التفاصيل البيبلوغرافية
| العنوان: |
Short Answer Detection for Open Questions: A Sequence Labeling Approach with Deep Learning Models |
| المؤلفون: |
Samuel González-López, Zeltzyn Guadalupe Montes-Rosales, Adrián Pastor López-Monroy, Aurelio López-López, Jesús Miguel García-Gorrostieta |
| المصدر: |
Mathematics; Volume 10; Issue 13; Pages: 2259 |
| بيانات النشر: |
Multidisciplinary Digital Publishing Institute |
| سنة النشر: |
2022 |
| المجموعة: |
MDPI Open Access Publishing |
| مصطلحات موضوعية: |
question answering, open questions, academic document analysis, sequence labeling, deep learning |
| الوصف: |
Evaluating the response to open questions is a complex process since it requires prior knowledge of a specific topic and language. The computational challenge is to analyze the text by learning from a set of correct examples to train a model and then predict unseen cases. Thus, we will be able to capture patterns that characterize answers to open questions. In this work, we used a sequence labeling and deep learning approach to detect if a text segment corresponds to the answer to an open question. We focused our efforts on analyzing the general objective of a thesis according to three methodological questions: Q1: What will be done? Q2: Why is it going to be done? Q3: How is it going to be done? First, we use the Beginning-Inside-Outside (BIO) format to label a corpus of targets with the help of two annotators. Subsequently, we adapted four state-of-the-art architectures to analyze the objective: Bidirectional Encoder Representations from Transformers (BERT-BETO) for Spanish, Code Switching Embeddings from Language Model (CS-ELMo), Multitask Neural Network (MTNN), and Bidirectional Long Short-Term Memory (Bi-LSTM). The results of the F-measure for detection of the answers to the three questions indicate that the BERT-BETO and CS-ELMo architecture obtained the best effectivity. The architecture that obtained the best results was BERT-BETO. BERT was the architecture that obtained more accurate results. The result of a detection analysis for Q1, Q2 and Q3 on a non-annotated corpus at the graduate and undergraduate levels is also reported. We found that for detecting the three questions, only the doctoral academic level reached 100%; that is, the doctoral objectives did contain the answer to the three questions. |
| نوع الوثيقة: |
text |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| العلاقة: |
Mathematics and Computer Science; https://dx.doi.org/10.3390/math10132259Test |
| DOI: |
10.3390/math10132259 |
| الإتاحة: |
https://doi.org/10.3390/math10132259Test |
| حقوق: |
https://creativecommons.org/licenses/by/4.0Test/ |
| رقم الانضمام: |
edsbas.AEA33FB6 |
| قاعدة البيانات: |
BASE |
