Keyword Extraction from Arabic Documents using Term Equivalence Classes

2015 ◽  
Vol 14 (2) ◽  
pp. 1-18 ◽  
Author(s):  
Arafat Awajan
Keyword(s):  
Equivalence Classes ◽  
Keyword Extraction ◽  
Term Equivalence

Automatic Keyword Extraction from Medical and Healthcare Curriculum

2016 ◽  
Author(s):  
Martin Komenda ◽  
Matěj Karolyi ◽  
Andrea Pokorná ◽  
Martin Víta ◽  
Vincent Kríž
Keyword(s):  
Keyword Extraction ◽  
Medical And Healthcare

Modeling, inference and clustering for equivalence classes of 3-D orientations

2014 ◽  
Author(s):  
Chuanlong Du
Keyword(s):  
Equivalence Classes

scholarly journals Keyword extraction method for machine reading comprehension based on natural language processing

2021 ◽  
Vol 1955 (1) ◽  
pp. 012072
Author(s):  
Ruiheng Li ◽  
Xuan Zhang ◽  
Chengdong Li ◽  
Zhongju Zheng ◽  
Zihang Zhou ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Extraction Method ◽  
Keyword Extraction ◽  
Machine Reading

4W1H Keyword Extraction based Summarization Model

2021 ◽  
Author(s):  
Seungyeon Lee ◽  
Taewon Park ◽  
Minho Lee
Keyword(s):  
Keyword Extraction

Video Semantic Keyword Extraction Based on Multi-Channel Information

2020 ◽  
Author(s):  
Shirou Ou ◽  
Ruomei Wang ◽  
Fan Zhou ◽  
Shujin Lin
Keyword(s):  
Keyword Extraction ◽  
Channel Information

scholarly journals MET: a Java package for fast molecule equivalence testing

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Jördis-Ann Schüler ◽  
Steffen Rechner ◽  
Matthias Müller-Hannemann
Keyword(s):  
Chemical Properties ◽  
Graph Isomorphism ◽  
Isomorphism Problem ◽  
Equivalence Classes ◽  
Equivalence Testing ◽  
Equivalence Test ◽  
Graph Isomorphism Problem ◽  
2D Structure ◽  
Node Labels ◽  
Labelled Graphs

AbstractAn important task in cheminformatics is to test whether two molecules are equivalent with respect to their 2D structure. Mathematically, this amounts to solving the graph isomorphism problem for labelled graphs. In this paper, we present an approach which exploits chemical properties and the local neighbourhood of atoms to define highly distinctive node labels. These characteristic labels are the key for clever partitioning molecules into molecule equivalence classes and an effective equivalence test. Based on extensive computational experiments, we show that our algorithm is significantly faster than existing implementations within , and . We provide our Java implementation as an easy-to-use, open-source package (via GitHub) which is compatible with . It fully supports the distinction of different isotopes and molecules with radicals.

scholarly journals Offensive keyword extraction based on the attention mechanism of BERT and the eigenvector centrality using a graph representation

2021 ◽  
Author(s):  
Gretel Liz De la Peña Sarracén ◽  
Paolo Rosso
Keyword(s):  
Language Model ◽  
Hybrid Approach ◽  
Attention Mechanism ◽  
Graph Representation ◽  
Keyword Extraction ◽  
Eigenvector Centrality ◽  
Model Learning ◽  
Harmful Content ◽  
The Impact ◽  
Offensive Language

AbstractThe proliferation of harmful content on social media affects a large part of the user community. Therefore, several approaches have emerged to control this phenomenon automatically. However, this is still a quite challenging task. In this paper, we explore the offensive language as a particular case of harmful content and focus our study in the analysis of keywords in available datasets composed of offensive tweets. Thus, we aim to identify relevant words in those datasets and analyze how they can affect model learning. For keyword extraction, we propose an unsupervised hybrid approach which combines the multi-head self-attention of BERT and a reasoning on a word graph. The attention mechanism allows to capture relationships among words in a context, while a language model is learned. Then, the relationships are used to generate a graph from what we identify the most relevant words by using the eigenvector centrality. Experiments were performed by means of two mechanisms. On the one hand, we used an information retrieval system to evaluate the impact of the keywords in recovering offensive tweets from a dataset. On the other hand, we evaluated a keyword-based model for offensive language detection. Results highlight some points to consider when training models with available datasets.

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