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Xiaohuo Liu

dblp:68/3179 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 1999
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 1 first-authorGraphics, 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.

Artificial intelligence
1 paper
Machine translation · 50% Knowledge representation and reasoning · 50%
Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

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

TopicWeightPapersLastEvidence papers
Natural language and speech › Machine translation
cross-lingual retrieval
0.011999
Mixed Language Query Disambiguation · ACL 1999
Knowledge, reasoning and agents › Knowledge representation and reasoning › ontology-based query answering
query rewriting
0.011999
Mixed Language Query Disambiguation · ACL 1999
Information retrieval › query understanding
query disambiguation
0.011999
Mixed Language Query Disambiguation · ACL 1999
Information retrieval
query processing
0.011999
Mixed Language Query Disambiguation · ACL 1999

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

nearest neighbor · 0.0contextual word voting · 0.0co-occurrence statistics · 0.0
YearPublicationVenuePosition
1999 Mixed Language Query Disambiguation
abstract
We propose a mixed language query disambiguation approach by using co-occurrence information from monolingual data only. A mixed language query consists of words in a primary language and a secondary language. Our method translates the query into monolingual queries in either language. Two novel features for disambiguation, namely contextual word voting and 1-best contextual word, are introduced and compared to a baseline feature, the nearest neighbor. Average query translation accuracy for the two features are 81.37% and 83.72%, compared to the baseline accuracy of 75.50%.
Pascale Fung, Xiaohuo Liu, Chi Shun Cheung
ACL2
1999 A monolingual semantic decoder based on word sense disambiguation for mixed language understanding
Xiaohuo Liu, Pascale Fung, Chi Shun Cheung
EUROSPEECH1