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Learning to Recognize Ancillary Information for Automatic Paraphrase Identification

Simone Filice, Alessandro Moschitti
2016 Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies  
Previous work on Automatic Paraphrase Identification (PI) is mainly based on modeling text similarity between two sentences.  ...  Engineering features for this new task is rather difficult, thus, we approach the problem by representing text with syntactic structures and applying tree kernels on them.  ...  Table 2 : 2 Results on Paraphrase Identification. skip-gram model applied to the UkWaC corpus (Ba- roni et al., 2009). Table 4 : 4 PI classifier performance using ATC.  ... 
doi:10.18653/v1/n16-1129 dblp:conf/naacl/FiliceM16 fatcat:nho3yi3wdvd3zcsmllbpulq2n4

Exploring the Recent Trends of Paraphrase Detection

Mohamed I., Wael H.
2019 International Journal of Computer Applications  
This study is to examine paraphrase detection (PD) for diagnostic purposes.  ...  The performance of the selected researches is assessed by how accurate the F-measures are in detecting paraphrase in Microsoft Research Paraphrase Corpus (MSPR).  ...  In [7] a software system that utilizes a recently developed lexico-syntactic method to the task of paraphrase identification was introduced.  ... 
doi:10.5120/ijca2019918317 fatcat:7sxb27g42bdclgmj3pyd7uefn4

SEMILAR: The Semantic Similarity Toolkit

Vasile Rus, Mihai C. Lintean, Rajendra Banjade, Nobal B. Niraula, Dan Stefanescu
2013 Annual Meeting of the Association for Computational Linguistics  
SEMILAR implements a number of algorithms for assessing the semantic similarity between two texts.  ...  Furthermore, it offers facilities for manual se-mantic similarity annotation by experts through its component SEMILAT (a SEMantic simILarity Annotation Tool).  ...  Acknowledgments This research was supported in part by Institute for Education Sciences under award R305A100875.  ... 
dblp:conf/acl/RusLBNS13 fatcat:eeltnqs6ezfi5k6aji2h7mr2ae

Paraphrase Identification on the Basis of Supervised Machine Learning Techniques [chapter]

Zornitsa Kozareva, Andrés Montoyo
2006 Lecture Notes in Computer Science  
This paper presents a machine learning approach for paraphrase identification which uses lexical and semantic similarity information.  ...  With the objective to increase the final performance of the system, we scrutinize the influence of the combination of lexical and semantic information, as well as techniques for classifier combination.  ...  For perfect similarity match, sim lin has value 1 and for completely dissimilar words 0.  ... 
doi:10.1007/11816508_52 fatcat:pggqpzuudnfsbfl7kzl3i7yap4

A Novel Approach for Developing Paraphrase Detection System using Machine Learning

Rudradityo Saha, G. Bharadwaja Kumar
2021 International Journal of Computer Applications  
The proposed paraphrase detection system has achieved comparable performance with existing paraphrase detection systems.  ...  Research Paraphrase (MSRP) Corpus and assessed on the same.  ...  paraphrase identification.  ... 
doi:10.5120/ijca2021921389 fatcat:clya63hxdvfz3kpmszhrjmhc2i

A Hybrid Model for Paraphrase Detection Combines pros of Text Similarity with Deep Learning

Mohamed I., Wael H., Hawaf Abdalhakim
2019 International Journal of Computer Applications  
Paraphrase detection (PD) is a very essential and important task in Natural language processing.  ...  The goal of paraphrase detection is to check whether two statements written in natural language have the identical semantic or not.  ...  A string metric is a metric that measures similarity or dissimilarity (distance) between two text strings for approximate string matching or comparison.  ... 
doi:10.5120/ijca2019919011 fatcat:jjl3modl6vbn7kfuddibziantu

Certainty factor model in paraphrase detection

Senem Kumova Metin, Bahar Karaoğlan, Tarık Kışla, Katira Soleymanzadeh
2021 Pamukkale University Journal of Engineering Sciences  
The proposed CF model in paraphrase identification is realized by utilizing the renowned paraphrase corpus of Microsoft Research (MSRP) [2] that is stated to be a standard resource in paraphrase identification  ...  Öz In this paper, we address the problem of uncertainty management in identification of paraphrase sentence pairs.  ...  Determining Value-ranges of Evidences In identification of paraphrase sentence pairs, for each evidence an evidence value that is actually a similarity score in a predefined range is calculated for the  ... 
doi:10.5505/pajes.2020.75350 fatcat:aw5geqmftnfrbpl4nbdpq3nrgq

Extracting Lexically Divergent Paraphrases from Twitter

Wei Xu, Alan Ritter, Chris Callison-Burch, William B. Dolan, Yangfeng Ji
2014 Transactions of the Association for Computational Linguistics  
We present MULTIP (Multi-instance Learning Paraphrase Model), a new model suited to identify paraphrases within the short messages on Twitter.  ...  We jointly model paraphrase relations between word and sentence pairs and assume only sentence-level annotations during learning.  ...  Government is authorized to reproduce and distribute reprints for governmental purposes.  ... 
doi:10.1162/tacl_a_00194 fatcat:54bwppjf55asjmpi7cicq46vt4

Learning to Represent Bilingual Dictionaries

Muhao Chen, Yingtao Tian, Haochen Chen, Kai-Wei Chang, Steven Skiena, Carlo Zaniolo
2019 Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)  
In the bilingual paraphrase identification task, we show that our model effectively associates sentences in different languages via a shared embedding space, and outperforms existing approaches in identifying  ...  bilingual paraphrases. * Both authors contributed equally to this work.  ...  Acknowledgement We thank the anonymous reviewers for their insightful comments. This work was supported in part by National Science Foundation Grant IIS-1760523.  ... 
doi:10.18653/v1/k19-1015 dblp:conf/conll/ChenTCCSZ19 fatcat:acovrv3wuzbtnnifgysnrkc63q

Learning to Represent Bilingual Dictionaries [article]

Muhao Chen, Yingtao Tian, Haochen Chen, Kai-Wei Chang, Steven Skiena, Carlo Zaniolo
2019 arXiv   pre-print
Meanwhile, our model effectively addresses the bilingual paraphrase identification problem and significantly outperforms previous approaches.  ...  The proposed model is trained to map the literal word definitions to the cross-lingual target words, for which we explore with different sentence encoding techniques.  ...  Acknowledgement We thank the anonymous reviewers for their insightful comments. This work was supported in part by National Science Foundation Grant IIS-1760523.  ... 
arXiv:1808.03726v3 fatcat:q5gbxiu2nbfxzlf34yvqjvz2x4

Predicting Comprehension from Students' Summaries [chapter]

Mihai Dascalu, Larise Lucia Stavarache, Philippe Dessus, Stefan Trausan-Matu, Danielle S. McNamara, Maryse Bianco
2015 Lecture Notes in Computer Science  
To this end, our aim is to provide an automated solution for analyzing and predicting students' comprehension levels by extracting a combination of reading strategies and textual complexity factors from  ...  We opted to use RBF kernels as the corresponding hyperparameters (the regularization constant C and the kernel hyperparameter γ) were optimized through Grid Search [26] .  ...  applied on lexicalized ontologies [15] , more specifically Wu-Palmer distance applied on WOLF [16] , cosine similarity from Latent Semantic Analysis (LSA) [17] vector spaces, and Jensen-Shannon dissimilarity  ... 
doi:10.1007/978-3-319-19773-9_10 fatcat:5cawjgxquvdm7bfbtngspghrvm

Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network

Sneha Rajana, Chris Callison-Burch, Marianna Apidianaki, Vered Shwartz
2017 Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)  
In this paper, we present a novel method for deriving antonym pairs using paraphrase pairs containing negation markers.  ...  Government is authorized to reproduce and distribute reprints for Governmental purposes.  ...  We would like to thank our anonymous reviewers for their thoughtful and helpful comments.  ... 
doi:10.18653/v1/s17-1002 dblp:conf/starsem/RajanaCAS17 fatcat:2lnueipczzbazjbztdjx3whgvi

A multi-cascaded model with data augmentation for enhanced paraphrase detection in short texts

Muhammad Haroon Shakeel, Asim Karim, Imdadullah Khan
2020 Information Processing & Management  
Paraphrase detection is an important task in text analytics with numerous applications such as plagiarism detection, duplicate question identification, and enhanced customer support helpdesks.  ...  In this work, we present a data augmentation strategy and a multi-cascaded model for improved paraphrase detection in short texts.  ...  paraphrase identification of news tweets in Arabic language.  ... 
doi:10.1016/j.ipm.2020.102204 fatcat:7ehvysetjrhmncciqfstqso6pi

Terminological paraphrase extraction from scientific literature based on predicate argument tuples

Sung-Pil Choi, Sung-Hyon Myaeng
2012 Journal of information science  
Terminological paraphrases (TPs) are sentences or phrases that express the concepts of terminologies in a different form.  ...  For evaluation, we constructed an evaluation collection for the TP recognition task by extracting TPs from a target literature database using the proposed method.  ...  the tree kernel-based machine learning with support vector machines for binary classification.  ... 
doi:10.1177/0165551512459920 fatcat:cwkzqvttc5bbjd5h3oqegw7spa

A deep network model for paraphrase detection in short text messages

Basant Agarwal, Heri Ramampiaro, Helge Langseth, Massimiliano Ruocco
2018 Information Processing & Management  
This paper is concerned with paraphrase detection.  ...  The ability to detect similar sentences written in natural language is crucial for several applications, such as text mining, text summarization, plagiarism detection, authorship authentication and question  ...  Heilman and Smith [9] propose a tree edit model for paraphrase identification based on syntactic relations among words.  ... 
doi:10.1016/j.ipm.2018.06.005 fatcat:okhjlegcxfcd5c2ywd73ie7er4
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