Technology

Natural Language Processing Text Analytic Engine

Exeact Discovery and Analytic Services employs patented proprietary NLP text analytics to develop insights from select electronic documents. This technology is the most advanced, reliable and human like analytic tool of its types for analyzing large sets of documents and then developing repeatable, actionable and defensible insights around the information.

Secure Collaboration Platform

To work quickly and effectively on cases that may have input from many people in remote offices, Exeact has developed a collaboration front-end that allows many different stake holders to review, comment and respond to knowledge extracted from the analytic work process.  These tools allow teams to comment and carry on a discussion while reviewing the insights as well as develop and plan follow on projects associated with the data as well as work with analysts to further the analysis around document sets.

Employing Game Changing NLP Text Analytic Technology

How does this technology compare to what a human can do?

When reviewing a relatively small volume of uncomplicated data a human could analyze the same data that our system does and likely discover the same or similar results; however, it would just takes them much longer. On the other hand, the greater the volume of data and complexity involved, the more difficult it becomes for humans to consistently and accurately review and analyze the data. Complex data with many variables renders the average lawyer's analytic capabilities inefficient and ineffective.

The use of ontologies, taxonomies, and dictionaries

Relationships such as synonyms, hyponyms, (words that are more specific than a more general word i.e. table or chair are hyponyms of furniture) and hypernyms (words that are more general than a given word i.e. musical instrument is hypernym to guitar) need to be leveraged to perform accurate analysis. Other aspects of relevance are anaphora resolution and stemming. Again, you cannot perform accurate analysis without being able to identify the subjects, predicates, and objects. If codes were to be used to replace the text analysis, the codes will have to be assigned by a human after carefully reading the text.

Analyzing natural language along with structured codes

Finding relationships among unstructured concepts is not possible without analyzing the context, which is the text surrounding the concept. If text was abstracted to codes, which many text analytic tools do, the context will be lost. Hence, discovery using NLP is exhaustive, i.e. nothing is missed.  Importantly, one of the characteristics of OLAP is that its dynamic nature allows the user to try combinations and permutations among many of the concepts. That feature provides the user of NLP the opportunity to look at multiple possibilities to see which works best for them without repeatedly having to rebuild the extract. The result is faster, deeper analysis and greater insight into the data.

Using functionally complex ideas for analysis

Functionally complex ideas may be assembled, revised and combined in English to include multidimensional  “if then” propositions as well as the elimination of unneeded “noise” terms to focus on important relationships intended by the analyst.  Concepts “flagged” by the Exeact analysis are more apt to be on- target and in synch with analyst intent with less noise or “false positives” included in the analytical “catch.” This is in contrast to other technologies that force unstructured data into a structured format or use abstract statistical analysis to produce a one- dimensional template that may identify desired data, but mask the target with noise and unwanted clutter.  Moreover, with other technologies the analytical task is more difficult due to loss of touch with the original data.  This is not the case with our e-Case Dictionary.

The reference to the original document is always maintained

This means at any time users can immediately drill-through to the actual document to find the actual text and see it in context. This is very important to users at almost every level in an organization and especially to lawyers reviewing documents and developing lists of search terms and trying to assess where or not there is a smoking gun.