Using Word Embeddings to Enhance Keyword Identification for Scientific Publications

Wang, Rui; Liu, Wei; McDonald, Chris

doi:10.1007/978-3-319-19548-3_21

Cited by 48 publications

(56 citation statements)

References 12 publications

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“…Each document has two lists of keyphrases assigned by humans -controlled, which are assigned by the authors, and uncontrolled, which are freely assigned by the readers. The controlled keyphrases are mostly abstractive, whereas the uncontrolled ones are mostly extractive (Wang et al, 2015). The Semeval 2010 dataset (Kim et al, 2010) consists of 284 full length ACM articles divided into a test set of size 100, training set of size 144 and trial set of size 40.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…(Liu et al, 2009;Danesh et al, 2015;Bougouin et al, 2013;Wang et al, 2015) for Avg. F1@10 on Inspec dataset.…”

Section: R(cmentioning

confidence: 99%

“…Some estimates of a maximally admissible degree of measurement error are obtained that make it possible, using the apparatus of fuzzy set theory, to correctly identify the current state of a system. Automatically identified keywords: complex systems, fuzzy information, admissible measurement errors, fuzzy values, informative attributes measurement error, maximally admissible degree, fuzzy set theory Manually assigned keywords: complex system state identification, admissible measurement errors, informative attributes, measurement errors fuzzy set theory Word embeddings have already shown promising results in the process of keyphrase extraction from scientific articles (Wang et al, 2015(Wang et al, , 2014. However, Wang et al did not use domain-specific word embeddings and had suggested that training them might lead to improvements.…”

Section: Introductionmentioning

confidence: 99%

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Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings

Mahata¹,

John²,

Shah³

et al. 2018

Preprint

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Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases. In this paper, we present an unsupervised technique (Key2Vec) that leverages phrase embeddings for ranking keyphrases extracted from scientific articles. Specifically, we propose an effective way of processing text documents for training multi-word phrase embeddings that are used for thematic representation of scientific articles and ranking of keyphrases extracted from them using theme-weighted PageRank. Evaluations are performed on benchmark datasets producing state-of-the-art results.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

“…(Liu et al, 2009;Danesh et al, 2015;Bougouin et al, 2013;Wang et al, 2015) for Avg. F1@10 on Inspec dataset.…”

Section: R(cmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings

Mahata¹,

John²,

Shah³

et al. 2018

Preprint

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show abstract

“…In unsupervised strategies graph-based techniques have been a popular choice for keyword extraction [29,145,211,212,213,228]. These techniques are inspired by the PageRank algorithm [159]; in these techniques the words of a document are modelled as nodes, while the edges between them define the relatedness between them [29,209,211].…”

Section: Unsupervised Strategiesmentioning

confidence: 99%

Utilising Wikipedia for Text Mining Applications

Qureshi

2016

SIGIR Forum

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The process whereby inferences are made from textual data is broadly referred to as text mining. In order to ensure the quality and effectiveness of the derived inferences, several approaches have been proposed for different text mining applications. Among these applications, classifying a piece of text into pre-defined classes through the utilisation of training data falls into supervised approaches while arranging related documents or terms into clusters falls into unsupervised approaches. In both these approaches, processing is undertaken at the level of documents to make sense of text within those documents. Recent research efforts have begun exploring the role of knowledge bases in solving the various problems that arise in the domain of text mining. Of all the knowledge bases, Wikipedia on account of being one of the largest human-curated, online encyclopaedia has proven to be one of the most valuable resources in dealing with various problems in the domain of text mining. However, previous Wikipedia-based research efforts have not taken both Wikipedia categories and Wikipedia articles together as a source of information. This thesis serves as a first step in eliminating this gap and throughout the contributions made in this thesis, we have shown the effectiveness of Wikipedia category-article structure for various text mining tasks. Wikipedia categories are organized in a taxonomical manner serving as semantic tags for Wikipedia articles and this provides a strong abstraction and expressive mode of knowledge representation. In this thesis, we explore the effectiveness of this mode of Wikipedia's expression (i.e., the category-article structure) via its application in the domains of text classification, subjectivity analysis (via a notion of "perspective" in news search), and keyword extraction. First, we show the effectiveness of exploiting Wikipedia for two classification tasks i.e., 1- classifying the tweets1 being relevant/irrelevant to an entity or brand, 2- classifying the tweets into different topical dimensions such as tweets related with workplace, innovation, etc. To do so, we define the notion of relatedness between the text in tweet and the information embedded within the Wikipedia category-article structure. Then, we present an application in the area of news search by using the same notion of relatedness to show more information related to each search result highlighting the amount perspective or subjective bias in each returned result towards a certain opinion, topical drift, etc. Finally, we present a keyword extraction strategy using community detection over the Wikipedia categories to discover related keywords arranged in different communities. The relationship between Wikipedia categories and articles is explored via a textual phrase matching framework whereby the starting point is textual phrases that match Wikipedia articles' titles/redirects. The Wikipedia articles for which a match occurs are then utilised by extraction of their associated categories, and these Wikipedia categories are used to derive various structural measures such as those relating to taxonomical depth and Wikipedia articles they contain. These measures are utilised in our proposed text classification, subjectivity analysis, and keyword extraction framework and the performance is analysed via extensive experimental evaluations. These experimental evaluations undertake comparisons with standard text mining approaches in the literature and our Wikipedia framework based on its category-article structure outperforms the standard text mining techniques.

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“…However, only a small portion of documents contains author-assigned keyphrases and a majority of documents do not have keyphrases. Therefore, extracting keyphrases from documents has become one of the main concerns in recent days, and there have been several studies on automatic keyphrase extraction task [1][2][3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Latent Keyphrase Extraction Using Deep Belief Networks

Lee

2015

Int. J. Fuzzy Log. Intell. Syst.

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Nowadays, automatic keyphrase extraction is considered to be an important task. Most of the previous studies focused only on selecting keyphrases within the body of input documents. These studies overlooked latent keyphrases that did not appear in documents. In addition, a small number of studies on latent keyphrase extraction methods had some structural limitations. Although latent keyphrases do not appear in documents, they can still undertake an important role in text mining because they link meaningful concepts or contents of documents and can be utilized in short articles such as social network service, which rarely have explicit keyphrases. In this paper, we propose a new approach that selects qualified latent keyphrases from input documents and overcomes some structural limitations by using deep belief networks in a supervised manner. The main idea of this approach is to capture the intrinsic representations of documents and extract eligible latent keyphrases by using them. Our experimental results showed that latent keyphrases were successfully extracted using our proposed method.

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Using Word Embeddings to Enhance Keyword Identification for Scientific Publications

Cited by 48 publications

References 12 publications

Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings

Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings

Utilising Wikipedia for Text Mining Applications

Latent Keyphrase Extraction Using Deep Belief Networks

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