Vector-space models for PPDB paraphrase ranking in context. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing , pages 2028–2034, Austin, Texas. Association for Computational Linguistics.

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tations to rank the PPDB paraphrases in context and retain the ones that preserve the semantics of spe-cic text fragments. We evaluate the vector-based ranking models on data hand-annotated with lexical variants and compare the obtained ranking to con-dence estimates available in the PPDB, highlighting ...

The Paraphrase Database (PPDB) is currently the largest available collection of paraphrases. Each paraphrase rule in the database has an automatically-assigned quality score between 1 and 5 (Pavlick et al., 2015). In this work, we use the PPDB-TLDR2 dataset, which contains 14 mil-lion high-scoring lexical and phrasal paraphrases,

PPDB. Each paraphrase rule in PPDB consists of four components: a phrase (e1), a paraphrase (e2), a syntactic category (LHS 1), and a feature vec-tor. This feature vector contains 33 scores of para-phrase quality, which are described in full in the supplementary material to this paper. The rules in PPDB 1.0 were scored using an ad-hoc weighting

We propose to use vector-space semantic models for selecting PPDB paraphrases that preserve the meaning of specific text fragments. This is the first work that addresses the substitutability of PPDB paraphrases in context.

Table 2: Average GAP scores for the contextual models, five paraphrase adequacy methods and the random ranking baseline against the gold C O I N C O annotations. Scores reported for different sizes of the PPDB (from S to XXL).

2016年1月1日 · We carry out experiments with a syntactic vector-space model (Thater et al., 2011; Apidianaki, 2016) and a word-embedding model for lexical substitution (Melamud et al., 2015).

Vector-space models for PPDB paraphrase ranking in context. In J. Su, K. Duh, & X. Carreras (Eds.), Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (pp. 2028-2034). The Association for Computational Linguistics. https://doi.org/10.18653/v1/D16-1215

Vector-space models for PPDB paraphrase ranking in context. In Jian Su , Xavier Carreras , Kevin Duh , editors, Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, November 1-4, 2016 .