Team members
Nitin Agarwal
Yandong Liu
Yanbo Xu
Ming Sun
LDA results
- Used ACL 2008 corpus for experimentation
- For exploratory analysis of corpus we ran the LDA model
- Parameters of the LDA model
- Number of topics : 100
- Gibbs iteration : 2000
- Beta prior : 0.5
- Alpha prior : 1.0
Some of the topics obtained post training
- Error Detection (Topic 6)
- errors, error, correct, rate, correction, spelling, detection, based, detect, types, detecting
- Evaluation (Topic 10)
- evaluation, human, performance, automatic, quality, evaluate, study, results, task, metrics
- Entity Coreference (Topic 13)
- names, entity, named, entities, person, coreference, task, ne, recognition, proper, location
- Parsing (Topic 18)
- parsing, parser, parse, grammar, parsers, parses, input, chart, partial, syntactic, parsed, algorithm
ATM results
Gibbs Sampling for Collaboration Influence Model
We want , the posterior distribution of topic Z, (author, collaborator) pair X and which favor of collaboration over influence R given the words W in the corpus:
We begin by calculating and :
,
where P is the number of all the different author-collaborator-favor of collaboration combination (a,a',r).
So the Gibbs sampling of :
Further manipulation can turn the above equation into update equations for the topic and author-collaboration of each corpus token:
Applications