Citation: Tohti, T.; Abdurxit, M.;
Hamdulla, A. Medical QA Oriented
Multi-Task Learning Model for
Question Intent Classification and
Named Entity Recognition.
Information 2022, 13, 581. https://
doi.org/10.3390/info13120581
Academic Editor: Krzysztof
Ejsmont
Received: 30 October 2022
Accepted: 10 December 2022
Published: 14 December 2022
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Article
Medical QA Oriented Multi-Task Learning Model for Question
Intent Classification and Named Entity Recognition
Turdi Tohti , Mamatjan Abdurxit and Askar Hamdulla *
School of Information Science and Engineering, Xinjiang Key Laboratory of Signal Detection and Processing,
Xinjiang University, Urumqi 830017, China
* Correspondence: askar@xju.edu.cn; Tel.: +86-139-9922-1222
Abstract:
Intent classification and named entity recognition of medical questions are two key subtasks
of the natural language understanding module in the question answering system. Most existing
methods usually treat medical queries intent classification and named entity recognition as two
separate tasks, ignoring the close relationship between the two tasks. In order to optimize the effect
of medical queries intent classification and named entity recognition tasks, a multi-task learning
model based on ALBERT-BILSTM is proposed for intent classification and named entity recognition
of Chinese online medical questions. The multi-task learning model in this paper makes use of
encoder parameter sharing, which enables the model’s underlying network to take into account both
named entity recognition and intent classification features. The model learns the shared information
between the two tasks while maintaining its unique characteristics during the decoding phase. The
ALBERT pre-training language model is used to obtain word vectors containing semantic information
and the bidirectional LSTM network is used for training. A comparative experiment of different
models was conducted on Chinese medical questions dataset. Experimental results show that the
proposed multi-task learning method outperforms the benchmark method in terms of precision,
recall and F
1
value. Compared with the single-task model, the generalization ability of the model has
been improved.
Keywords: multi-task learning; named entity recognition; intent classification; ALBERT; deep learning
1. Introduction
Along with the rapid development of online medical service technology, people can
ask questions and receive answers online through health service websites. Medical question
and answer systems require the ability to build a medical knowledge base and apply natural
language understanding techniques to extract structured information from users’ questions,
and automatically generate answers to them. For this type of health service, the accuracy
of the generated answers depends not only on the quality of the knowledge base, but also
on the accuracy of the user’s question understanding.
Intent classification and named entity recognition are two subtasks of natural lan-
guage understanding. Most existing medical natural language processing studies in text
classification and named entity recognition are usually performed independently. The
purpose of the intent classification task is to first identify possible intent classes in a given
domain and then classify sentences to specific intent classes based on contextual infor-
mation in the text. Named entity recognition aims at extracting medical entities from the
text and predicting the different kinds of entities. Both of these tasks can help a medical
question and answer system to correctly provide the services required by the user. For
example, suppose a user asks the question “I have kidney stones, what should I do?”.
Based on the intent analysis, the user is seeking a treatment and based on named entity
recognition, we know that the question contains the disease term “kidney stones”. In this
case, we can search our knowledge base and return an answer about the treatment for
Information 2022, 13, 581. https://doi.org/10.3390/info13120581 https://www.mdpi.com/journal/information
