Learning Paraphrasing for Multiword Expressions
Seid Muhie Yimam, Héctor Martínez Alonso, Martin Johannes Riedl, Chris Biemann · 2016
In this paper, we investigate the impact of context for the paraphrase ranking task, comparing and quantifying results for multi-word expressions and single words.We focus on systematic integration of existing paraphrase resources to produce paraphrase candidates and later ask human annotators to judge paraphrasability in context.We first conduct a paraphrase-scoring annotation task with and without context for targets that are i) single-and multi-word expressions ii) verbs and nouns.We quantify how differently annotators score paraphrases when context information is provided.Furthermore, we report on experiments with automatic paraphrase ranking.If we regard the problem as a binary classification task, we obtain an F1-score of 81.56% and 79.87% for multi-word expressions and single words resp.using kNN classifier.Approaching the problem as a learning-to-rank task, we attain MAP scores up to 87.14% and 91.58% for multiword expressions and single words resp.using LambdaMART, thus yielding highquality contextualized paraphrased selection.Further, we provide the first dataset with paraphrase judgments for multi-word targets in context.