Automated Detection of Syntactic Ambiguity Using Shallow Parsing and Web Data

Abstract

Technical documents are mostly written in natural languages and they are highly ambiguity-prone due to the fact that ambiguity is an inevitable feature of natural languages. Many researchers have urged technical documents to be free from ambiguity to avoid unwanted and, in some cases, disastrous consequences ambiguity and misunderstanding can have in technical context. Therefore the need for ambiguity detection tools to assist writers with ambiguity detection and resolution seems indispensable. The purpose of this thesis work is to propose an automated approach in detection and resolution of syntactic ambiguity. AmbiGO is the name of the prototyping web application that has been developed for this thesis which is freely available on the web. The hope is that a developed version of AmbiGO will assist users with ambiguity detection and resolution. Currently AmbiGO is capable of detecting and resolving three types of syntactic ambiguity, namely analytical, coordination and PP attachment types. AmbiGO uses syntactic parsing to detect ambiguity patterns and retrieves frequency counts from Google for each possible reading as a segregate for semantic analysis. Such semantic analysis through Google frequency counts has significantly improved the precision score of the tool’s output in all three ambiguity detection functions. AmbiGO is available at this URL: http://omidemon.pythonanywhere.com/