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NEC Technology Automatically Groups Vast Amounts of Text Data According to Meaning - Empowers marketing and risk control support through rapid recognition of content -

*** For immediate use November 18, 2014

Tokyo, November 18, 2014 - NEC Corporation (NEC; TSE: 6701) has developed an "RTE-based Clustering Technology" as a big data analysis technology for automatically grouping vast amounts of text data according to meaning then indicating the general semantic content of each group of sentences in the form of a headline.

The new technology applies NEC's Recognizing Textual Entailment (RTE) technology for judging whether or not two pieces of text have the same meaning. This enables the technology to group text data quickly and automatically without setting the classification standpoint in advance.

"This technology clarifies the standpoint of grouping, which was formerly unclear. This enables, for example, the early discovery of products to be recalled through an analysis of complaints made to a call center, trend analysis through social media analysis, and prompt responses by local governments or police through the automatic organization of disaster information and information provided in calls for service."

Technology Features

NEC's RTE technology not only judges whether or not two sentences have the same meaning, it is also capable of rapidly searching for text containing a specific meaning in vast amounts of data. Based on this technology, NEC has developed a technology that automatically groups texts and generates headline sentences.

It recognizes meanings of sentences in texts that are subject to analysis and groups the sentences according to the meanings they contain. It also consolidates groups containing the same meanings, enabling automatic grouping without the overlapping of meanings or redundancy. What is more, it generates a headline sentence that simply expresses the meaning contained commonly in each group of sentences. This makes it possible to rapidly recognize the general semantic content of each group, which was formerly difficult and time consuming.

In addition, this technology's high-speed data processing also enables texts to be grouped quickly enough to permit the practical application of the result. For example, it takes less than an hour to process and analyze the user opinions provided to a large-scale contact center in a single day, which previously required several days. This makes it possible to implement measures promptly based on the results of the analysis.


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