Named entity identification method based on time convolution network

Abstract

The invention relates to a named entity identification method based on a time convolution network. The method comprises the following steps of: firstly, constructing a feature representation layer which mainly consists of a word vector and a character feature layer, wherein the word vector layer and the character vector layer respectively accept words and characters as input, and respectively mapdiscrete One-hot representations to respective continuous dense low-dimensional feature spaces; splicing the word vectors and the character-level vectors to represent features of the words in a particular semantic space; secondly, taking the spliced features as input of a time convolution network, extracting different features through the time convolution network with different fusion convolutionkernel sizes, and obtaining final features h1h2... hn; finally, taking the obtained features as input of a CRF layer; and after the CRF further restrains context annotation, outputting sequence annotation results y1y2... yn. Compared with an existing LSTM network, the TCN network has the advantages that the recognition precision is slightly improved, and the training time is only about 1/3 of thatof the LSTM network.

Description

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AI Technical Summary

Benefits of technology

This patented technology allows for better extraction of timings during sequences through an algorithm called Time Convolution (TC). By doing this with just one dimensionally inherited convolution, it overcomes issues such as slow convergence or lack of memory when dealing with real world data. Additionally, its Backpropagated Pathway differs from regular convolution processing due to differences between times at both ends. It also simplifies the process of learning networks quickly without losing their ability to recall past experiences. Overall, these technical improvements improve performance and efficiency across various types of neural systems used today.

Problems solved by technology

The technical problem addressed by this patented technology is that current networks have limited ability for recognizing new or changed things quickly due to their limitations such as being too short memory capacity or requiring large amounts of storage space.

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