A New Domain Independent Keyphrase Extraction System

Pudota, Nirmala; Dattolo, Antonina; Baruzzo, Andrea; Tasso, Carlo

doi:10.1007/978-3-642-15850-6_8

Cited by 11 publications

(6 citation statements)

References 16 publications

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“…In order to accomplish our goals and to take into consideration our previous work on keyphrase extraction for English texts (Pudota et al, 2010), we propose here a Knowledge-Based KP extraction technique based upon (i) exploitation of several kinds of knowledge, (ii) consideration of the specific languages addressed, and (iii) typical/common writing styles. An initial design work of knowledge engineering allowed us to identify four classes of knowledge which can be exploited to recognize meaningful phrases in a text:…”

Section: A Knowledge-based Approach To Keyphrase Generationmentioning

confidence: 99%

“…In order to support our claims we have developed DIKpE-G, a revised extended version of the system presented in (Pudota et al, 2010) and (De Nart and Tasso, 2014). DIKpE-G stands for Domain Independent Keyphrase Extractor -Generator.…”

Section: System Overviewmentioning

confidence: 99%

“…The highly modular architecture of DIKpE-G, allows a seamless substitution of the modules and submodules devoted to ranking, permitting in such a way the experimentation of alternative approaches. The current DIKpE-G prototype follows the approach proposed in (Pudota et al, 2010), which adheres to a numerical approach: each feature is given a numerical value and all the features are then combined in order to compute a unique index called keyphraseness, which represents how much a candidate KP is considered suitable and significant for representing the content of the input text. The keyphraseness index is computed in the current DIKpE-G prototype as a weighted linear combination of the features values.…”

Section: System Overviewmentioning

confidence: 99%

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A New Multi-lingual Knowledge-base Approach to Keyphrase Extraction for the Italian Language

Degl’Innocenti

Nart

Tasso

2014

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

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Associating meaningful keyphrases to text documents and Web pages is an activity that can significantly increase the accuracy of Information Retrieval, Personalization and Recommender systems, but the growing amount of text data available is too large for an extensive manual annotation. On the other hand, automatic keyphrase generation can significantly support this activity. This task is already performed with satisfactory results by several systems proposed in the literature, however, most of them focuses solely on the English language which represents approximately more than 50% of Web contents. Only few other languages have been investigated and Italian, despite being the ninth most used language on the Web, is not among them. In order to overcome this shortage, we propose a novel multi-language, unsupervised, knowledge-based approach towards keyphrase generation. To support our claims, we developed DIKpE-G, a prototype system which integrates several kinds of knowledge for selecting and evaluating meaningful keyphrases, ranging from linguistic to statistical, meta/structural, social, and ontological knowledge. DIKpE-G performs well over English and Italian texts.

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Section: A Knowledge-based Approach To Keyphrase Generationmentioning

confidence: 99%

Section: System Overviewmentioning

confidence: 99%

Section: System Overviewmentioning

confidence: 99%

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A New Multi-lingual Knowledge-base Approach to Keyphrase Extraction for the Italian Language

Degl’Innocenti

Nart

Tasso

2014

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

Self Cite

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“…Pudota et al (2010) in [113] design a domain independent keyphrase extraction system that can extract potential phrases from a single document in an unsupervised, domain-independent way. They engaged n-grams, but they also incorporated linguistic knowledge (POS tags) and statistics (frequency, position, lifespan) of each n-gram in defining candidate phrases and their respective feature sets.…”

Section: Unsupervisedmentioning

confidence: 99%

“…Other Harmonic centrality, LIN centrality, Katz centrality, Wiener index, eccentricity, connectedness [127], POS tags [92,113], CRF [129], LexRank [85], Semanti-cRank [118], SimRank, etc. For the network based keyword extraction we use the data set composed of Croatian news articles [107].…”

Section: Name Definitionmentioning

confidence: 99%

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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A Content-Based Approach to Social Network Analysis: A Case Study on Research Communities

Nart

Degl’Innocenti

Basaldella

et al. 2016

Communications in Computer and Information Science

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Several works in literature investigated the activities of research communities using big data analysis, but the large majority of them focuses on papers and co-authorship relations, ignoring that most of the scientific literature available is already clustered into journals and conferences with a well defined domain of interest. We are interested in bringing out underlying implicit relationships among such containers and more specifically we are focusing on conferences and workshop proceedings available in open access and we exploit a semantic/conceptual analysis of the full free text content of each paper. We claim that such content-based analysis may lead us to a better understanding of the research communities’ activities and their emerging trends. In this work we present a novel method for research communities activity analysis, based on the combination of the results of a Social Network Analysis phase and a Content-Based one. The major innovative contribution of this work is the usage of knowledge-based techniques to meaningfully extract from each of the considered papers the main topics discussed by its authors

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A New Domain Independent Keyphrase Extraction System

Cited by 11 publications

References 16 publications

A New Multi-lingual Knowledge-base Approach to Keyphrase Extraction for the Italian Language

A New Multi-lingual Knowledge-base Approach to Keyphrase Extraction for the Italian Language

Language networks

A Content-Based Approach to Social Network Analysis: A Case Study on Research Communities

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