Work

An Evaluation of Parser Robustness for Ungrammatical Sentences

Year: 2016

Type: conference-paper

Abstract: For many NLP applications that require a parser, the sentences of interest may not be well-formed.If the parser can overlook problems such as grammar mistakes and produce a parse tree that closely resembles the correct analysis for the intended sentence, we say that the parser is robust.This paper compares the performances of eight state-of-the-art dependency parsers on two domains of ungrammatical sentences: learner English and machine translation outputs.We have developed an evaluation metric and conducted a suite of experiments.Our analyses may help practitioners to choose an appropriate parser for their tasks, and help developers to improve parser robustness against ungrammatical sentences.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/d16-1182

Authors: Homa B. Hashemi, Rebecca Hwa

Institution: University of Pittsburgh

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, University of Pittsburgh

Award: EAGER: Computational Models of Essay Rewritings