A Dirichlet-Smoothed Bigram Model for Retrieving Spontaneous Speech

We present two simple but effective smoothing techniqes for the standard language model (LM) approach to information retrieval [12]. First, we extend the unigram Dirichlet smoothing technique popular in IR [17] to bigram modeling [16]. Second, we propose a method of collection expansion for more robust estimation of the LM prior, particularly intended for sparse collections. Retrieval experiments on the MALACH archive [9] of automatically transcribed and manually summarized spontaneous speech interviews demonstrates strong overall system performance and the relative contribution of our extensions1 .

• Type: Conference
• Year: 2007
• Where: CLEF
• Authors: Matthew Lease, Eugene Charniak
• Keywords: CLEF 2007, Dirichlet Smoothing Technique, Effective Smoothing Techniqes, Information Retrieval, Standard Language Model