Integrated concept blending with vector space models

Calvo, Hiram; Méndez, Oscar; Moreno-Armendáriz, Marco A.

doi:10.1016/j.csl.2016.01.004

Cited by 17 publications

(14 citation statements)

References 11 publications

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“…The experimental results show that for the citation recommendation, topological-based similarity is better as compared to textual-based similarity. Secondly, the results of cosine and jaccard similarity are analyzed, where cosine competed jaccard similarity 8 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 03 2019 -06 2019 | Volume 6 | Issue 19| e2 with highest score. Afterwards, we evaluated the centrality measures to check which centrality measures is best to find the influential papers.…”

Section: Resultsmentioning

confidence: 99%

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Centrality-Based Paper Citation Recommender System

Samad¹,

Islam²,

Iqbal³

et al. 2019

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Researchers cite papers in order to connect the new research ideas with previous research. For the purpose of finding suitable papers to cite, researchers spend a considerable amount of time and effort. To help researchers in finding relevant/important papers, we evaluated textual and topological similarity measures for citation recommendations. This work analyzes textual and topological similarity measures (i.e., Jaccard and Cosine) to evaluate which one performs well in finding similar papers? To find the importance of papers, we compute centrality measures (i.e., Betweeness, Closeness, Degree and PageRank). After evaluation, it is found that topological-based similarity via Cosine achieved 85.2% and using Jaccard obtained 61.9% whereas textualbased similarity via Cosine on abstract obtained 68.9% and using Cosine on title achieved 37.4% citation links. Likewise, textual-based similarity via Jaccard on abstract obtained 35.4% and using Jaccard on title achieved 28.3% citation links.

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Section: Resultsmentioning

confidence: 99%

“…In this paper, we computed text similarity between set of papers using Title and Abstract. Cosine similarity and Jaccard similarity [25] are used to compute similarity of papers, because these measures are usually used to measure similarity between two vectors [8]. Equation 5 is the Cosine, while Equation 6 represents Jaccard index.…”

Section: Textual Similaritymentioning

confidence: 99%

Centrality-Based Paper Citation Recommender System

Samad¹,

Islam²,

Iqbal³

et al. 2019

EAI Endorsed Transactions on Industrial Networks and Intelligen

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show abstract

“…REVDICT can be realized directly by comparing the input definitions with all the definitions in the dictionary and returning the most similar ones, without taking into account any semantic or grammatical information (El-Kahlout and Oflazer, 2004). However, REVDICT systems that include semantics give better results, such as in Méndez et al, 2013 andCalvo et al, 2016 where words are represented as vectors in a semantic space.…”

Section: Related Workmentioning

confidence: 99%

IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations

Korenčić¹,

Grubišić²

2022

Preprint

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What is the relation between a word and its description, or a word and its embedding? Both descriptions and embeddings are semantic representations of words. But, what information from the original word remains in these representations? Or more importantly, which information about a word do these two representations share? Definition Modeling and Reverse Dictionary are two opposite learning tasks that address these questions. The goal of the Definition Modeling task is to investigate the power of information laying inside a word embedding to express the meaning of the word in a humanly understandable way -as a dictionary definition. Conversely, the Reverse Dictionary task explores the ability to predict word embeddings directly from its definition. In this paper, by tackling these two tasks, we are exploring the relationship between words and their semantic representations. We present our findings based on the descriptive, exploratory, and predictive data analysis conducted on the CODWOE dataset. We give a detailed overview of the systems that we designed for Definition Modeling and Reverse Dictionary tasks, and that achieved top scores on SemEval-2022 CODWOE challenge in several subtasks. We hope that our experimental results concerning the predictive models and the data analyses we provide will prove useful in future explorations of word representations and their relationships.

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“…There are two classic approaches of the text representation, that is the vector space model (VSM) and word embedding [28]. The VSM maps a document to a great many content-related words or phrases, which successfully translates the textual document calculation to the vector calculation [29]. Word embedding is widely utilized in the natural language processing (NLP) that contains the one-hot representation and distributed representation [32].…”

Section: Social Media Miningmentioning

confidence: 99%

Multifunctional Product Marketing Using Social Media Based on the Variable-Scale Clustering

Wang

Gao

2019

Teh. vjesn.

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Customers' demands have become more dynamic and complicated owing to the functional diversity and lifecycle reduction of products which pushes enterprises to identify the real-time needs of distinct customers in a superior way. Meanwhile, social media turned as an emerging channel where customers often spontaneously can express their perceptions and thoughts about products promptly. This paper examines the customer satisfaction identification and improvement problem based on social media mining. First, we proposed the public opinion sensitivity index (POSI) to uncover target customers from extensive short-textual reviews. Subsequently, we presented a customer segmentation approach based on the sentiment analysis and the variable-scale clustering (VSC). The approach is able to get several customer clusters with the same satisfaction level where customers belonging to each cluster have similar interests. Finally, customer-centered marketing strategies and customer difference marketing campaigns are planned under the shadow of customer segmentation results. The experiments illustrate that our proposed method can support marketing decision marketing in practice that enriches the intention of the current customer relationship management.

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Integrated concept blending with vector space models

Cited by 17 publications

References 11 publications

Centrality-Based Paper Citation Recommender System

Centrality-Based Paper Citation Recommender System

IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations

Multifunctional Product Marketing Using Social Media Based on the Variable-Scale Clustering

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