A Natural Language Understanding Model COVID-19 based for chatbots

Santos, Valmir Oliveira Dos; Branco, João A.; Oliveira, Marcos Antonio De; Silva, Ticiana L. Coelho da; Cruz, Lívia Almada; Magalhães, Régis Pires

doi:10.1109/bibe52308.2021.9635248

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…This work extends the experiments presented in [12]. There we built several NLU models using the Rasa Framework with different embedding models using the same dataset described in this study.…”

Section: Introductionmentioning

confidence: 60%

“…Given a dataset of dialogues as input not annotated, we aim to propose an NLU intent model to create a chatbot. Besides these differences, we also study different embedding representation models, the bias added from the annotation processing, we improve the related work section and we perform more experiments compared to our previous paper [12]. The main research questions that guide this study are:…”

Section: Introductionmentioning

confidence: 99%

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WITHDRAWN: Learning and Semi-automatic Intention Labeling for Classification Models: ACOVID-19 Study for Chatbots

Júnior

Oliveira

Cruz

et al. 2022

Preprint

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It is increasingly common to use chatbots as an interface to services. Making this experience more humanized requires the chatbot to understand natural language and express itself using it. One of the main components of a chatbot is Natural Language Understanding (NLU) model, which is responsible for interpreting the text and extracting the intent and entities present in that text. It's possible to focus only on one of these tasks of NLU, such as Intent classification. To train an NLU intent classification model, it's generally necessary to use a considerable amount of annotated data, where each sentence of the dataset receives a label indicating an intent.Performing manually labeling data is an arduous and impracticable process depending on the data volume. Thus, an unsupervised machine learning technique, such as data clustering, could be applied to find patterns in the data and label them. For this task, it is essential to have an effective vector embedding representation of texts that depicts the semantic information and helps the machine understand the context, intent, and other nuances of the entire text. In this paper, we perform an extensive evaluation of different text embedding models for clustering and labeling, we also apply some operations to improve the quality of the dataset, such as removing sentences establishing different strategies for distance thresholds in terms of (cosine similarity) for the clusters' centroids. Then we trained some Intent Classification Models with two different architectures, one built with the Rasa framework and the other with a Neural Network (NN) using the text of attendance from the Coronavirus Platform Service of Ceará, Brazil. We also manually annotated a dataset to be used as validation data. We found that semi-automatic labeling through clustering and visual inspection added some biases to the intent classification models. However, we still achieved competitive results in terms of accuracy for the trained models.

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Section: Introductionmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Learning and Semi-automatic Intention Labeling for Classification Models: ACOVID-19 Study for Chatbots

Júnior

Oliveira

Cruz

et al. 2022

Preprint

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The HoPE Model Architecture: a Novel Approach to Pregnancy Information Retrieval Based on Conversational Agents

Montenegro

Costa

2022

J Healthc Inform Res

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Conversational agents are used to communicating with humans in a friendly manner. To achieve the highest level of performance, agents need to respond assertively and fastly. Transformer architectures are shown to produce excellent performances on recent tasks; however, for tasks involving conversational agents, they may have a lower speed performance. The main goal of this study is to evaluate and propose a HoPE (Healthcare Obstetric in PrEgnancy) model that is tailored to pregnancy data. We carried out a dataset extraction and construction process based on collections of health documents related to breastfeeding, childcare, pregnant care, nutrition, risks, vaccines, exams, and physical exercises. We evaluated two pre-trained models in the Portuguese language for the conversational agent architecture proposal and chose the one with the best performance to compose the HoPE architecture. The BERTimbau model, which has been trained on data augmentation strategies, proves to be able to retrieve information quickly and most accurately than others. For the fine-tuning process, we achieved a Spearman correlation of 95.55 on BERTimbau augmented with a few pairs (1.500 pairs). The HoPE model architecture achieved an F1-Score of 0.89, outperforming other combinations tested in this study. We will evaluate this approach for clinical studies in future studies.

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Learning and semiautomatic intention labeling for classification models: a COVID-19 dialog attendance study for chatbots

dos Santos Júnior,

de Oliveira,

Cruz

et al. 2024

Nat. lang. processing

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It is increasingly common to use chatbots as an interface to services. One of the main components of a chatbot is the Natural Language Understanding (NLU) model, which is responsible for interpreting the text and extracting the intent and entities present in that text. It’s possible to focus only on one of these tasks of NLU, such as intent classification. To train an NLU intent classification model, it’s generally necessary to use a considerable amount of annotated data, where each sentence of the dataset receives a label indicating an intent. Performing manually labeling data is arduous and impracticable, depending on the data volume. Thus, an unsupervised machine learning technique, such as data clustering, could be applied to find and label patterns in the data. For this task, it is essential to have an effective vector embedding representation of texts that depicts the semantic information and helps the machine understand the context, intent, and other nuances of the entire text. This paper extensively evaluates different text embedding models for clustering and labeling. We also apply some operations to improve the dataset’s quality, such as removing sentences and establishing various strategies for distance thresholds (cosine similarity) for the clusters’ centroids. Then, we trained some intent classification Models with two different architectures, one built with the Rasa framework and the other with a neural network (NN) using the attendance text from the Coronavirus Platform Service of Ceará, Brazil. We also manually annotated a dataset to be used as validation data. We conducted a study on semiautomatic labeling, implemented through clustering and visual inspection, which introduced some labeling errors to the intent classification models. However, it would be unfeasible to annotate the entire dataset manually. Nevertheless, results of competitive accuracy were still achieved with the trained models.

show abstract

A Natural Language Understanding Model COVID-19 based for chatbots

Cited by 4 publications

References 13 publications

WITHDRAWN: Learning and Semi-automatic Intention Labeling for Classification Models: ACOVID-19 Study for Chatbots

WITHDRAWN: Learning and Semi-automatic Intention Labeling for Classification Models: ACOVID-19 Study for Chatbots

The HoPE Model Architecture: a Novel Approach to Pregnancy Information Retrieval Based on Conversational Agents

Learning and semiautomatic intention labeling for classification models: a COVID-19 dialog attendance study for chatbots

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