Question similarity in community question answering: A systematic exploration of preprocessing methods and models

Florian Kunneman, Thiago Castro Ferreira, Emiel Krahmer, Antal Van Den Bosch

Research output: Chapter in book/volumeContribution to conference proceedingsScientificpeer-review

5 Citations (Scopus)


Community Question Answering forums are popular among Internet users, and a basic problem they encounter is trying to find out if their question has already been posed before. To address this issue, NLP researchers have developed methods to automatically detect question-similarity, which was one of the shared tasks in SemEval. The best performing systems for this task made use of Syntactic Tree Kernels or the SoftCosine metric. However, it remains unclear why these methods seem to work, whether their performance can be improved by better preprocessing methods and what kinds of errors they (and other methods) make. In this paper, we therefore systematically combine and compare these two approaches with the more traditional BM25 and translation-based models. Moreover, we analyze the impact of preprocessing steps (lowercasing, suppression of punctuation and stop words removal) and word meaning similarity based on different distributions (word translation probability, Word2Vec, fastText and ELMo) on the performance of the task. We conduct an error analysis to gain insight into the differences in performance between the system set-ups. The implementation is made publicly available.1

Original languageEnglish
Title of host publicationInternational Conference on Recent Advances in Natural Language Processing in a Deep Learning World, RANLP 2019 - Proceedings
EditorsGalia Angelova, Ruslan Mitkov, Ivelina Nikolova, Irina Temnikova, Irina Temnikova
PublisherIncoma Ltd
Number of pages9
ISBN (Electronic)9789544520557
Publication statusPublished - 01 Jan 2019

Publication series

NameInternational Conference Recent Advances in Natural Language Processing, RANLP
ISSN (Print)1313-8502


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