Text Keyword Extraction Based on Multi-dimensional Features

Jin, Yu; Chen, Rong; Xu, Lizhen

doi:10.1007/978-3-030-60029-7_23

Cited by 2 publications

(2 citation statements)

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“…In this paper, the length feature refers to the length of the candidate keyword itself and the sentence in which it is located, and refers to the number of words contained in the candidate keywords [44]. Because the length of the keyword is usually less than or equal to the length of 6, it has a good distinction.…”

Section: Length Featurementioning

confidence: 99%

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A Method of Domain Dictionary Construction for Electric Vehicles Disassembly

Ren

Zhang

Chen

2022

Entropy

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Currently, there is no domain dictionary in the field of electric vehicles disassembly and other domain dictionary construction algorithms do not accurately extract terminology from disassembly text, because the terminology is complex and variable. Herein, the construction of a domain dictionary for the disassembly of electric vehicles is a research work that has important research significance. Extracting high-quality keywords from text and categorizing them widely uses information mining, which is the basis of named entity recognition, relation extraction, knowledge questions and answers and other disassembly domain information recognition and extraction. In this paper, we propose a supervised learning dictionary construction algorithm based on multi-dimensional features that combines different features of extraction candidate keywords from the text of each scientific study. Keywords recognition is regarded as a binary classification problem using the LightGBM model to filter each keyword, and then expand the domain dictionary based on the pointwise mutual information value between keywords and its category. Here, we make use of Chinese disassembly manuals, patents and papers in order to establish a general corpus about the disassembly information and then use our model to mine the disassembly parts, disassembly tools, disassembly methods, disassembly process, and other categories of disassembly keywords. The experiment evidenced that our algorithms can significantly improve extraction and category performance better than traditional algorithms in the disassembly domain. We also investigated the performance algorithms and attempts to describe them. Our work sets a benchmark for domain dictionary construction in the field of disassembly of electric vehicles that is based on the newly developed dataset using a multi-class terminology classification.

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Section: Length Featurementioning

confidence: 99%

“…However, there are some exceptions where the frequency of some words is high but not important and there are some sparse data but also very important. In this paper, head word frequency, term frequency, inverse document frequency, TF-IDF and title word frequency to measure the importance of candidate keywords [44] (Table 2).…”

Section: Term-frequency Featurementioning

confidence: 99%