Demo Data from EDBT 2026 / reviewers in the wild / expert
Kenji Tateishi
dblp:87/5487
· DBLP profile ↗
3ranked-venue papers
1as first author
0since 2021 · last 2008
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 1 first-authorDatabases, data management, data science and information retrieval · 1
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.
| Artificial intelligence
1 paper |
Information extraction and text analysis · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Web and social media mining · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis
sentiment analysis |
0.0 | 1 | 2002 | Mining product reputations on the Web · KDD 2002 |
Web and social media mining › web mining
web content analysis |
0.0 | 1 | 2002 | Mining product reputations on the Web · KDD 2002 |
Methods — techniques the papers use, named apart from their topics
text mining · 0.1syntactic and linguistic rules · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Fast Duplicated Documents Detection using Multi-level Prefix-filter
Kenji Tateishi, Dai Kusui |
IJCNLP | 1 |
| 2004 | Collecting Evaluative Expressions for Opinion Extraction
Nozomi Kobayashi, Kentaro Inui, Yuji Matsumoto 0001, Kenji Tateishi, Toshikazu Fukushima |
IJCNLP | 4 |
| 2002 | Mining product reputations on the WebabstractKnowing the reputations of your own and/or competitors' products is important for marketing and customer relationship management. It is, however, very costly to collect and analyze survey data manually. This paper presents a new framework for mining product reputations on the Internet. It automatically collects people's opinions about target products from Web pages, and it uses text mining techniques to obtain the reputations of those products. On the basis of human-test samples, we generate in advance syntactic and linguistic rules to determine whether any given statement is an opinion or not, as well as whether such any opinion is positive or negative in nature. We first collect statements regarding target products using a general search engine, and then, using the rules, extract opinions Satoshi Morinaga, Kenji Yamanishi, Kenji Tateishi, Toshikazu Fukushima |
KDD | 3 |