Cross-Domain Ambiguity Detection using Linear Transformation of Word Embedding Spaces

EasyChair Preprint no. 1785, version 1

Abstract

The requirements engineering process is a crucial stage of the software development life cycle. It involves various stakeholders from different professional backgrounds, particularly in the requirements elicitation phase. Each stakeholder carries distinct domain knowledge, causing them to differently interpret certain words, leading to cross-domain ambiguity. This can result in misunderstanding amongst them and jeopardize the entire project. We propose a computationally cheap natural language processing approach to find potentially ambiguous words for a given set of domains. The idea is to apply linear transformations on word embedding models trained on different domain corpora, to bring them into a unified embedding space. We then find words with divergent embeddings as they signify a variation in the meaning across the domains. Applying the approach to a set of hypothetical scenarios produces promising results. It can help a requirements analyst in preventing misunderstandings during elicitation interviews and meetings by defining a set of potentially ambiguous terms in advance.

Keyphrases: Cross-domain ambiguity, linear transformation, Natural Language Processing, Requirements Engineering, Work embeddings

@Booklet{EasyChair:1785,
  author = {Vaibhav Jain and Sanskar Jain and Nishant Tanwar},
  title = {Cross-Domain Ambiguity Detection using Linear Transformation of Word Embedding Spaces},
  howpublished = {EasyChair Preprint no. 1785},

  year = {EasyChair, 2019}}