Named entity linking method based on knowledge base feature extraction

A feature extraction and named entity technology, applied in relational databases, database models, special data processing applications, etc., can solve problems such as massive calculations and long running times

Active Publication Date: 2018-07-20
ZHEJIANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method takes global information into account, but requires a lot of calculations and takes a long time to run

Method used

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  • Named entity linking method based on knowledge base feature extraction
  • Named entity linking method based on knowledge base feature extraction
  • Named entity linking method based on knowledge base feature extraction

Examples

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Embodiment

[0083] Take the following piece of news as an example, “Holland rushed to the scene of the shooting. Agence France-Presse has just quoted news from the prosecution. The shooting has killed 10 people. French President Hollande is rushing to the scene and will call an emergency cabinet meeting. ." to link entities, the methods and steps are as follows:

[0084] 1. Extract entity type, entity name, entity ID, and entity redirection information according to different predicates to form relational tables ObjectType, ObjectName, WikiID and WikiRedirect and store them in the MySQL database;

[0085] 2. Extract the triples where the subject and the object are both MIDs, and count the number of times each MID appears to form a relational table NodeHot and store it in the MySQL database;

[0086] 3. Use the relational table ObjectType to establish a one-to-many mapping relationship between the entity mention type and Freebase Object type, and form a series of relational tables PERNeedType, ORG...

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Abstract

The invention discloses a named entity linking method based on knowledge base feature extraction. The method comprises the following steps of (1), extracting triple entries with specified features from Freebase data dump to form a relational data table, and saving the relational data table in a knowledge base; (2), designing a complex rule, and searching for a plurality of Freebase Objects which are closely related to entity reference from the knowledge base as candidate entities; (3), using a statistical-based method to design and extract the entity reference and features of the candidate entities, and Embedding is conducted on the features; (4), Embedding for feature extraction is used as an input of a multi-layer neural network to obtain the probability of each candidate entity as a target entity, and returning to a Freebase MID of the candidate entity with the highest probability is conducted. The method combines a complex rule-based candidate generation technology and a statistical learning-based candidate sorting technology, and a set of processing framework suitable for entity linking is established for a specific type of named entities, so that convenience is provided for auser to obtain an entity linking result by adopting a batch processing manner.

Description

Technical field [0001] The present invention relates to natural language processing, and more particularly to a named entity linking method based on feature extraction of knowledge base. Background technique [0002] Natural Language Processing (NLP) is an interdisciplinary subject integrating linguistics and computer science. Named Entity Linking (NEL) is a basic task in natural language processing, which aims to disambiguate the ambiguous mentions in the text and link them to the knowledge base. With the rise of information extraction and big data concepts, the task of named entity linking has received increasing attention and has become an important part of natural language processing such as public opinion analysis, information retrieval, automatic question and answer, and machine translation. How to automatically, accurately, and quickly link to the target entity from the massive amount of Internet text information has gradually become a hot issue in the academic and indust...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06F17/22
CPCG06F16/2468G06F16/288G06F40/194
Inventor 汤斯亮杨希远林升陈博吴飞庄越挺
Owner ZHEJIANG UNIV
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