Demo Data from VLDB 2026 / reviewers in the wild / expert
Theerawat Songyot
dblp:153/9507
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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.
| Artificial intelligence
1 paper |
Machine translation · 77% Information extraction and text analysis · 23% |
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 › Machine translation › statistical machine translation
word alignment |
0.2 | 1 | 2014 | Improving Word Alignment using Word Similarity · EMNLP 2014 |
Natural language and speech › Information extraction and text analysis › text similarity › semantic similarity
word similarity |
0.1 | 1 | 2014 | Improving Word Alignment using Word Similarity · EMNLP 2014 |
Methods — techniques the papers use, named apart from their topics
word similarity · 0.2word alignment model · 0.2neural network · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Improving Word Alignment using Word SimilarityabstractWe show that semantic relationships can be used to improve word alignment, in addition to the lexical and syntactic features that are typically used.In this paper, we present a method based on a neural network to automatically derive word similarity from monolingual data.We present an extension to word alignment models that exploits word similarity.Our experiments, in both large-scale and resourcelimited settings, show improvements in word alignment tasks as well as translation tasks. Theerawat Songyot, David Chiang 0001 |
EMNLP | 1 |