Voted Approach for Part of Speech Tagging in Bengali

PACLIC 23 / City University of Hong Kong / 3-5 December 200

RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and Majority Voted Fine-Tuned Transformers

This paper describes our approach to submissions made at Shared Task 2 at BLP Workshop - Sentiment Analysis of Bangla Social Media Posts. Sentiment Analysis is an action research area in the digital age. With the rapid and constant growth of online social media sites and services and the increasing amount of textual data, the application of automatic Sentiment Analysis is on the rise. However, most of the research in this domain is based on the English language. Despite being the world's sixth most widely spoken language, little work has been done in Bangla. This task aims to promote work on Bangla Sentiment Analysis while identifying the polarity of social media content by determining whether the sentiment expressed in the text is Positive, Negative, or Neutral. Our approach consists of experimenting and finetuning various multilingual and pre-trained BERT-based models on our downstream tasks and using a Majority Voting and Weighted ensemble model that outperforms individual baseline model scores. Our system scored 0.711 for the multiclass classification task and scored 10th place among the participants on the leaderboard for the shared task. Our code is available at https://github.com/ptnv-s/RSM-NLP-BLP-Task2 .Comment: Accepted at The 1st Workshop on Bangla Language Processing (BLP 2023), EMNLP 202

Multi-Engine Approach for Named Entity Recognition in Bengali

PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200

Adapting Machine Learning Techniques for Developing Automatic Q&A Interaction Module for Translation Robots based on NLP

Research on Automatic Q&A Interaction Module of Computer-based Translation Robot is a study that focuses on developing an automatic question and answer (Q&A) interaction module for computer-based translation robots. The goal of the research is to enhance the capability of translation robots to perform more human-like interactions with users, particularly in terms of providing more efficient and accurate translations. In this paper proposed a Conditional Random Field Discriminative Analysis (CRFDA) for feature extraction to derive translation robot with Q&A. The proposed CRFDA model comprises of the discriminative analysis for the CRF model. The estimation CRF model uses the bi-directional classifier for the estimation of the feature vector. Finally, the classification is performed with the voting-based classification model for feature extraction. The performance of the CRFDA model is examined based on the Name Entity (Nes) in the TempVal1 &2 dataset. The extraction is based on the strict and relaxed feature model for the exact match and slight variation. The simulation analysis expressed that proposed CRFDA model achieves a classification accuracy of 91% which is significantly higher than the state-of-art techniques

Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in Spanish

Tagged language resources are an essential requirement for developing machine-learning text-based classifiers. However, manual tagging is extremely time consuming and the resulting datasets are rather small, containing only a few thousand samples. Basic emotion datasets are particularly difficult to classify manually because categorization is prone to subjectivity, and thus, redundant classification is required to validate the assigned tag. Even though, in recent years, the amount of emotion-tagged text datasets in Spanish has been growing, it cannot be compared with the number, size, and quality of the datasets in English. Quality is a particularly concerning issue, as not many datasets in Spanish included a validation step in the construction process. In this article, a dataset of social media comments in Spanish is compiled, selected, filtered, and presented. A sample of the dataset is reclassified by a group of psychologists and validated using the Fleiss Kappa interrater agreement measure. Error analysis is performed by using the Sentic Computing tool BabelSenticNet. Results indicate that the agreement between the human raters and the automatically acquired tag is moderate, similar to other manually tagged datasets, with the advantages that the presented dataset contains several hundreds of thousands of tagged comments and it does not require extensive manual tagging. The agreement measured between human raters is very similar to the one between human raters and the original tag. Every measure presented is in the moderate agreement zone and, as such, suitable for training classification algorithms in sentiment analysis field

Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in Spanish

Tagged language resources are an essential requirement for developing machine-learning text-based classifiers. However, manual tagging is extremely time consuming and the resulting datasets are rather small, containing only a few thousand samples. Basic emotion datasets are particularly difficult to classify manually because categorization is prone to subjectivity, and thus, redundant classification is required to validate the assigned tag. Even though, in recent years, the amount of emotion-tagged text datasets in Spanish has been growing, it cannot be compared with the number, size, and quality of the datasets in English. Quality is a particularly concerning issue, as not many datasets in Spanish included a validation step in the construction process. In this article, a dataset of social media comments in Spanish is compiled, selected, filtered, and presented. A sample of the dataset is reclassified by a group of psychologists and validated using the Fleiss Kappa interrater agreement measure. Error analysis is performed by using the Sentic Computing tool BabelSenticNet. Results indicate that the agreement between the human raters and the automatically acquired tag is moderate, similar to other manually tagged datasets, with the advantages that the presented dataset contains several hundreds of thousands of tagged comments and it does not require extensive manual tagging. The agreement measured between human raters is very similar to the one between human raters and the original tag. Every measure presented is in the moderate agreement zone and, as such, suitable for training classification algorithms in sentiment analysis field.Fil: Tessore, Juan Pablo. Universidad Nacional del Noroeste de la Pcia.de Bs.as.. Escuela de Tecnologia. Instituto de Investigacion y Transferencia En Tecnologia. - Comision de Investigaciones Cientificas de la Provincia de Buenos Aires. Instituto de Investigacion y Transferencia En Tecnologia.; ArgentinaFil: Esnaola, Leonardo Martín. Universidad Nacional del Noroeste de la Pcia.de Bs.as.. Escuela de Tecnologia. Instituto de Investigacion y Transferencia En Tecnologia. - Comision de Investigaciones Cientificas de la Provincia de Buenos Aires. Instituto de Investigacion y Transferencia En Tecnologia.; ArgentinaFil: Lanzarini, Laura Cristina. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; ArgentinaFil: Baldassarri, Sandra Silvia. Universidad de Zaragoza; Españ