run submitted is a supervised version based on two classification models: 1) We used logistic regression for determining the semantic relatedness between a pair of sentences, and 2) We employed support ...
The behaviour for the second subtask (textual entailment) obtained much better performance than the one evaluated at the first subtask (relatedness), ranking our approach in the 7th position of 18 teams ...
Semeval-2014 task 1: Evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment. ...
doi:10.3115/v1/s14-2021
dblp:conf/semeval/LeonVPTB14
fatcat:flkudjmbdbbtpe6veluy4z5je4
SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment
2014
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)
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Systems were presented with pairs of sentences and were evaluated on their ability to predict human judgments on (i) semantic relatedness and (ii) entailment. ...
This paper presents the task on the evaluation of Compositional Distributional Semantics Models on full sentences organized for the first time within SemEval-2014. ...
Introduction Distributional Semantic Models (DSMs) approximate the meaning of words with vectors summarizing their patterns of co-occurrence in corpora. ...
doi:10.3115/v1/s14-2001
dblp:conf/semeval/MarelliBBBMZ14
fatcat:smqlwhf4lffabgvvm4sggtqnx4
Improvement of query-based text summarization using word sense disambiguation
2019
Complex & Intelligent Systems
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The drawback with current methods is that while finding semantic relatedness between input text and query, in general they do not consider the sense of the words present in the input text sentences and ...
The correct sense for each word is being used while finding semantic relatedness between input text and query. ...
Find sentence relatedness score Find out the semantic relatedness score between the query words and expanded query words with words of the important sentences using the Algorithm 2. ...
doi:10.1007/s40747-019-0115-2
fatcat:uwmim63nlngvlgrlvpve27bmam
Retrofitting Contextualized Word Embeddings with Paraphrases
[article]
2019
arXiv
pre-print
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To enhance the stability of contextualized word embedding models, we propose an approach to retrofitting contextualized embedding models with paraphrase contexts. ...
Experiments show that the retrofitted model significantly outperforms the original ELMo on various sentence classification and language inference tasks. ...
Semantic relatedness tasks The semantic relatedness tasks include SICK-R (Marelli et al., 2014) and the STS Benchmark dataset (Cer et al., 2017) , which comprise pairs of sentences annotated with ...
arXiv:1909.09700v1
fatcat:wodactnxyra3tefpys4hfbovwa
Retrofitting Contextualized Word Embeddings with Paraphrases
2019
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
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To enhance the stability of contextualized word embedding models, we propose an approach to retrofitting contextualized embedding models with paraphrase contexts. ...
Experiments show that the retrofitted model significantly outperforms the original ELMo on various sentence classification and language inference tasks. ...
Semantic relatedness tasks The semantic relatedness tasks include SICK-R (Marelli et al., 2014) and the STS Benchmark dataset (Cer et al., 2017) , which comprise pairs of sentences annotated with ...
doi:10.18653/v1/d19-1113
dblp:conf/emnlp/ShiCZC19
fatcat:6ekqg2h2hncenmbmrnvihlerei
Polish evaluation dataset for compositional distributional semantics models
2017
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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The designed procedure is verified on Polish, a fusional language with a relatively free word order, and contributes to building a Polish evaluation dataset. ...
The resource consists of 10K sentence pairs which are human-annotated for semantic relatedness and entailment. ...
The goal of the task was to evaluate CDS models of English in terms of semantic relatedness and entailment on proper sentences from the SICK corpus. ...
doi:10.18653/v1/p17-1073
dblp:conf/acl/WroblewskaK17
fatcat:xr32ifjpnvhvhn7wdqtcgyearm
Information-based methods for evaluating the semantics of automatically generated test items
2016
Artificial intelligence research
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Model (CDSM) to measure the semantic relatedness among the pool of automatically generated items. ...
We illustrated our approach using eleven item models from the medical education domain, and discussed the possible applications to advance the AIG research. ...
When more than two sentences (i.e., items) are compared, the pairwise ISH indices need to be computed and then averaged for evaluation of semantic relatedness. ...
doi:10.5430/air.v6n1p69
fatcat:7yf2xh3terhuvb37ymsd4c2ria
SEBF: A Single-Chain based Extension Model of Blockchain for Fintech
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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The traditional blockchain has the shortcoming that a single-chain can only deal with one or a few specific data types. ...
The research question of how to make blockchain be able to deal with various data types has not been well studied. ...
. • Deep statement semantic comparison with GNN-based sentence encoder • Clause semantic relatedness detection with neural attention model. ...
doi:10.24963/ijcai.2020/613
dblp:conf/ijcai/GuoAGS20
fatcat:e24acrutpngyxajez5bs47bxdy
What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study
[article]
2021
arXiv
pre-print
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Here for the first time, we introduce a dataset of semantic relatedness for sentence pairs. ...
This dataset, STR-2021, has 5,500 English sentence pairs manually annotated for semantic relatedness using a comparative annotation framework. ...
Evaluating Sentence Representation Models using STR-2021 Since STR-2021 captures a wide range of finegrained relations that exist between sentences, it is a valuable asset in evaluating sentence representation ...
arXiv:2110.04845v1
fatcat:wlyamle475gnloyudoclohrz3m
Evaluation of Sentence Representations in Polish
[article]
2020
arXiv
pre-print
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We consider classic word embedding models, recently developed contextual embeddings and multilingual sentence encoders, showing strengths and weaknesses of specific approaches. ...
In this study, we introduce two new Polish datasets for evaluating sentence embeddings and provide a comprehensive evaluation of eight sentence representation methods including Polish and multilingual ...
Table 1: Evaluation of sentence representations on four classification tasks and one semantic relatedness task (SICK-R). For classification, we report accuracy of each model. ...
arXiv:1910.11834v2
fatcat:vh3v5mojqrgk5byulmvecrv7g4
A Domain Independent Semantic Measure for Keyword Sense Disambiguation
2021
Zenodo
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Our approach grounds on a semantic relatedness measure between words and concepts, and explores different disambiguation algorithms to study the contribution of both word and sentence-level representations ...
WSD techniques typically require well-formed sentences as context to operate, as well as pre-defined catalogues of word senses. ...
Results: In general, using the angular distance to calculate relatedness between pairs of words offers semantic correlation with the human judgment. ...
doi:10.5281/zenodo.4631684
fatcat:vdu3jzuzonfvzejc4dwewepkwy
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