Research on Weighted Complex Network Based Keywords Extraction

Zhou, Zhi; Zou, Xiaojun; Lv, Xueqiang; Hu, Junfeng

doi:10.1007/978-3-642-45185-0_47

Cited by 10 publications

(8 citation statements)

References 5 publications

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“…relation is calculated between the linguistic entities as a closeness measure among them. Zhou et al [18] Graph is constructed with keywords using Jaccard coefficient similarity measure.…”

Section: Semanticmentioning

confidence: 99%

Machine Learning Methods for Keyword Extraction and Indexing

2019

IJITEE

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The digital age results in the creation of massive information. It is a common tradition among the users to digitalize almost every moment of daily life, since it has become convenient to fetch the information as and when needed from the Internet. User can able to retrieve information by providing query keyword. The objective of the search is to quickly return the set of most relevant documents given a search string. Accomplishing this task for a fixed query involves determining the most relevant documents form the big-data. Queries given to the IR systems are enabled by the keywords. Keyword extraction is a process of identifying the document. Manual keyword extraction is cumbersome and it is in feasible to efficiently identify all the keywords in the document. Therefore the machine learning approaches for keyword extraction are proposed. In this paper various machine learning approaches have discussed along with its merits and de-merits. Here we are also proposing a trained index structure which is efficient to identify the specific locus of the record.

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“…relation is calculated between the linguistic entities as a closeness measure among them. Zhou et al [18] Graph is constructed with keywords using Jaccard coefficient similarity measure.…”

Section: Semanticmentioning

confidence: 99%

Machine Learning Methods for Keyword Extraction and Indexing

2019

IJITEE

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“…Nuestra tarea está muy relacionada con la extracción de temas (topics) y palabras clave (keywords), entendidas estas como términos que representan el contenido fundamental del texto (Ercan & Cicekli, 2007;Grineva et al, 2009), en el procesamiento del lenguaje natural y, en este ámbito, hay ya numerosos trabajos que emplean de alguna forma el análisis de grafos para dicha extracción (Mihalcea & Radev, 2011, pp. 154-162;Lahiri, 2013;Zhou et al, 2013;Bougouin et al, 2013;Boudin, 2018;Garg & Kumar, 2018), pero también con otras áreas como la calidad textual y el análisis estilístico por medio de grafos (Antiqueira et al, 2005(Antiqueira et al, , 2007Amancio, 2015). De los primeros tomamos la idea de que los grafos son una herramienta adecuada para analizar la temática de los textos; pero, a diferencia de la mayoría de ellos, nuestro objetivo no es la indexación de textos, sino el estudio de la organización de textos particulares.…”

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Progresión temática y cohesión textual a través de grafos de coocurrencias

Sánchez¹

2020

CLAC

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Este artículo estudia la posibilidad de aplicar la teoría de grafos o redes al análisis de la progresión temática y la cohesión textual. Para ello, se analizan seis textos de divulgación científica: tres sobre el mismo tema, el aceite de palma, otro sobre el aceite de oliva y dos de control; uno creado por la unión de dos textos de temas diferentes y el otro por párrafos con distintas temáticas seleccionados aleatoriamente. Se ha creado, de cada texto, un grafo no dirigido y pesado de coocurrencias con ventana 5 gram. En primer lugar, se han analizado las medidas globales del grafo para conocer su topología; en segundo lugar, se ha empleado el grado de intermediación de los lemas para conocer los temas de cada texto y se ha estudiado como estos evolucionan; en tercer lugar, a través de la modularidad del grafo, se ha analizado en qué párrafos aparecen los diferentes temas y cómo ha evolucionado.

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“…Zhou et al (2013) in [132] investigate a weighted complex network based keyword extraction incorporating the exploration of the network structure and linguistics knowledge. The focus is on the construction of a lexical network including the reasonable selection of vertices, the proper description of the relationships between words, a simple weighted network and TF-IDF.…”

Section: Graph-basedmentioning

confidence: 99%

“…[59,80,91,96,127] Closeness* Reciprocal of the sum of distances of all vertices to some vertex. [59,75,80,96,101,127,132] Community detection* Community detection techniques are based on the principles which detect nodes with dense internal connections and sparser connections between groups.…”

Section: Name Definitionmentioning

confidence: 99%

“…Keyword extraction (KE) is tasked with the automatic identification of a set of the terms that best describe the subject of a document [75,79,80,87,96,99,106,110,122,132]. Different terminology for defining the terms that represent the most relevant information contained in the document is used: key phrases, key segments, key terms or just keywords .…”

Section: Introductionmentioning

confidence: 99%

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Language networks

Physics Subject Headings (PhySH)

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The language networks book provides insights into the principles of modeling and analyzing structural properties of language-manly in its written form, hence text. Book guidelines the basic principles of text preprocessing, covering the very initial steps needed for any natural language processing task. Further, the book examines the possibilities of representing text in a complex networks framework. The second part overviews the application of language networks as one of a data science disciplines. It covers important data science topics for the processing of the big textual data from extracting the most salient structural parts of documents, across differentiation between text genres to predicting the speeding of the information through social media. Finally, the last part of the book is tasked with formal modeling of the linguistic subsystems in a multilayer complex networks formalism, which allows systematic study of language across all of its subsystems. The first part of the book studies the general principles of language networks construction and analysis. It covers language network construction types. Specifically, it analyzes the effects of constructing directed vs. undirected, weighted vs. unweighted network from lemmatized (stemmed) or non-lemmatized texts with stopwords included or excluded. The effects of text randomization are studied enabling better insights into characteristics of language networks compared to their shuffled counterparts. Some preliminary experiments reveal the possibilities of the differentiation of the structural properties of networks constructed from different text types and in different languages like Croatian, English, and Italian. Next, some initial insights into the characterization of syllabic networks are presented. The analysis of motifs of the linguistic networks reveals the typical building blocks of the structure of networks of the literature in the Croatian language. Finally, the first part of the book concludes with the LaNCoA a Python Toolkit for the construction and analysis of language networks implementing the majority of the findings presented in this part of the book. The second part of the book is dedicated to the applications of language networks. The language networks enable the extraction of the most salient words in texts-keywords and extraction of the domain knowledge-context studied on the content of Wikipedia entries. The applicative part of language networks includes the differentiation between different text types and polarization of tweets, as well. Finally, the possibilities of predicting the future content of tweets solely from the structural properties of the complex language networks are presented. Contents I Language Networks Construction

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Research on Weighted Complex Network Based Keywords Extraction

Cited by 10 publications

References 5 publications

Machine Learning Methods for Keyword Extraction and Indexing

Machine Learning Methods for Keyword Extraction and Indexing

Progresión temática y cohesión textual a través de grafos de coocurrencias

Language networks

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