scholarly journals Semantic Similarity Matching Using Contextualized Representations

2021 ◽  
Author(s):  
Farhood Farahnak ◽  
Elham Mohammadi ◽  
MohammadReza Davari ◽  
Leila Kosseim
Keyword(s):  
Semantic Similarity ◽  
Similarity Matching

scholarly journals A Semantic Recommender Engine for Idea Generation Improvement

2018 ◽  
Vol 11 (3) ◽  
pp. 112
Author(s):  
Maria El Haiba ◽  
Lamyaa Elbassiti ◽  
Rachida Ajhoun
Keyword(s):  
Recommender Systems ◽  
Semantic Similarity ◽  
Idea Generation ◽  
Global Market ◽  
System Innovation ◽  
Similarity Matching ◽  
New Ideas ◽  
Creative Ideation

In order to develop the ability to be competitive in considering rapidly growing global market and enormously changing in technology, organizations are looking for up-to-date procedures to respond to all these transformations. Being smart and innovative is actually the most significant pillars of successful organization strategies. In other words, organizations need to encourage learning, manage knowledge and create innovative ideas. A major issue of creative ideation is improving the quality of the ideas generated. In this paper, we propose a semantic recommender engine for idea generation in order to assist organizations to improve their ways of generating new ideas. Through this novel system, innovation actors will be able to consider new perspectives, make new connections, think differently and thus produce new promising ideas. We initially introduce the concept behind a smart organization, explore the idea generation in such organizations and examine the role of recommender systems for managing this stage and identifying breakthrough ideas. Next, we present the context of design, the conceptual architecture of the suggested system and finally expand the workflow of semantic similarity matching of ideas with a focus on the key components of the semantic recommendation engine.

Document- and Keyword-based Author Co-citation Analysis

2018 ◽  
Vol 2 (2) ◽  
pp. 70-82 ◽  
Author(s):  
Binglu Wang ◽  
Yi Bu ◽  
Win-bin Huang
Keyword(s):  
Citation Analysis ◽  
Semantic Similarity ◽  
Method Validation ◽  
New Method ◽  
Global Network ◽  
Network Visualization ◽  
Knowledge Domain ◽  
Knowledge Domains ◽  
Domain Mapping ◽  
The Relationship

AbstractIn the field of scientometrics, the principal purpose for author co-citation analysis (ACA) is to map knowledge domains by quantifying the relationship between co-cited author pairs. However, traditional ACA has been criticized since its input is insufficiently informative by simply counting authors’ co-citation frequencies. To address this issue, this paper introduces a new method that reconstructs the raw co-citation matrices by regarding document unit counts and keywords of references, named as Document- and Keyword-Based Author Co-Citation Analysis (DKACA). Based on the traditional ACA, DKACA counted co-citation pairs by document units instead of authors from the global network perspective. Moreover, by incorporating the information of keywords from cited papers, DKACA captured their semantic similarity between co-cited papers. In the method validation part, we implemented network visualization and MDS measurement to evaluate the effectiveness of DKACA. Results suggest that the proposed DKACA method not only reveals more insights that are previously unknown but also improves the performance and accuracy of knowledge domain mapping, representing a new basis for further studies.

The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

2020 ◽  
Author(s):  
Kun Sun
Keyword(s):  
Eye Tracking ◽  
Semantic Similarity ◽  
Cognitive Processing ◽  
Language Processing ◽  
Language Comprehension ◽  
Word Processing ◽  
Reading Time ◽  
Computational Models ◽  
Tracking Data ◽  
Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

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