Semantic robust parsing for noun extraction from natural language queries

TL;DR

This paper explores semantic robust parsing techniques to improve noun extraction from natural language queries, especially in noisy or imperfect input conditions, enhancing NLP query processing in restricted domains.

Contribution

It demonstrates the importance of semantic robustness in handling partial and ill-formed utterances for effective query generation in NLP systems.

Findings

Semantic robustness improves noun extraction accuracy.

Robust parsing handles noisy and partial inputs effectively.

Enhanced query generation in restricted domains.

Abstract

This paper describes how robust parsing techniques can be fruitful applied for building a query generation module which is part of a pipelined NLP architecture aimed at process natural language queries in a restricted domain. We want to show that semantic robustness represents a key issue in those NLP systems where it is more likely to have partial and ill-formed utterances due to various factors (e.g. noisy environments, low quality of speech recognition modules, etc...) and where it is necessary to succeed, even if partially, in extracting some meaningful information.