Open Universiteit

Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/9650
Title: Unlocking the Power of Word2Vec for Identifying Implicit Links
Authors: Gutu, Gabriel
Dascalu, Mihai
Ruseti, Stefan
Rebedea, Traian
Trausan-Matu, Stefan
Keywords: implicit links
CSCL
Word2Vec
semantic models
text cohesion
Issue Date: 2017
Publisher: IEEE
Citation: Gutu, G., Dascalu, M., Ruseti, S., Rebedea, T., & Trausan-Matu, S. (2017). Unlocking the Power of Word2Vec for Identifying Implicit Links. In 17th IEEE Int. Conf. on Advanced Learning Technologies (ICALT2017) (pp. 199–200). Timisoara, Romania: IEEE.
Abstract: This paper presents a research on using Word2Vec for determining implicit links in multi-participant Computer-Supported Collaborative Learning chat conversations. Word2Vec is a powerful and one of the newest Natural Language Processing semantic models used for computing text cohesion and similarity between documents. This research considers cohesion scores in terms of the strength of the semantic relations established between two utterances; the higher the score, the stronger the similarity between two utterances. An implicit link is established based on cohesion to the most similar previous utterance, within an imposed window. Three similarity formulas were used to compute the cohesion score: an unnormalized score, a normalized score with distance and Mihalcea’s formula. Our corpus of conversations incorporated explicit references provided by authors, which were used for validation. A window of 5 utterances and a 1- minute time frame provided the highest detection rate both for exact matching and matching of a block of continuous utterances belonging to the same speaker. Moreover, the unnormalized score correctly identified the largest number of implicit links.
URI: http://hdl.handle.net/1820/9650
Appears in Collections:1. RAGE Publications

Files in This Item:
File Description SizeFormat 
3870a199.pdf216.42 kBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons