IBM Research | Almaden Research Center | Computer Science

IBM Research Almaden Research Center Computer Science: "An increasingly important class of keyword search tasks are those where users are looking for a specific piece of information buried within a few documents in a large collection. Examples include searching for (a) someone's phone number or a package tracking URL, within a personal email collection, (b) reviews from blogs and (c) internal homepage for a person or a group within the company intranet. While modern information extraction techniques can be used to extract the concepts involved in these tasks (persons, phone numbers, restaurant reviews, etc.), since users only provide keywords as input, the problem of identifying the documents that contain the information of interest remains a challenge.
In Avatar Semantic Search, we are building a solution to this problem based on the concept of automatically generating ``interpretations'' of keyword queries. Interpretations are precise structured queries, over the extracted concepts, that model the real intent behind a keyword query. We have formalized the notion of interpretations and are addressing the various challenges in identifying the most likely interpretations for a given keyword query. The resulting interpretations are presented in an intuitive interface resulting in a dialogue between the user and the system to determine the true user intent (as shown in the screenshots below).
[Screenshot 1] [Screenshot 2] "

Technology Review: The Future of Search

Technology Review: The Future of Search: "The two biggest projects are machine translation and the speech project. Translation and speech went all the way from one or two people working on them to, now, live systems."

Human Language Technology Center at Johns Hopkins

Johns Hopkins Gazette | June 25, 2007: "The Johns Hopkins University has been awarded a long-term multimillion-dollar contract to establish and operate a Human Language Technology Center of Excellence near the Homewood campus. The center's research will focus on advanced technology for automatically analyzing a wide range of speech, text and document image data in multiple languages."

FactSpotter from XRCE Grenoble

Searching for documents that contain specific information can be time consuming and frustrating in today’s office environment. Xerox scientist Frédérique Segond helped develop FactSpotter, a new technology that takes ordinary search to the next level by digging into more documents, analyzing meaning of words and context, accepting queries in everyday language. Segond manages parsing and semantics research at Xerox Research Centre Europe, in Grenoble.

Business Objects to Acquire Text Analytics Leader Inxight Software

Combination of Inxight and Business Objects to Deliver First Full Spectrum Business Intelligence Platform: "With the acquisition of Inxight Software, Inc., Business Objects expands its leadership in extending BI to embrace enterprise search. Going beyond basic keyword searches and solutions that simply provide a ranked listing of searched items, Inxight’s web services-based federated search and extraction capabilities extend the value of enterprise search engines by instantly clustering and filtering results from multiple search engines, including Google Search Appliance and Oracle Secure Enterprise Search. By providing a BI platform that leverages these capabilities, Business Objects will become the first vendor to bridge the gap between search and intelligence – delivering a broader view of data and dramatically accelerating the ability to locate hidden information in search results that might otherwise be overlooked. "

How Google translates without understanding

How Google translates without understanding The Register: "The Google approach is a lesson in practical software development: try things and see what sticks. It has just a few major steps:
1. Google starts with lots and lots of paired-example texts, like formal documents from the United Nations, in which identical content is expertly translated into many different languages. With these documents they can discover that 'white house' tends to co-occur with 'casa blanca,' so that the next time they have to translate a text containing 'white house' they will tend to use 'casa blanca' in the output.
2. They have even more untranslated text in each language, which lets them make models of 'well-formed' sentence fragments (for example, preferring 'white house' to 'house white'). So the raw output from the first translation step can be further massaged into (statistically) nicer-sounding text.
3. Their key for improving the system - and winning competitions - is an automated performance metric, which assigns a translation quality number to each translation attempt. More on this fatally weak link below."

PROMT 8.0: revamped translation software

PROMT revamped translation software product line: OSP International: "Evaluation of machine translation quality is usually quite individual but PROMT claims that PROMT 8.0 analyzes the context and generates grammatically correct translation of most of linguistic structures and set expressions. The user can teach the translator, enriching its vocabulary by adding personal dictionaries and using earlier translated text pieces in further translations. The quality of translation, especially of specialized texts, also largely depends on setting up software according to the document subject. The system set-up procedure, which many users used to ignore because of its length and complexity, has been much simplified in version 8.0. "