Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Eiko Yamamoto

dblp:29/4451 · DBLP profile ↗
← Back
11ranked-venue papers
5as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 11 · 5 first-authorDatabases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 33% Data mining · 33% Knowledge graphs · 33%
Artificial intelligence
1 paper
Information extraction and text analysis · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis
relation extraction
0.112007
Extracting Word Sets with Non-Taxonomical Relation · ACL 2007
Knowledge graphs › taxonomy construction
concept hierarchy extraction
0.112005
Extraction of Hierarchies Based on Inclusion of Co-occurring Words with Frequency Information · IJCAI 2005
Data mining › pattern mining › association analysis
co-occurrence analysis
0.112005
Extraction of Hierarchies Based on Inclusion of Co-occurring Words with Frequency Information · IJCAI 2005
Information retrieval
text analysis
0.112005
Extraction of Hierarchies Based on Inclusion of Co-occurring Words with Frequency Information · IJCAI 2005

Methods — techniques the papers use, named apart from their topics

frequency information · 0.1
YearPublicationVenuePosition
2020 Improving Semantic Similarity Calculation of Japanese Text for MT Evaluation
Yuki Tanahashi, Kyoko Kanzaki, Eiko Yamamoto, Hitoshi Isahara
PACLIC3
2014 Fusion of Multiple Semantic Networks and Human Association
abstract
We are trying to construct a conceptual system that accurately represents human thoughts by fusing of semantic networks.As semantic networks to fuse, we use the Japanese Wordnet which is a thesaurus made manually based on linguistic intuition and the knowledge acquired automatically from the actual text stored in the huge corpus.Such knowledge are represented as mutual relations of the concepts of words.In order to acquire such relations, we focus on the case relations in sentences and calculate inclusive relations of co-occurrence by using Complementary Similarity Measure.As an application and verification of the conceptual system created, we try to simulate human associations by using the conceptual system.As an experimental result, we found the obvious difference in generated association links between using the semantic network of Japanese Wordnet and using the fused semantic networks with Japanese Wordnet and the acquired mutual relations.
Hitoshi Isahara, Kyoko Kanzaki, Eiko Yamamoto, Takayuki Kuribayashi, Michinaga Otsuka
GWC3
2008 Application of Resource-based Machine Translation to Real Business Scenes
Hitoshi Isahara, Masao Utiyama, Eiko Yamamoto, Akira Terada, Yasunori Abe
LREC3
2008 Extraction of Informative Expressions from Domain-specific Documents
Eiko Yamamoto, Hitoshi Isahara, Akira Terada, Yasunori Abe
LREC1
2007 Extracting Word Sets with Non-Taxonomical Relation
Eiko Yamamoto, Hitoshi Isahara
ACL1
2006 Semantic Analysis of Abstract Nouns to Compile a Thesaurus of Adjectives
Kyoko Kanzaki, Eiko Yamamoto, Hitoshi Isahara
LREC3
2006 Detection of inconsistencies in concept classifications in a large dictionary - Toward an improvement of the EDR electronic dictionary
Eiko Yamamoto, Kyoko Kanzaki, Hitoshi Isahara
LREC1
2005 Extraction of Hierarchies Based on Inclusion of Co-occurring Words with Frequency Information
Eiko Yamamoto, Kyoko Kanzaki, Hitoshi Isahara
IJCAI1
2004 Construction of an Objective Hierarchy of Abstract Concepts via Directional Similarity
Kyoko Kanzaki, Eiko Yamamoto, Hitoshi Isahara
COLING2
2004 Extraction of Hyperonymy of Adjectives from Large Corpora by Using the Neural Network Model
Kyoko Kanzaki, Eiko Yamamoto, Masaki Murata, Hitoshi Isahara
LREC3
2004 Related Word-pairs Extraction Without Dictionaries
Eiko Yamamoto, Kyoji Umemura
LREC1