DIKEA: Domain-Independent Keyphrase Extraction Algorithm

2012 ◽  
pp. 719-730 ◽  
Author(s):  
David X. Wang ◽  
Xiaoying Gao ◽  
Peter Andreae
Keyword(s):  
Keyphrase Extraction ◽  
Extraction Algorithm ◽  
Domain Independent

scholarly journals Автоматическое извлечение ключевых слов и словосочетаний из русскоязычных текстов с помощью алгоритма KEA

2018 ◽  
pp. 157-165
Author(s):  
Елена Вячеславовна Соколова ◽  
Ольга Александровна Митрофанова
Keyword(s):  
Keyphrase Extraction ◽  
Extraction Algorithm

В докладе представлены результаты работы по модификации алгоритма KEA ( Keyphrase Extraction Algorithm ), используемого для извлечения ключевых слов и словосочетаний. KEA широко известен своей эффективностью для извлечения ключевых слов и словосочетаний из англоязычных текстов. В статье представлены результаты применения данного алгоритма к текстам на русском языке. Для определения качества работы алгоритма с русскоязычными текстами были проведены эксперименты на материале представительных корпусов.

News Headline Building using Hybrid Headline Generation Technique for Quick Gist

2017 ◽  
Vol 6 (1) ◽  
pp. 36-52
Author(s):  
Urmila Shrawankar ◽  
Kranti Wankhede
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Reading Time ◽  
News Article ◽  
Keyword Extraction ◽  
Keyphrase Extraction ◽  
Generation Technique ◽  
Extraction Algorithm ◽  
Key Terms

A considerable amount of time is required to interpret whole news article to get the gist of it. Therefore, in order to reduce the reading and interpretation time, headlines are necessary. The available techniques for news headline construction mainly includes extractive and abstractive headline generation techniques. In this paper, context based news headline is formed from long news article by using techniques of core Natural Language Processing (NLP) and key terms of news article. Key terms are retrieved from lengthy news article by using various approaches of keyword extraction. The keyphrases are picked out using Keyphrase Extraction Algorithm (KEA) which helps to construct headline syntax along with NLP's parsing technique. Sentence compression algorithm helps to generate compressed sentences from generated parse tree of leading sentences. Headline helps user for reducing cognitive burden of reader by reflecting important contents of news. The objective is to frame headline using key terms for reducing reading time and efforts of reader.

A domain knowledge graph construction method based on Wikipedia

2020 ◽  
pp. 016555152093251
Author(s):  
Haoze Yu ◽  
Haisheng Li ◽  
Dianhui Mao ◽  
Qiang Cai
Keyword(s):  
Domain Knowledge ◽  
Multiple Scale ◽  
Construction Method ◽  
Knowledge Graph ◽  
Relationship Extraction ◽  
Proposed Model ◽  
Extraction Algorithm ◽  
Structured Knowledge ◽  
Extraction Model ◽  
Domain Independent

In order to achieve real-time updating of the domain knowledge graph and improve the relationship extraction ability in the construction process, a domain knowledge graph construction method is proposed. Based on the structured knowledge in Wikipedia’s classification system, we acquire concepts and instances contained in subject areas. A relationship extraction algorithm based on co-word analysis is intended to extract the classification relationships in semi-structured open labels. A Bi-GRU remote supervised relationship extraction model based on a multiple-scale attention mechanism and an improved cross-entropy loss function is proposed to obtain the non-classification relationships of concepts in unstructured texts. Experiments show that the proposed model performs better than the existing methods. Based on the obtained concepts, instances and relationships, a domain knowledge graph is constructed and the domain-independent nodes and relationships contained in them are removed through a vector variance algorithm. The effectiveness of the proposed method is verified by constructing a food domain knowledge graph based on Wikipedia.

scholarly journals Adaptation of a Key Phrase Extractor for Japanese Text

2013 ◽  
Author(s):  
Jerome Mathieu
Keyword(s):  
Efficient Method ◽  
Contextual Information ◽  
Japanese Text ◽  
Keyphrase Extraction ◽  
Statistical Observation ◽  
Extraction Algorithm

This paper presents some statistical observation relevant to Japanese keyphrase extraction, as well as the details of the implementation of a keyphrase extraction algorithm (called Extractor) for Japanese documents. Parts of the algorithm include an efficient method of extracting the keyphrase candidates, a way to pinpoint the most probable keyphrases using contextual information. . .

scholarly journals A comparative study of keyword extraction algorithms for English texts

2021 ◽  
Vol 30 (1) ◽  
pp. 808-815
Author(s):  
Jinye Li
Keyword(s):  
English Literature ◽  
Recall Rate ◽  
English Text ◽  
Keyword Extraction ◽  
Keyphrase Extraction ◽  
Inverse Document Frequency ◽  
Document Frequency ◽  
Analysis Experiment ◽  
Extraction Algorithm ◽  
Precision Rate

Abstract This study mainly analyzed the keyword extraction of English text. First, two commonly used algorithms, the term frequency–inverse document frequency (TF–IDF) algorithm and the keyphrase extraction algorithm (KEA), were introduced. Then, an improved TF–IDF algorithm was designed, which improved the calculation of word frequency, and it was combined with the position weight to improve the performance of keyword extraction. Finally, 100 English literature was selected from the British Academic Written English Corpus for the analysis experiment. The results showed that the improved TF–IDF algorithm had the shortest running time and took only 4.93 s in processing 100 texts; the precision of the algorithms decreased with the increase of the number of extracted keywords. The comparison between the two algorithms demonstrated that the improved TF–IDF algorithm had the best performance, with a precision rate of 71.2%, a recall rate of 52.98%, and an F 1 score of 60.75%, when five keywords were extracted from each article. The experimental results show that the improved TF–IDF algorithm is effective in extracting English text keywords, which can be further promoted and applied in practice.

Geoscience keyphrase extraction algorithm using enhanced word embedding

2019 ◽  
Vol 125 ◽  
pp. 157-169 ◽  
Author(s):  
Qinjun Qiu ◽  
Zhong Xie ◽  
Liang Wu ◽  
Wenjia Li
Keyword(s):  
Word Embedding ◽  
Keyphrase Extraction ◽  
Extraction Algorithm
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