Bilingual AutoCategorization Comparison of Two LSTM Text Classifiers Multi linguistic problems such as autocategorization is not an easy task. It is possible to train different models for each language, another way to do autocategorization is to build the model in one base language and use automatic translation from other languages to that base language. Different languages have a bias to a language specific grammar and syntax and will therefore pose problems to be expressed in other languages. Translating from one language into a nonverbal language could potentially have a positive impact of the categorization results. A nonverbal language could for example be pure information in form of a knowledge graph relation extraction from the text. In this article a comparison is conducted between Chinese and Swedish languages. Two categorization models are developed and validated on each dataset. The purpose is to make an autocategorization model that works for nu0027importe quel langage. One model is built upon LSTM and optimized for Swedish and the other is an improved BidirectionalLSTM Convolution model optimized for Chinese. The improved algorithm is trained on both languages and compared with the LSTM algorithm. The BidirectionalLSTM algorithm performs approximately 20% units better than the LSTM algorithm, which is significant.

Outpatient Text Classification Using AttentionBased Bidirectional LSTM for RobotAssisted Servicing in Hospital In general, patients who are unwell do not know with which outpatient department they should register, and can only get advice after they are diagnosed by a family doctor. This may cause a waste of time and medical resources. In this paper, we propose an attentionbased bidirectional long shortterm memory (AttBiLSTM) model for service robots, which has the ability to classify outpatient categories according to textual content. With the outpatient text classification system, users can talk about their situation to a service robot and the robot can tell them which clinic they should register with. In the implementation of the proposed method, dialog text of users in the Taiwan E Hospital were collected as the training data set. Through natural language processing (NLP), the information in the dialog text was extracted, sorted, and converted to train the longshort term memory (LSTM) deep learning model. Experimental results verify the ability of the robot to respond to questions autonomously through acquired casual knowledge.