Demo Data from EDBT 2026 / reviewers in the wild / expert
Yihui Peng
dblp:264/4987
· DBLP profile ↗
1ranked-venue papers
0as 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 · 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 · 87% Language models and text generation · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis
morphological analysis |
0.4 | 1 | 2020 | Unsupervised Morphological Paradigm Completion · ACL 2020 |
Natural language and speech › Information extraction and text analysis › morphological analysis
paradigm completion |
0.4 | 1 | 2020 | Unsupervised Morphological Paradigm Completion · ACL 2020 |
Natural language and speech › Language models and text generation
low-resource language processing |
0.1 | 1 | 2020 | Unsupervised Morphological Paradigm Completion · ACL 2020 |
Methods — techniques the papers use, named apart from their topics
inflection generation · 0.4edit tree retrieval · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Unsupervised Morphological Paradigm CompletionabstractWe propose the task of unsupervised morphological paradigm completion.Given only raw text and a lemma list, the task consists of generating the morphological paradigms, i.e., all inflected forms, of the lemmas.From a natural language processing (NLP) perspective, this is a challenging unsupervised task, and high-performing systems have the potential to improve tools for low-resource languages or to assist linguistic annotators.From a cognitive science perspective, this can shed light on how children acquire morphological knowledge.We further introduce a system for the task, which generates morphological paradigms via the following steps: (i) EDIT TREE retrieval, (ii) additional lemma retrieval, (iii) paradigm size discovery, and (iv) inflection generation.We perform an evaluation on 14 typologically diverse languages.Our system outperforms trivial baselines with ease and, for some languages, even obtains a higher accuracy than minimally supervised systems. 1 Sé vigilante y confirma las otras cosas que están para morir , porque no he hallado tus obras bien acabadas delante de Dios .Acuérdate , pues , de lo que has recibido y oído ; guárdalo y arrepiéntete , pues si no velas vendré sobre ti como ladrón y no sabrás a qué hora vendré sobre ti .El vencedor será vestido de vestiduras blancas , y no borraré su nombre del libro de la vida , y confesaré su nombre delante de mi Padre y delante de sus ángeles .El que tiene oído , oiga lo que el Espíritu dice a las iglesias . Huiming Jin, Liwei Cai, Yihui Peng, Chen Xia, Arya McCarthy, Katharina Kann |
ACL | 3 |