Efficient natural language classification algorithm for detecting duplicate unsupervised features

IF 1.9 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Intelligenza Artificiale Pub Date : 2021-06-02 DOI:10.15622/IA.2021.3.5
S. Altaf, Sofia Iqbal, M. Soomro
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引用次数: 2

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.
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重复无监督特征检测的高效自然语言分类算法
本文主要研究自然语言理解(NLU)文本特征的意义捕获,以检测重复的无监督特征。将NLU特征与词法方法进行比较,以证明适合的分类技术。利用迁移学习方法对语义文本相似度(STS)任务的特征提取进行训练。所有的特征都是用两种类型的数据集来评估的,这两种数据集分别属于Bosch bug和Wikipedia文章报告。本研究的目的是通过比较NLU概念在文本语义特征及其应用于IR方面的研究成果,对近年来的研究成果进行梳理。本文的主要贡献是对语义相似度度量的比较研究。实验结果表明,术语频率-逆文档频率(TF-IDF)特征在两个数据集上都具有合理的词汇量。这表明双向长短期记忆(BiLSTM)可以通过学习句子的结构来提高分类能力。
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来源期刊
Intelligenza Artificiale
Intelligenza Artificiale COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
3.50
自引率
6.70%
发文量
13
期刊最新文献
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