Using Dependency-Based Features to Take the ’Para-farce’ out of Paraphrase

By Stephen Wan, Mark Dras, Rick Dale, Cécile Paris. 2006

178 citations recorded by the source corpus.

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Citations in the corpus

Skip-Thought Vectors (2015, neutral): “7 WDDP [27] 75.”
Semantic Parsing via Paraphrasing (2014, methodology): “The NLP paraphrase literature is vast and ranges from simple methods employing surface features (Wan et al., 2006), through vector space models (Socher et al., 2011), to latent variable models (Das and Smith, 2009; Wang and Manning, 2010; Stern and Dagan, 2011).”
Convolutional Neural Network for Paraphrase Identification (2015, methodology): “Second, (Socher et al., 2011) is partially based on parsing as is some other work on paraphrase iden-tiﬁcation (e.g., Wan et al. (2006), Ji and Eisenstein (2013)).”
Discriminative Improvements to Distributional Sentence Similarity (2013, methodology): “…literature on paraphrase identiﬁcation, we note three high-level approaches: (1) string similarity metrics such as n-gram overlap and BLEU score (Wan et al., 2006; Madnani et al., 2012), as well as string kernels (Bu et al., 2012); (2) syntactic operations on the parse structure (Wu, 2005; Das…”
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks (2015, methodology): “…of sparse features have been found useful, including: (1) string-based, including n -gram overlap features on both the word and character levels (Wan et al., 2006) and features based on machine translation evaluation metrics (Madnani et al., 2012); (2) knowledge-based, using exter-nal lexical…”
Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement (2016, methodology): “For example, n -gram overlap features at the word and character levels (Madnani et al., 2012; Wan et al., 2006), syntax features (Das and Smith, 2009; Xu et al., 2014), knowledge-based features using Word-Net (Fellbaum, 1998; Fern and Stevenson, 2008) and word-alignment features (Sultan et al.,…”
Reading and Thinking: Re-read LSTM Unit for Textual Entailment Recognition (2016, methodology): “…between sentences ( T and H ) and sentence pairs ( ( T 0 , H 0 ) and ( T 00 , H 00 ) )(Malakasiotis and Androutsopoulos, 2007; Jijkoun and de Rijke, 2005; Wan et al., 2006; Zanzotto and Moschitti, 2006; Wang and Neumann, 2007; Dinu and Wang, 2009; Nielsen et al., 2009; Malakasiotis, 2011).”
A Comparison of Vector-based Representations for Semantic Composition (2012, background): “recently, the addition of syntactic features based on dependency parse trees (Wan et al., 2006; Das and Smith, 2009) has been shown to substantially boost performance.”
Paraphrase Identification as Probabilistic Quasi-Synchronous Recognition (2009, methodology): “This combined model leverages complementary strengths of the two approaches, outperforming a strong state-of-the-art baseline (Wan et al., 2006).”
UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement (2016, methodology): “Traditional approaches are based on hand-crafted feature engineering (Wan et al., 2006; Madnani et al., 2012; Fellbaum, 1998; Fern and Stevenson, 2008; Das and Smith, 2009; Guo and Diab, 2012; Sultan et al., 2014; Kashyap et al., 2014; Lynum et al., 2014).”
Tree Edit Models for Recognizing Textual Entailments, Paraphrases, and Answers to Questions (2010, methodology): “One approach, by Wan et al. (2006), uses an SVM classiﬁer with features based on syntactic dependencies, TED, unigram overlap, and BLEU scores (Papineni et al., 2002).”
Discriminative Learning over Constrained Latent Representations (2010, methodology): “Examples include RTE (Zanzotto and Moschitti, 2006; MacCartney et al., 2008; Roth et al., 2009); string matching (Bergsma and Kon-drak, 2007); transliteration (Klementiev and Roth, 2008); and paraphrase identiﬁcation (Qiu et al., 2006; Wan et al., 2006).”
SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT) (2015, methodology): “These can be categorized into supervised methods (Qiu et al., 2006; Wan et al., 2006; Das and Smith, 2009; Socher et al., 2011; Blacoe and Lapata, 2012; Mad-nani et al., 2012; Ji and Eisenstein, 2013) and unsupervised methods (Mihalcea et al., 2006; Rus et al., 2008; Fernando and Stevenson, 2008;…”
Re-examining Machine Translation Metrics for Paraphrase Identification (2012, methodology): “Although the notion of using MT metrics for the task of para-phrase identiﬁcation is not novel (Finch et al., 2005; Wan et al., 2006), it merits a re-examination in the light of the development of these novel MT metrics for which we can ask “How much better, if at all, do these newer metrics…”
A Survey of Paraphrasing and Textual Entailment Methods (2009, methodology): “…unseen pairs as correct or incorrect paraphrases or textual entailment pairs by examining their features (Bos & Markert, 2005; Brockett & Dolan, 2005; Zhang & Patrick, 2005; Finch et al., 2005; Wan et al., 2006; Burchardt et al., 2007; Hickl, 2008; Malakasiotis, 2009; Nielsen et al., 2009).”
TakeLab: Systems for Measuring Semantic Text Similarity (2012, methodology): “A similar measure has been proposed in (Wan et al., 2006).”
Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection (2011, background): “Instead of comparing only single words [21] adds features from dependency parses.”