Variations on Abstract Semantic Spaces

2020 ◽  
pp. 71-99
Author(s):  
Katrin Erk
Keyword(s):  
Semantic Spaces

scholarly journals A Framework Based on Semantic Spaces and Glyphs for Social Sensing on Twitter

2016 ◽  
Vol 88 ◽  
pp. 107-114 ◽  
Author(s):  
Giovanni Pilato ◽  
Umberto Maniscalco
Keyword(s):  
Social Sensing ◽  
Semantic Spaces

A fuzzy integral based methodology to elicit semantic spaces in usability tests

2014 ◽  
Vol 44 (1) ◽  
pp. 11-17 ◽  
Author(s):  
M. Camargo ◽  
L. Wendling ◽  
E. Bonjour
Keyword(s):  
Fuzzy Integral ◽  
Usability Tests ◽  
Semantic Spaces

scholarly journals Comparison of Templates with Word2vec in Finding Semantic Relations Between Words

2018 ◽  
pp. 13-17
Author(s):  
Kaan Ant ◽  
Ugur Sogukpinar ◽  
Mehmet Fatif Amasyali
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Semantic Relations ◽  
Template Method ◽  
Semantic Relationships ◽  
Semantic Spaces

The use of databases those containing semantic relationships between words is becoming increasingly widespread in order to make natural language processing work more effective. Instead of the word-bag approach, the suggested semantic spaces give the distances between words, but they do not express the relation types. In this study, it is shown how semantic spaces can be used to find the type of relationship and it is compared with the template method. According to the results obtained on a very large scale, while is_a and opposite are more successful for semantic spaces for relations, the approach of templates is more successful in the relation types at_location, made_of and non relational.

Bridging the Gap of Dimensions in Distillation: Understanding the knowledge transfer between different-dimensional semantic spaces

2021 ◽  
Author(s):  
Zhiyuan Ma ◽  
Ziyue Song ◽  
Haodong Zhao ◽  
Kui Meng ◽  
Gongshen Liu
Keyword(s):  
Knowledge Transfer ◽  
Semantic Spaces

scholarly journals Word Embeddings as Metric Recovery in Semantic Spaces

2016 ◽  
Vol 4 ◽  
pp. 273-286 ◽  
Author(s):  
Tatsunori B. Hashimoto ◽  
David Alvarez-Melis ◽  
Tommi S. Jaakkola
Keyword(s):  
Random Walks ◽  
Manifold Learning ◽  
State Of The Art ◽  
Inductive Reasoning ◽  
Semantic Space ◽  
Word Embedding ◽  
Word Embeddings ◽  
Recovery Algorithm ◽  
Series Completion ◽  
Semantic Spaces

Continuous word representations have been remarkably useful across NLP tasks but remain poorly understood. We ground word embeddings in semantic spaces studied in the cognitive-psychometric literature, taking these spaces as the primary objects to recover. To this end, we relate log co-occurrences of words in large corpora to semantic similarity assessments and show that co-occurrences are indeed consistent with an Euclidean semantic space hypothesis. Framing word embedding as metric recovery of a semantic space unifies existing word embedding algorithms, ties them to manifold learning, and demonstrates that existing algorithms are consistent metric recovery methods given co-occurrence counts from random walks. Furthermore, we propose a simple, principled, direct metric recovery algorithm that performs on par with the state-of-the-art word embedding and manifold learning methods. Finally, we complement recent focus on analogies by constructing two new inductive reasoning datasets—series completion and classification—and demonstrate that word embeddings can be used to solve them as well.

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