This study tackles the problem of paraphrase acquisition : achieving high coverage as well as accuracy . Our method first induces paraphrase patterns from given seed paraphrases , exploiting the generality of paraphrases exhibited by pairs of lexical variants , e.g. , " amendment " and " amending , " in a fully empirical way . It then searches monolingual corpora for new paraphrases that match the patterns . This can extract paraphrases comprising words that are completely different from those of the given seeds . In experiments , our method expanded seed sets by factors of 42 to 206 , gaining 84 % to 208 % more coverage than a previous method that generalizes only identical word forms . Human evaluation through a paraphrase substitution test demonstrated that the newly acquired paraphrases retained reasonable quality , given substantially high-quality seeds . One of the characteristics of human languages is that the same semantic content can be expressed using several different linguistic expressions , i.e. , paraphrases . Dealing with paraphrases is an important issue in a broad range of natural language processing ( NLP ) tasks ( Madnani and Dorr , 2010 ; Androutsopoulos and Malakasiotis , 2010 ) . To adequately and robustly deal with paraphrases , a large-scale knowledge base containing words and phrases having approximately the same meaning is indispensable . Thus , the task of automatically creating such large-scale paraphrase lexicons has been drawing the attention of many researchers ( see Section 2 for details ) . The challenge is to en-sure substantial coverage along with high accuracy despite the natural tension between these factors . Among the different types of language resources , monolingual corpora1 offer the largest coverage , but the quality of the extracted candidates is generally rather low . The difficulty lies in the manner of distinguishing paraphrases from expressions that stand in different semantic relations , e.g. , antonyms and sibling words , using only the statistics estimated from such corpora . In contrast , highly accurate paraphrases can be extracted from parallel or comparable corpora , but their coverage is limited owing to the limited availability of such corpora for most languages . This study aims to improve coverage while maintaining accuracy . To that end , we propose a method that exploits the generality exhibited by pairs of lexical variants . Given a seed set of paraphrase pairs , our method first induces paraphrase patterns by generalizing not only identical word forms ( Fujita et al . , 2012 ) but also pairs of lexical variants . For instance , from a seed pair ( 1a ) , a pattern ( 1b ) is acquired , where the pair of lexical variants ( " amendment " , " amending " ) and the shared word form " regulation " are generalized . ( 1 ) a. amendment of regulation ⇔ amending regulation b. X : ment of Y : ϕ ⇔ X : ing Y : ϕ With such patterns , new paraphrase pairs that would have been missed using only the surface forms are extracted from a monolingual corpus . Obtainable pairs can include those comprising words that are completely different from those of the seed paraphrases , e.g. , ( 2a ) and ( 2b ) . ( 2 ) a. investment of resources ⇔ investing resources b. recruitment of engineers ⇔ recruiting engineers While the generality underlying paraphrases has been exploited either by handcrafted rules ( Harris , 1957 ; Mel'čuk and Polguère , 1987 ; Jacquemin , 1999 ; Fujita et al . , 2007 ) or by data-driven techniques ( Ganitkevitch et al . , 2011 ; Fujita et al . , 2012 ) , we still lack a robust and accurate way of identifying various types of lexical variants . Our method tackles this issue using affix patterns that are also acquired from high-quality seed paraphrases in a fully empirical way . Consequently , our method has the potential to apply to many languages . We proposed a method for expanding given paraphrase lexicons by first inducing paraphrase patterns and then searching monolingual corpora with these patterns for new paraphrase pairs . To the best of our knowledge , this is the first attempt to exploit various types of lexical variants for acquiring paraphrases in a completely empirical way . Our method requires minimal language-dependent resources , i.e. , stoplists and tokenizers , other than raw corpora . We demonstrated the quantitative impact of our method and confirmed the potential quality of the expanded paraphrase lexicon . Our future work is four-fold . ( i ) Paraphrase lexicons created by different methods and sources have different properties . Designing an overall model to harmonize such heterogeneous lexicons is an important issue . ( ii ) We aim to investigate an extensive collection of corpora : there are far more corpora than those we used in this experiment . We are also interested in expanding paraphrase lexicons created by a method other than bilingual pivoting ; for instance , those extracted from a Web-harvested monolingual comparable corpus ( Hashimoto et al . , 2011 ; Yan et al . , 2013 ) . ( iii ) We will apply our method to various languages for demonstrating its applicability , extending it for a wider range of lexical variants depending on the targeted language . ( iv ) Paraphrases are the fundamental linguistic phenomena that affect a wide range of NLP tasks . We are therefore interested in determining to what extent our paraphrase lexicons can improve the performance of application tasks such as machine translation , text summarization , and text simplification .

Challenge: For paraphrase acquisition, monolingual corpus achieves high coverage but quality is low and parallel or comparable corpus can keep quality high but coverage is low.
Approach: They propose to first induce paraphrase patterns from high quality seed paraphrases and then perform a search over a monolingual corpus with obtained patterns.
Outcome: The proposed method achieves up to 208% more coverage than a previous method and human evaluation shows the quality is reasonably high.