Using semi-structured data for assessing research paper similarity

Martín, Germán Hurtado; Schockaert, Steven; Cornelis, Chris; Naessens, Helga

doi:10.1016/j.ins.2012.09.044

Cited by 13 publications

(16 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although econo-ESA works in an index matrix such as (Aryafar and Shokoufandeh 2011; Gottron et al 2011; Hassan and Mihalcea 2009; Martín et al 2013; Polajnar et al 2013; Popescu and Grefenstette 2011; Potthast et al 2008; Scholl et al 2010; Sorg and Cimiano 2012; Szczuka et al 2011; Szarvas et al 2011), we do not propose a new index matrix. We intend to investigate the dimensions of the matrix by showing that econo-ESA decreases the index matrix’s dimensions appropriately.…”

Section: Improvements To the Methodsmentioning

confidence: 97%

“…Hassan and Mihalcea (2009) used Wikipedia’s graph of category and intra-lingual links; they used the category to scale a concept’s weight based on its distance to the root and its intra-lingual link to map a concept into the other languages. Martín et al (2013) used a collection of research papers as an ESA corpus. Potthast et al (2008) proposed a cross-language ESA (CL-ESA) for cross-language similarity measurements.…”

Section: Improvements To the Methodsmentioning

confidence: 99%

“…Hassan and Mihalcea (2009) replaced cosine similarity with a Lesk-like metric. Martín et al (2013) explored two similarity measurements of two concept vectors: a cosine and a generalized jaccard. Sorg and Cimiano (2010) explored the TFIDF, KL-divergence, and LM schemes in addition to cosine similarity.…”

Section: Improvements To the Methodsmentioning

confidence: 99%

“…Although it is obviously easy to use other schemes in our proposal, we do not propose a new similarity scheme such as (Aggarwal et al 2012; Hassan and Mihalcea 2009; Martín et al 2013; Sorg and Cimiano 2010; Szarvas et al 2011; Yan and Jin 2012). …”

Section: Improvements To the Methodsmentioning

confidence: 99%

“…Researchers have further developed ESA for use in different environments, including information retrieval (Hassan and Mihalcea 2009; Polajnar et al 2013; Potthast et al 2008; Potthast et al 2012; Scholl et al 2010; Sorg and Cimiano 2010; 2012; Tanase and Kapetanios 2012), image retrieval (Popescu and Grefenstette 2011; Zhang et al 2012), semantic text similarity (STS) (Aggarwal et al 2012; Martín et al 2013; Szarvas et al 2011), categorization (O’Banion et al 2012; Szarvas et al 2011; Szczuka et al 2011), machine translation (Matsuno and Ishida 2011), and question-answering (Walter et al 2012). It is also used in knowledge discovery (Yan and Jin 2012), music classification (Aryafar and Shokoufandeh 2011), learning systems (Schmidt et al 2011), text disambiguation (Fernandez et al 2011), and case based reasoning systems (Patelia et al 2011).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Econo-ESA in semantic text similarity

Rahutomo

Aritsugi

2014

SpringerPlus

View full text Add to dashboard Cite

Explicit semantic analysis (ESA) utilizes an immense Wikipedia index matrix in its interpreter part. This part of the analysis multiplies a large matrix by a term vector to produce a high-dimensional concept vector. A similarity measurement between two texts is performed between two concept vectors with numerous dimensions. The cost is expensive in both interpretation and similarity measurement steps. This paper proposes an economic scheme of ESA, named econo-ESA. We investigate two aspects of this proposal: dimensional reduction and experiments with various data. We use eight recycling test collections in semantic text similarity. The experimental results show that both the dimensional reduction and test collection characteristics can influence the results. They also show that an appropriate concept reduction of econo-ESA can decrease the cost with minor differences in the results from the original ESA.Electronic supplementary materialThe online version of this article (doi:10.1186/2193-1801-3-149) contains supplementary material, which is available to authorized users.

show abstract

Section: Improvements To the Methodsmentioning

confidence: 97%

Section: Improvements To the Methodsmentioning

confidence: 99%

Section: Improvements To the Methodsmentioning

confidence: 99%

Section: Improvements To the Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Econo-ESA in semantic text similarity

Rahutomo

Aritsugi

2014

SpringerPlus

View full text Add to dashboard Cite

show abstract

A Deep Learning Approach for Scientific Paper Semantic Ranking

Gargiulo

Silvestri

Fontanella

et al. 2017

Intelligent Interactive Multimedia Systems and Services 2017

View full text Add to dashboard Cite

On the selection of the correct number of terms for profile construction: Theoretical and empirical analysis

Campos

Fernández‐Luna

Huete

2018

Information Sciences

View full text Add to dashboard Cite

In this paper, we examine the problem of building a user profile from a set of documents. This profile will consist of a subset of the most representative terms in the documents that best represent user preferences or interests. Inspired by the discrete concentration theory we have conducted an axiomatic study of seven properties that a selection function should fulfill: the minimum and maximum uncertainty principle, invariant to adding zeros, invariant to scale transformations, principle of nominal increase, transfer principle and the richest get richer inequality. We also present a novel selection function based on the use of similarity metrics, and more specifically the cosine measure which is commonly used in information retrieval, and demonstrate that this verifies six of the properties in addition to a weaker variant of the transfer principle, thereby representing a good selection approach.The theoretical study was complemented with an empirical study to compare the performance of different selection criteria (weight-and unweight-based) using real data in a parliamentary setting. In this study, we analyze the performance of the different functions focusing on the two main factors affecting the selection process: profile size (number of terms) and weight distribution. These profiles are then used in a document filtering task to show that our similaritybased approach performs well in terms not only of recommendation accuracy but also efficiency (we obtain smaller profiles and consequently faster recommendations).

show abstract

Using semi-structured data for assessing research paper similarity

Cited by 13 publications

References 37 publications

Econo-ESA in semantic text similarity

Econo-ESA in semantic text similarity

A Deep Learning Approach for Scientific Paper Semantic Ranking

On the selection of the correct number of terms for profile construction: Theoretical and empirical analysis

Contact Info

Product

Resources

About