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Evaluating semantic models with word-sentence relatedness [article]

Kimberly Glasgow, Matthew Roos, Amy Haufler, Mark Chevillet, Michael Wolmetz
2017 arXiv   pre-print
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

Ngoc Phuoc An Vo, Octavian Popescu, Tommaso Caselli
2014 Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)  
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

Goran Glavaš, Federico Nanni, Simone Paolo Ponzetto
2016 Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics  
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

Rohit Gupta, Hanna Bechara, Ismail El Maarouf, Constantin Orasan
2014 Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)  
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

Saul Leon, Darnes Vilariño, David Pinto, Mireya Tovar, Beatriz Beltrán
2014 Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)  
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

Marco Marelli, Luisa Bentivogli, Marco Baroni, Raffaella Bernardi, Stefano Menini, Roberto Zamparelli
2014 Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)  
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

Nazreena Rahman, Bhogeswar Borah
2019 Complex & Intelligent Systems  
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]

Weijia Shi, Muhao Chen, Pei Zhou, Kai-Wei Chang
2019 arXiv   pre-print
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

Weijia Shi, Muhao Chen, Pei Zhou, Kai-Wei Chang
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)  
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

Alina Wróblewska, Katarzyna Krasnowska-Kieraś
2017 Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
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

Syed Latifi, Mark Gierl, Ren Wang, Hollis Lai, Andong Wang
2016 Artificial intelligence research  
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

Yimu Ji, Weiheng Gu, Fei Chen, Xiaoying Xiao, Jing Sun, Shangdong Liu, Jing He, Yunyao Li, Kaixiang Zhang, Fen Mei, Fei Wu
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
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]

Mohamed Abdalla, Krishnapriya Vishnubhotla, Saif M. Mohammad
2021 arXiv   pre-print
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]

Sławomir Dadas, Michał Perełkiewicz, Rafał Poświata
2020 arXiv   pre-print
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

María G. Buey, Carlos Bobed, Jorge Gracia, Eduardo Mena
2021 Zenodo  
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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