Efficient natural language classification algorithm for detecting duplicate unsupervised features
- Authors: Altaf S.1, Iqbal S.2, Soomro M.3
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Affiliations:
- AUT University
- Pakistan Space and Upper Atmosphere Research Commission (SUPARCO), Pakistan
- Manukau Institute of Technology
- Issue: Vol 20, No 3 (2021)
- Pages: 623-653
- Section: Artificial intelligence, knowledge and data engineering
- URL: https://journals.rcsi.science/2713-3192/article/view/266316
- DOI: https://doi.org/10.15622/ia.2021.3.5
- ID: 266316
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Abstract
This paper focuses on capturing the meaning of Natural Language Understanding (NLU) text features to detect the duplicate unsupervised features. The NLU features are compared with lexical approaches to prove the suitable classification technique. The transfer-learning approach is utilized to train the extraction of features on the Semantic Textual Similarity (STS) task. All features are evaluated with two types of datasets that belong to Bosch bug and Wikipedia article reports. This study aims to structure the recent research efforts by comparing NLU concepts for featuring semantics of text and applying it to IR. The main contribution of this paper is a comparative study of semantic similarity measurements. The experimental results demonstrate the Term Frequency–Inverse Document Frequency (TF-IDF) feature results on both datasets with reasonable vocabulary size. It indicates that the Bidirectional Long Short Term Memory (BiLSTM) can learn the structure of a sentence to improve the classification.
About the authors
S. Altaf
AUT University
Author for correspondence.
Email: saud@uaar.edu.pk
Maine Murry Road 1
S. Iqbal
Pakistan Space and Upper Atmosphere Research Commission (SUPARCO), Pakistan
Email: sofiaiqbal.suparco@gmail.com
Sector-H, DHA Phase II -
M. Soomro
Manukau Institute of Technology
Email: MWASEEM@manukau.ac.nz
Newbury Street -
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