Neural network systems and methods for target identification from text

Patent metadata

Field	Value
Publication number	US-11017177-B2
Application number	US-201916454771-A
Country	US
Kind code	B2
Filing date	Jun 27, 2019
Priority date	Jun 27, 2019
Publication date	May 25, 2021
Grant date	May 25, 2021

Abstract

Neural network systems are provided that comprise one or more neural networks. The first neural network can comprise a convolutional neural network (CNN) long short-term memory (LSTM) architecture for receiving a primary data set comprising text messages and output a primary data structure comprising a text pattern-based feature. The second neural network can comprise a CNN architecture for receiving a secondary data sets derived from the primary data set and output a plurality of secondary data structures. The third neural network can combine the data structures to produce a combined data structure, and then process it to produce a categorized data structure comprising the text messages assigned to targets. The primary data set can comprise hate speech and the categorized data structure can comprise target categories, for example, hate targets. Methods of operating neural network systems and computer program products for performing such methods are also provided.

First claim

What is claimed is:

1. A neural network system comprising:
a computer readable medium comprising:
a first neural network comprising a convolutional neural network (CNN) long short-term memory (LSTM) architecture, the first neural network configured to receive a primary data set comprising text messages and output a primary data structure,
a second neural network comprising a CNN architecture, the second neural network configured to receive a plurality of secondary data sets derived from the primary data set and output a plurality of secondary data structures, wherein the plurality of secondary data sets comprises a graph-based feature, a semantic feature, or both, and
a third neural network comprising a deep neural network (DNN) architecture, the third neural network configured to:
combine the primary data structure and the plurality of second data structures to produce a combined data structure, and
process the combined data structure to produce a categorized data structure comprising the text messages assigned to targets; and

a processor configured to operate the first, second, and third neural networks.

2. The neural network system of claim 1 , wherein at least one of the first, second, and third neural networks comprise a max-pooling layer, a dropout layer, or both.

3. The neural network system of claim 1 , wherein the plurality of secondary data sets comprises at least two graph-based features.

4. The neural network system of claim 1 , wherein the second neural network comprises a plurality of channels, and each channel of the plurality of channels comprises a different data set of the plurality of secondary data sets.

5. The neural network system of claim 4 , wherein the second neural network comprises a convolution layer and a filter length of the convolution layer differs between channels.

6. The neural network system of claim 5 , wherein:
the plurality of channels comprises at least three channels comprising a first channel, a second channel, and a third channel;
the filter length of the convolution layer is different in each of the three channels;
the plurality of secondary data sets comprising a first data set, a second data set, and a third data set;
the first channel comprises the first data set, the second channel comprises the second data set, and the third channel comprises the third data set; and
the first data set comprises a first graph-based feature, the second data set comprises a second graph-based feature, and the third data set comprises a semantic feature.

7. The neural network system of claim 1 , wherein:
the text messages comprise hate speech;
the categorized data structure comprises a plurality of target categories; and
the target categories comprise hate targets; and the hate targets comprise two or more of behavior, religion, ethnicity, class, nationality, race, sexual orientation, disability, gender, and morphology.

8. The neural network system of claim 1 , wherein:
the text messages comprise language relating to an event, a product, an individual, a hobby, music, a location, an activity, a health issue, a utility issue, a safety issue, a weather phenomenon, a complaint, or an emotion, or any combination thereof;
the categorized data structure comprises a plurality of target categories; and
the target categories comprise events, products, individuals, hobbies, music genres, songs, locations, activities, health issues, utility issues, safety issues, weather phenomena, complaints, or emotions, or any combination thereof.

9. The neural network system of claim 1 , wherein the output of the first neural network comprises a text pattern-based feature.

10. The neural network system of claim 1 , wherein the third neural network is configured as a classifier comprising a plurality of binary classifiers configured to operate as a one versus all classifier.

11. The neural network system of claim 1 , further comprising
a user interface configured to enable a user to interact with the first, second, and third neural networks.

12. A method of operating a target identification system, the method comprising:
receiving a primary data set comprising text messages;
generating a plurality of secondary data sets from the primary data set, the generation comprising production of a graph-based feature data set and a semantic feature data set;
processing the primary data set using a first convolutional neural network (CNN) comprising long short-term memory (LSTM) to produce a primary data structure comprising a text pattern feature;
processing the plurality of secondary data sets using a second CNN to produce a plurality of secondary data structures;
combining the primary data structure and the plurality of secondary data structures to produce a combined data structure; and
processing the combined data structure using a deep neural network (DNN) configured as a classifier to output a categorized data structure comprising the text messages assigned to targets.

13. The method of claim 12 , wherein the processing of the primary data set comprises embedding the primary data set in the first CNN, and the processing of the plurality of secondary data sets comprises embedding the plurality of secondary data sets in the second CNN.

14. The method of claim 12 , wherein the combining comprises concatenating the primary data structure and the plurality of secondary data structures, and the method further comprises flattening the primary data structure and the plurality of secondary data structures prior to the concatenation.

15. The method of claim 12 , wherein the generating comprises:
constructing a graph comprising nodes corresponding to words in the text messages and edges connecting nodes based on occurrence within a predetermined distance;
identifying words biased by predetermined keywords in the graph to produce the graph-based feature data set, the graph-based feature data set being a first graph-based feature data set; and
identify words having a high load determined by a number of shortest path passes using a node corresponding to a word to produce a second graph-based feature data set of the secondary data set.

16. The method of claim 12 , wherein the second CNN comprises a plurality of channels comprising a first channel configured to process the graph-based feature data set and a second channel configured to process the semantic feature data set; the method further comprising applying a different length filter to each filter.

17. The method of claim 12 , wherein:
the text messages comprise language relating to hate, an event, a product, an individual, a hobby, music, a location, an activity, a health issue, a utility issue, a safety issue, a weather phenomenon, a complaint, or an emotion, or any combination thereof;
the categorized data structure comprises a plurality of target categories; and
the target categories comprise hate targets, events, products, individuals, hobbies, music genres, songs, locations, activities, health issues, utility issues, safety issues, weather phenomena, complaints, or emotions, or any combination thereof.

18. The neural network system of claim 1 , wherein the plurality of secondary data sets comprises a graph-based feature data set, or a semantic feature data set, or both.

19. The neural network system of claim 1 , wherein the plurality of secondary data sets comprises a graph-based feature data set and a semantic feature data set.

20. A computer program product comprising a non-transitory computer readable medium, wherein the non-tran

Assignees

• Conduent Business Services Llc

Inventors

• Kumar Niraj

Classifications

• G06N3/045

  Combinations of networks · CPC title

• G06N3/048

  Activation functions · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title