ORCID Profile
0000-0002-0278-7738
Current Organisation
Monash University
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Publisher: IEEE
Date: 23-01-2023
Publisher: Springer Nature Singapore
Date: 2023
Publisher: Springer Nature Singapore
Date: 2023
Publisher: Elsevier BV
Date: 2022
Publisher: Institution of Engineering and Technology
Date: 2021
Publisher: Association for Computing Machinery (ACM)
Date: 26-08-2024
DOI: 10.1145/3596597
Abstract: Identifying requirements for an information system is an important task and conceptual modelling is the first step in this process. Conceptual modelling plays a critical role in the information system design process and usually involves domain experts and knowledge engineers who brainstorm together to identify the required knowledge to build an information system. The conceptual modelling process starts with the collection of necessary information from the domain experts by the knowledge engineers. Afterwards, the knowledge engineers use traditional model driven engineering techniques to design the system based on the collected information. Natural language–based conceptual modelling frameworks or systems are used to help domain experts and knowledge engineers in eliciting requirements and building conceptual models from a natural language text. In this article, we discuss the state of the art of some recent conceptual modelling frameworks that are based on natural language. We take a closer look at how these frameworks are built, in particular at the underlying motivation, architecture, types of natural language used (e.g., restricted vs. unrestricted), types of the conceptual model generated, verification support of the requirements specifications as well as the conceptual models, and underlying knowledge representation formalism. We also discuss some future research opportunities that these frameworks offer.
Publisher: IEEE
Date: 08-2020
Publisher: IEEE
Date: 12-2019
Publisher: ACM
Date: 18-11-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Informa UK Limited
Date: 06-02-2022
Publisher: Springer Science and Business Media LLC
Date: 29-03-2021
Publisher: Inderscience Publishers
Date: 2021
Publisher: IEEE
Date: 12-2017
Publisher: ACM
Date: 06-09-2022
Publisher: Springer Singapore
Date: 24-07-2020
Publisher: IEEE
Date: 08-2020
Publisher: IEEE
Date: 20-01-2023
Publisher: Association for Computing Machinery (ACM)
Date: 31-10-2021
DOI: 10.1145/3474363
Abstract: Sentiment Analysis (SA) is a Natural Language Processing (NLP) and an Information Extraction (IE) task that primarily aims to obtain the writer’s feelings expressed in positive or negative by analyzing a large number of documents. SA is also widely studied in the fields of data mining, web mining, text mining, and information retrieval. The fundamental task in sentiment analysis is to classify the polarity of a given content as Positive, Negative, or Neutral . Although extensive research has been conducted in this area of computational linguistics, most of the research work has been carried out in the context of English language. However, Bengali sentiment expression has varying degree of sentiment labels, which can be plausibly distinct from English language. Therefore, sentiment assessment of Bengali language is undeniably important to be developed and executed properly. In sentiment analysis, the prediction potential of an automatic modeling is completely dependent on the quality of dataset annotation. Bengali sentiment annotation is a challenging task due to ersified structures (syntax) of the language and its different degrees of innate sentiments (i.e., weakly and strongly positive/negative sentiments). Thus, in this article, we propose a novel and precise guideline for the researchers, linguistic experts, and referees to annotate Bengali sentences immaculately with a view to building effective datasets for automatic sentiment prediction efficiently.
Publisher: Association for Computing Machinery (ACM)
Date: 10-03-2022
DOI: 10.1145/3575804
Abstract: Punctuation prediction is critical as it can enhance the readability of machine-transcribed speeches or texts significantly by adding appropriate punctuation. Furthermore, systems like Automatic Speech Recognizer (ASR) produce texts that are unpunctuated, making the readability difficult for humans and also h ers the performance of various natural language processing (NLP) tasks. Such NLP related tasks have been investigated thoroughly for English however, very limited work is done for punctuation prediction in the Bangla language. In this study, we train a bidirectional recurrent neural network (BRNN) along with Attention model with a plausibly large Bangla dataset. Afterwards, we apply extensive postprocessing techniques for predicting punctuation more accurately with the employed model. Initially, we perform experimentation with a relatively imbalanced dataset, and our model shows promising results F1=56.9 for Period) in punctuation prediction. Later, we also investigate the model’s performance using a balanced Bangla dataset to achieve higher performance scores ( F1=62.2 for Question). Thus, the goal of this study is to propose an efficient approach that can predict punctuation in Bangla texts effectively. Our study also includes investigation on how our postprocessing techniques affect the prediction performance. Being an early attempt for the punctuation prediction in Bangla text, our work is expected to significantly contribute in the NLP field for the Bangla language, and will pave the way for future work with the Bangla language in this direction.
Location: Bangladesh
Location: Bangladesh
No related grants have been discovered for Md. Adnanul Islam.