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Resolving Ambiguity: NTENT Uses Semantic Ranking to Infer User Intent


NTENT has announced a major advancement in the ability of its technology to decipher human intention nestled within search queries. Through a process known as Ambiguity Resolution, NTENT incorporates Natural Language Processing (NLP) and advanced Machine Learning (ML) to discern ambiguity by parsing words from concepts within different languages, and inferring the relationships between them. This enhancement further allows the company to understand short and ambiguous queries while bypassing the cultural or grammatical challenges each language inalienably presents, and ensures that end users get the most relevant answers.

The goal of Ambiguity Resolution is to enable a machine to infer the correct user intent when a query has several meanings. NTENT’s approach detects which language a person is using and implements statistical NLP trained for that language to break down the query into individual tokens. The tokens are categorized as parts of speech and assigned semantic labels. NTENT then applies a specialized Machine Learning technique to formulate a semantic ranking for all possible interpretations behind the query. The technique itself combines language independent features coming from the query, the user context and a vortex of data sources, with language dependent features coming from the query semantics and language dependent mappings, stemming from a large-scale language-agnostic knowledge base. Through semantic ranking, NTENT’s system calculates the probability of each interpretation, simultaneously weighing all potential intentions and hypothesizing about the “why” buried behind a user’s choice of words, the transactions he or she plans to engage in, or the websites a person chooses to visit. The final answer presents the feasible interpretations in the right order.

“Understanding the intent behind a query is the most essential part of being able to deliver information the user wants and frankly, for marketers to deliver valuable messages at the right time. Ambiguity Resolution makes inductive reasoning more effective,” said NTENT Chief Executive Officer, Dan Stickel.

“Combining the right semantics and the overall user context is paramount to harness NTENT’s extensive set of answer experts and for returning results that are precisely what users want,” added NTENT’s Chief Technology Officer, Ricardo Baeza-Yates.

NTENT continues to make strides in the areas of semantic search, NLP and ML to streamline the flow of global information retrieval. Earlier this year they announced the role of their powerful Knowledge Base to help achieve that endeavor.

About NTENT: NTENT™ sits at the crossroads of semantic search and natural language processing technologies. Our patented, proprietary technology powers our comprehensive platform that transforms structured and unstructured data into relevant and actionable insights. This level of intelligence enables us to predict and deliver relevant information based on user intention. Learn more about NTENT at http://www.ntent.com.

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