The primary goal of the Midas project is to build a system that enables easy and scalable integration of unstructured and semi-structured information present across multiple data sources. As a first step in this direction, we have built a system that extracts and integrates information from regulatory filings submitted to the U.S. Securities and Exchange Commission (SEC) and the Federal Deposit Insurance Corporation (FDIC). Midas creates a repository of entities, events, and relationships by extracting, conceptualizing, integrating, and aggregating data from unstructured and semi-structured documents. This repository enables applications to use the extracted and integrated data in a variety of ways including mashups with other public data and complex risk analysis.

Speed to market is critical to companies that are driven by sales in a competitive market. The earlier a potential customer can be approached in the decision making process of a purchase, the higher are the chances of converting that prospect into a customer. Traditional methods to identify sales leads such as company surveys and direct marketing are manual, expensive and not scalable. Over the past decade the World Wide Web has grown into an information-mesh, with most important facts being reported through Web sites. Several news papers, press releases, trade journals, business magazines and other related sources are on-line. These sources could be used to identify prospective buyers automatically. In this paper, we present a system called ETAP (Electronic Trigger Alert Program) that extracts trigger events from Web data that help in identifying prospective buyers. Trigger events are events of corporate relevance and indicative of the propensity of companies to purchase new products associated with these events. Examples of trigger events are change in management, revenue growth and mergers & acquisitions. The unstructured nature of information makes the extraction task of trigger events difficult. We pose the problem of trigger events extraction as a classification problem and develop methods for learning trigger event classifiers using existing classification methods. We present methods to automatically generate the training data required to learn the classifiers. We also propose a method of feature abstraction that uses named entity recognition to solve the problem of data sparsity. We score and rank the trigger events extracted from ETAP for easy browsing. Our experiments show the effectiveness of the method and thus establish the feasibility of automatic sales lead generation using the Web data.

We develop a novel formalism for modeling speech signals which are irregularly or incompletely sampled. This situation can arise in real world applications where the speech signal is being transmitted over an error prone channel where parts of the signal can be dropped. Typical speech systems based on hidden Markov models, cannot handle such data since HMMs rely on the assumption that observations are complete and made at regular intervals. We introduce the asynchronous HMM, a variant of the inhomogeneous HMM commonly used in bioinformatics, and show how it can be used to model irregularly or incompletely sampled data. A nested EM algorithm is presented in brief which can be used to learn the parameters of this asynchronous HMM. Evaluation on real world speech data, which has been modified to simulate channel errors, shows that this model and its variants significantly outperform the standard HMM and methods based on data interpolation.

This paper examines how any parameter in the typical front end of a speech recognizer, can be rapidly and inexpensively adapted with usage. It focusses on firstly demonstrating that effective adaptation can be accomplished using low CPU/Memory cost stochastic gradient descent methods, secondly showing that adaptation can be done at time scales small enough to make it effective with just a single utterance, and lastly showing that using a prior on the parameter significantly improves adaptation performance on small amounts of data. It extends previous work on stochastic gradient descent implementation of fMLLR [1] and work on adapting any parameter in the front-end chain using general 2nd order opimization techniques [2]. The framework for general stochastic gradient descent of any front-end parameter with a prior is presented, along with practical techniques to improve convergence. In addition the methods for obtaining the alignment at small time intervals before the end of the utterance are presented. Finally it shown that experimentally online causal adaptation can result in a 5-15% WER reduction across a variety of problems sets and noise conditions, even with just 1 or 2 utterances of adaptation data.