Invited Talk at the Forum for Information Retrieval Evaluation (FIRE 2012), Indian Statistical Institute, Kolkata, India on 19-Dec-2012. iWork : Analytics for Human Resources Management. Girish Keshav Palshikar Tata Consultancy Services Limited
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Invited Talk at the Forum for Information Retrieval Evaluation (FIRE 2012), Indian Statistical Institute, Kolkata, India on 19-Dec-2012
Tata Consultancy Services Limited
54B Hadapsar Industrial Estate, Pune 411013, India.
HR Domain Knowledge
Complaints Management System
PULSE PEEP mPOWER
ITIS workforce management: team sizing + shift planning; optimal team skill profile; service level rationalization; expert finding; training plans; DC transformation planning
Automation of survey responses tagging Nielsen
Document retrievals; ranking
Cross-linking and information fusion (e.g., with FB, LinkedIn)
Learning to rank
Various databases and text repositories
On-the-fly extraction using an IR engine
R. Srivastava, G. K. Palshikar, RINX: Information Extraction, Search and Insights from Resumes, Proc. TCS Technical Architects' Conf., (TACTiCS 2011), Thiruvanthapuram, India, Apr. 2011.
G.K. Palshikar, S. Deshpande, S. Bhat, QUEST: Discovering Insights from Survey Responses, Proc. 8th Australasian Data Mining Conf. (AusDM09), Dec. 1-4, 2009, Melbourne, Australia, P.J. Kennedy, K.-L. Ong, P. Christen (Ed.s), CRPIT, vol. 101, published by Australian Computer Society, pp. 83 - 92, 2009.
Things you don’t like about TCS
PULSE 2008-09 Responses for TCS Mumbai
Interesting subset discovery: finding bumps in a large-dimensional distribution
S. Deshpande, G.K. Palshilkar, G Athiappan, An Unsupervised Approach to Sentence Classification, Proc. Int. Conf. on Management of Data (COMAD 2010), Nagpur, 2010, Allied PublishersPvt. Ltd., pp. 88 - 99.
Average semantic depthS.ASD for a sentence S = <w1 w2 . . . wn> containing n content-carrying words = the average of the semantic depths of the individual words
Semantic height (SH) SHT(w) of a word w is the length of the longest path in T from word w to a leaf node
Average semantic heightS.ASH for a sentence S = <w1 w2 . . . wn> containing n content-carrying words (non stop-words) = the average of the semantic heights of the individual words
Intuition: more specific sentences tend to include words which occur rarely in some reference corpus
Named entities (NE) are commonly occurring groups of words which indicate specific semantic content
Proper Nouns (PN) are commonly occurring groups of words which indicate specific semantic content
Sentence length, denoted S.Len, is a weak indicator of its specificity in the sense that more specific sentences tend to be somewhat longer than more general sentences.
Scaling + reversal of polarity
algorithm from 110,000 responses in
an employee satisfaction survey
Some specific sentences identified by our algorithm from 220 sentences from 32 reviews of a hiking backpack product by Kelty.