TalentBrowser’s customizable domain-specific search engine is powered by DataScava’s unstructured text data mining technology.

Our system helps you quickly and efficiently home in on “the right fit” and really excels at jargon-intensive specialty jobs in technology, finance, healthcare and more.

Domain-Specific Language Processing (DSLP) and Weighted Topic Scoring (WTS)

We don’t use Artificial Intelligence, Machine Learning, Natural Language Processing (NLP) or off-the-shelf semantic toolkits.

TalentBrowser uses Machine Training,  Human Intelligence and DataScava’s proprietary Domain-Specific Language Processing (DSLP) and patented Weighted Topic Scoring (WTS) methodologies to mine the unstructured text on resumes and profiles, which allow users to set minimum “required” and “nice-to-have” score thresholds to be met in each defined topic.

With DSLP and WTS, people who may match a one-dimensional Boolean search but lack the depth of experience in key skills required to do the job are filtered out. In addition, the high percentage of false matches associated with generic NLP, a frequent complaint, disappears.

Your industry-specific topics libraries and search templates are fully editable (you can also create brand new ones easily on the fly!) and used to match and filter on-target resumes and profiles, with found topic key terms color-coded and highlighted within the text itself, with topic scores you can see, multi-sort and rank.


Encapsulate Business Intelligence

Our users are excited at how their specific requirements and nuances of ongoing needs are encapsulated in their own customizable search engine that’s available at the earliest stage of the decision-making process and on an ongoing basis.

With TalentBrowser, you can gain insight into both individual and corpus-wide skills by topic and by source to gain insight into data you already have and filter incoming resumes and profiles automatically.

Navigational vs. Research Search

Ramanathan V. Guha is responsible for products such as Google Custom Search.  In their paper on Semantic Search,  he and his colleagues distinguished between two major forms of search,  navigational and research:

“Before getting into the details of how the Semantic Web can contribute to search, we need to distinguish between two very different kinds of searches.

In navigational search, the user is using the search engine as a navigation tool to navigate to a particular intended document. In this class of searches, the user provides the search engine a phrase or combination of words which s/he expects to find in the documents. There is no straightforward, reasonable interpretation of these words as denoting a concept. In such cases, the user is using the search engine as a navigation tool to navigate to a particular intended document. We are not interested in this class of searches.

In research search, the user provides the search engine with a phrase which is intended to denote an object about which the user is trying to gather/research information. There is no particular document which the user knows about and is trying to get to. Rather, the user is trying to locate a number of documents which together will provide the desired information. Semantic search lends itself well with this approach that is closely related with exploratory search.”

This article on Github  What is Semantic Search? references Guha’s work and discusses why Semantic Search is not suitable to navigational search.

When a person “navigates” to a resume or profile for recruitment and other people initiatives, they are trying to find the best overall “match.” They are not interested in “researching” all resumes that have a mention of any one of their topics of interest.

TalentBrowser excels at Navigational Search.