The current interest in data acquisition and analysis has resulted in a large number of solutions available to the public.However, anyone other than professionals in the field can find it difficult to make sense of this sea of data.This demo showcases a tool that produces general static reports (as opposed to query or intention based systems of past NLG interest) of combined text and graphics given any spreadsheet sent by email.

This paper demonstrates a conceptually simple but effective method of increasing the accuracy of QA systems on factoid-style questions. We define the notion of an inverted question, and show that by requiring that the answers to the original and inverted questions be mutually consistent, incorrect answers get demoted in confidence and correct ones promoted. Additionally, we show that lack of validation can be used to assert no-answer (nil) conditions. We demonstrate increases of performance on TREC and other question-sets, and discuss the kinds of future activities that can be particularly beneficial to approaches such as ours.

In some IR applications, it is desirable to adopt a high precision search strategy to return a small set of documents that are highly focused and relevant to the user's information need. With these applications in mind, we investigate semantic search using the XML Fragments query language on text corpora automatically pre-processed to encode semantic information useful for retrieval. We identify three XML Fragment operations that can be applied to a query to conceptualize, restrict, or relate terms in the query. We demonstrate how these operations can be used to address four different query-time semantic needs: to specify target information type, to disambiguate keywords, to specify search term context, or to relate select terms in the query. We demonstrate the effectiveness of our semantic search technology through a series of experiments using the two applications in which we embed this technology and show that it yields significant improvement in precision in the search results.

In a language generation system, a content planner embodies one or more "plans" that are usually hand-crafted, sometimes through manual analysis of target text.In this paper, we present a system that we developed to automatically learn elements of a plan and the ordering constraints among them.As training data, we use semantically annotated transcripts of domain experts performing the task our system is designed to mimic.Given the large degree of variation in the spoken language of the transcripts, we developed a novel algorithm to find parallels between transcripts based on techniques used in computational genomics.Our proposed methodology was evaluated two-fold: the learning and generalization capabilities were quantitatively evaluated using cross validation obtaining a level of accuracy of 89%.A qualitative evaluation is also provided.