Method and apparatus for determining user intent

Patent metadata

Field	Value
Publication number	US-2019377794-A1
Application number	US-201916352159-A
Country	US
Kind code	A1
Filing date	Mar 13, 2019
Priority date	Jun 7, 2018
Publication date	Dec 12, 2019
Grant date	—

Abstract

The disclosed embodiments describe methods, systems, and apparatuses for determining user intent. A method is disclosed comprising obtaining a session text of a user; calculating, by the processor, a feature vector based on the session text; determining probabilities that the session text belongs to a plurality of intent labels, the probabilities calculated using a multi-level hierarchal intent classification model, the intent labels assigned to levels in the multi-level hierarchal intent classification model; and assigning a user intent to the session text based on the probabilities.

First claim

What is claimed is:

1 . A method comprising:
obtaining, by a processor, a session text of a user; calculating, by the processor, a feature vector based on the session text; determining, by the processor, probabilities that the session text belongs to a plurality of intent labels, the probabilities calculated using a multi-level hierarchal intent classification model, the intent labels assigned to levels in the multi-level hierarchal intent classification model; and assigning, by the processor, a user intent to the session text based on the probabilities.

2 . The method of claim 1 , the calculating the feature vector comprising:
identifying, by the processor, a word segment set corresponding to the session text, the word segment set comprising a plurality of word segments; calculating, by the processor, a weight value for each of the word segments; constructing, by the processor, a word vector for each of the word segments; constructing, by the processor, a text vector corresponding to the session text based on the word vector of each of the word segments and the corresponding weight values; and using, by the processor, the feature vector as the text vector.

3 . The method of claim 2 , the constructing the text vector comprising:
performing, by the processor, a weighted summation on word vectors of each of the word segments according to the corresponding weight values of each word segment, the weighted summation generating a sum vector; and using, by the processor, the sum vector as the corresponding text vector

4 . The method of claim 2 , the using the feature vector as the text vector comprising:
combining, by the processor, the weight values of each of the word segments into a weight vector; combining, by the processor, the weight vector and the text vector into a combined vector; and using, by the processor, the combined vector as the feature vector.

5 . The method of claim 1 , the determining probabilities that the session text belongs to the plurality of intent labels comprising:
inputting, by the processor, the feature vector into a plurality of classifiers corresponding to the levels of the multi-level hierarchal intent classification model; and obtaining, by the processor, probabilities of the session text belonging to each of the intent labels in each of the levels.

6 . The method of claim 1 , the determining probabilities that the session text belongs to the plurality of intent labels comprising:
inputting, by the processor, the feature vector into a first classifier corresponding to a first level of the levels; obtaining, by the processor, probabilities of the session text belonging to each of the intent labels in the first level based on output of the first classifier; determining, by the processor, a combination vector based on the feature vector and the probabilities of the session text belonging to each of the intent labels in the first level; and inputting, by the processor, the combination vector into a second classifier corresponding to a second level of the multi-level hierarchal intent classification model, and obtaining probabilities of the session text belonging to each intent label in the second level.

7 . The method of claim 1 , the assigning the user intent to the session text based on the probabilities comprising determining, by the processor, the user intent based on the probabilities and probability thresholds corresponding to each of the levels of the multi-level hierarchal intent classification model.

8 . A non-transitory computer readable storage medium for tangibly storing computer program instructions capable of being executed by a computer processor, the computer program instructions defining the steps of:
obtaining a session text of a user; calculating a feature vector based on the session text; determining probabilities that the session text belongs to a plurality of intent labels, the probabilities calculated using a multi-level hierarchal intent classification model, the intent labels assigned to levels in the multi-level hierarchal intent classification model; and assigning a user intent to the session text based on the probabilities.

9 . The non-transitory computer readable storage medium of claim 8 , the calculating the feature vector comprising:
identifying a word segment set corresponding to the session text, the word segment set comprising a plurality of word segments; calculating a weight value for each of the word segments; constructing a word vector for each of the word segments; constructing a text vector corresponding to the session text based on the word vector of each of the word segments and the corresponding weight values; and using the feature vector as the text vector.

10 . The non-transitory computer readable storage medium of claim 9 , the constructing the text vector comprising:
performing a weighted summation on word vectors of each of the word segments according to the corresponding weight values of each word segment, the weighted summation generating a sum vector; and using the sum vector as the corresponding text vector

11 . The non-transitory computer readable storage medium of claim 9 , the using the feature vector as the text vector comprising:
combining the weight values of each of the word segments into a weight vector; combining the weight vector and the text vector into a combined vector; and using the combined vector as the feature vector.

12 . The non-transitory computer readable storage medium of claim 8 , the determining probabilities that the session text belongs to the plurality of intent labels comprising:
inputting the feature vector into a plurality of classifiers corresponding to the levels of the multi-level hierarchal intent classification model; and obtaining probabilities of the session text belonging to each of the intent labels in each of the levels.

13 . The non-transitory computer readable storage medium of claim 8 , the determining probabilities that the session text belongs to the plurality of intent labels comprising:
inputting the feature vector into a first classifier corresponding to a first level of the levels; obtaining probabilities of the session text belonging to each of the intent labels in the first level based on output of the first classifier; determining a combination vector based on the feature vector and the probabilities of the session text belonging to each of the intent labels in the first level; and inputting the combination vector into a second classifier corresponding to a second level of the multi-level hierarchal intent classification model, and obtaining probabilities of the session text belonging to each intent label in the second level.

14 . The non-transitory computer readable storage medium of claim 8 , the assigning the user intent to the session text based on the probabilities comprising determining the user intent based on the probabilities and probability thresholds corresponding to each of the levels of the multi-level hierarchal intent classification model.

15 . An apparatus comprising:
a processor; and a storage medium for tangibly storing thereon program logic for execution by the processor, the stored program logic comprising:
logic, executed by the processor, for obtaining a session text of a user,
logic, executed by the processor, for calculating a feature vector based on the session text,
logic, executed by the processor, for determining probabilities that the session text belongs to a plurality of intent labels, the probabilities calculated using a multi-level hierarchal intent classification

Assignees

• Alibaba Group Holding Ltd

Inventors

• Li Ling
• Shi Zhiwei
• Liang Yanjie

Classifications

• G06V30/40

  Document-oriented image-based pattern recognition · CPC title

• G06V30/153

  using recognition of characters or words · CPC title

• G06V10/40

  Extraction of image or video features · CPC title

• G06F18/214 Primary

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06F18/24

  Classification techniques · CPC title