- Discrete PCA - Wray Buntine
- Perspectives Seminar

- Hierarchical Dirichlet Processes (E Y Teh)
- GAP (Gamma Poisson) - Hierarchical angle?
- (Bach) K(ernelized) G(en?) V(ar?)
- CCA - Least squares -> relation
- Max probability regions?

- HW - Discriminative LDA
- Clusty
- GAP (Gamma-Poisson) (John...) (Alternative to Dir-Mult of LDA)
- HW - Appln of CCA in 3+ languages

- STORY at UMIACS, Prof. V.S. Subrahamanian

- Wray Buntine, Discrete PCA
- Covariance matrix, has eigenvalues, take the max eigenvalue, its eigenvector, that is the direction of max variance
- LDA for multilingual case

FIRE 2008 at Kolkata

FIRE (Forum For Information Retrieval Evaluation) Workshop 2008 at Kolkata, Dec 13-15

Talks
Donna Harman - failure analysis
Xerox - Nicola Cancedda - CCA for EU langs
Kalervo Jaervelin - Finland - morph analysis
Noriko Kando, Tatsuya Sakai - NTCIR, Galaxy of Words - interaction in IR
Doug Ouard - Univ. of Maryland
Carol Peters - CLEF
Mark Sanderson - Diversity in results (evaluation using clustering)
CLIA (Cross-Lingual Information Access) - an-India project (IITB/K, IIITH,ISI,JU,AU) - building IR for Indian lang + building resources for that (dic,corp,Ont,Rules etc.)

Runs
JHU - n-grams,skipgrams, lang-indep
Univ. of Neuchatel (Jacques Savoy) (recommended reading by Donna Harman) - various techniques - Okapi,BM25, DFR (prob), LM (stat), tf.idf, data fusion

Others
Measures - MAP is the most preferred, Prec. vs. Recall also used, Performance never discussed
People - Manoj Chinnakotla, Vishal Vachnani, Ashish Almeida, Pavitro Mitra (IITKgp)

Upcoming confs
NE workshop at ACL-IJCNLP - NE task - Dates - Task details - Jan 31 - Paper submsn - May 01 - acl-ijcnlp-2009.org/workshops/NEWS2009
Workshop in CLIA - talks and papers - search.iiit.ac.in/CLIA2009 - Dates: Mar 06 - paper subm
Discourse Anaphora and Anaphora Resolution Colloquium (DAARC2009) - Nov 5-6 2009, Goa - www.au-kbc.org/daarc2009 - Dates: April 25 - pap subm,

IIIT Hyd's work

Indian language font/script related
- identifying language of given text
- identifying encoding of given text
- developing a metaformat for uniformity
- converting from one encoding to another
Techniques used - TF*IDF, Glyph assimilation, IT3 (phonetic transliteration scheme)


Language and encoding identification (Anil Kumar Singh)
- choices -> (1) What kind of language modeling should be used for representing training texts? (2) Which similarity measure should be used for comparing the models obtained from the texts?
- Models -> (1) n-grams model
- Similarity measures -> (1) out of rank - sum[for all n-grams in test data: diff(rank of n-gram in test data,rank in training data)](2) mutual cross entropy (3) translator approaches (4) compare profiles of n-gram frequencies (5)
- orthographical features (based on letter sequences and frequencies)
- add-k smoothing
- pruning
- Monte Carlo sampling
- cross entropy
- Prediction by partial matching (Teahan and Harper 2001)
- Cavnar -> top 300 n-grams indicate language of text, bottom n-grams indicate topic of text


POS tagging for Gujarati using CRF's
- 26 POS tags, 600 tagged sentences, 5000 untagged sentences, 10000 training corpus (??)
- use CRF methods for tagging (why?)
- errors attributed to not enough training data

Similarity measures for sentence alignment
- weighted sentence length (charc,wordc,sig) -> Poisson (how?)
- word correspondence (based on distribution of words in the language??)
- NPC matching
- common word count
- syno/hyper nym intersection (using WordNet) -> for more abstract similarity measurement
- F-measure??