Syntactic Parsing

Provide dependency relationship and syntactic structure information between words in texts (such as subject-verb-object and attribute-adverbial-complement) and help the machine precisely understand the user’s intentions

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Benefits

High Accuracy
The best dependence relationship is worked out with the global non-greedy algorithm, achieving the leading level of the industry
Rapid Speed
Based on transferred neutral network dependency grammar model, it is more faster than image-based model
Rich Linguistic Information
Rich Data Annotated with Chinese Dependency Grammar

Features

Dependence Analysis

Analyze linguistic unit relevance

Analyze semantics relevance between linguistic units of sentences and show the linguistic relevance in the form of dependency structures

Syntactic Structure

Parse syntactic structure information

With syntactic structure information (such as subject-verb-object and attribute-adverbial-complement), it helps the machine precisely understand the user’s intentions, realizing semantics comprehension, semantic match, real database building and other applications

Scenarios

Semantics Comprehension

For Semantics Comprehension The structure information of dependency grammar with correct comprehension can increase accuracy of semantic comprehension tasks such as intention type and the like

Semantics Match

Semantics Match can increase semantic match computing accuracy

Real Database Setup

Explore more entities, relations and semantics relations