# Intent Classification in Question-Answering Using LSTM Architectures

arXiv: https://arxiv.org/abs/2001.09330 · HTML: https://mlsanity.com/paper/2001.09330/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [A Deep Learning Approach for User-Centric Clustering in Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2410.02775/) (2024)
- [Implications of Deep Circuits in Improving Quality of Quantum Question Answering](https://mlsanity.com/paper/2305.07374/) (2023)

## Abstract (arXiv)

Question-answering (QA) is certainly the best known and probably also one of the most complex problem within Natural Language Processing (NLP) and artificial intelligence (AI). Since the complete solution to the problem of finding a generic answer still seems far away, the wisest thing to do is to break down the problem by solving single simpler parts. Assuming a modular approach to the problem, we confine our research to intent classification for an answer, given a question. Through the use of an LSTM network, we show how this type of classification can be approached effectively and efficiently, and how it can be properly used within a basic prototype responder.