Machine learning based named entity recognition for natural language processing

Abstract

A system performs named entity recognition for performing natural language processing, for example, for conversation engines. The system uses context information in named entity recognition. The system includes the context of a sentence during model training and execution. The system generates high quality contextual data for training NER models. The system utilizes labeled and unlabeled contextual data for training NER models. The system provides NER models for execution in production environments. The system uses heuristics to determine whether to use a context-based NER model or a simple NER model that does not use context information. This allows the system to use simple NER models when the likelihood of improving the accuracy of prediction based on context is low.

Description

CROSS-REFERENCES TO RELATED APPLICATIONS[0001]This application claims the benefits of U.S. Provisional Application No. 63 / 136,831, filed on Jan. 13, 2021, which is incorporated by reference in its entirety.BACKGROUNDField of Art[0002]The disclosure relates in general to machine learning based natural language processing, and more specifically to machine learning based named entity recognition for natural language processing.Description of the Related Art[0003]Named entity recognition (NER) is a commonly used operation performed by many natural language processing (NLP) tasks. Named entity recognition is used in NLP services such as conversational BOTs that allow users to interact with online systems using unstructured text, for example, natural language sentences.[0004]Named entity recognition is performed using linguistic grammar-based techniques as well as machine learning based models such as neural networks. Machine learning based techniques for NER typically require a large amo...

AI Technical Summary

Benefits of technology

The patent text describes a system and method for improving natural language processing by using machine learning based named entity recognition for natural language processing tasks. The system uses context information to generate high quality contextual data for training the NER models, which can improve accuracy in identifying entities in unstructured text. The system also uses heuristics to determine whether to use labeled or unlabeled contextual data for training the NER models. The technical effects of the patent text include improved accuracy in natural language processing and better training of NER models for conversational engines.

Problems solved by technology

Machine learning based techniques for NER typically require a large amount of manually annotated training data.

Furthermore, trained machine learning based models for performing named entity recognition have lower accuracy in production since they are trained using training data that is different from real conversations.

Conventional techniques typically fail to perform accurate named entity recognition in such situations.

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