Clustering a set of natural language queries based on significant events

Patent metadata

Field	Value
Publication number	US-11036776-B2
Application number	US-201916436882-A
Country	US
Kind code	B2
Filing date	Jun 10, 2019
Priority date	Nov 8, 2016
Publication date	Jun 15, 2021
Grant date	Jun 15, 2021

Abstract

Clustering a set of natural language queries NLQs based on a set of significant events retrieved from a corpus stored in a computer system is described. A set of NLQs is used by a search engine for searching a selected corpus to retrieve respective sets of significant events. The set of NLQs is clustered into a plurality of NLQ clusters according to a number of common significant events being returned by the search engine for respective members of an NLQ cluster.

First claim

The invention claimed is:

1. An improved method for searching a selected corpus by clustering a set of natural language queries (NLQ) based on a set of significant events retrieved from a corpus stored in a computer system comprising:
using a set of NLQs by a search engine for searching a selected corpus to retrieve respective sets of significant events from the selected corpus;
for each NLQ in the set of NLQs, using a first set of entities from the NLQ and using the first set of entities to search for a first set of significant events in the selected corpus;
using a second set of entities from the first set of significant events to search for a second set of significant events in the selected corpus;
producing a distribution profile for each NLQ based on a number of common significant events retrieved using the first set of entities and a number of common significant events retrieved using the second set of entities;
clustering the set of NLQs into NLQ clusters according to the distribution profiles; and
using a respective NLQ cluster for a function in the search engine.

2. The method as recited in claim 1 , wherein the first set of significant events in the selected corpus are determined in a first search pass and the second set of significant events in the selected corpus in a second search pass.

3. The method as recited in claim 1 , wherein the clustering is also based in part on common linguistic and semantic features of respective NLQs.

4. The method as recited in claim 3 , further comprising:
from user input, receiving a threshold number of common significant events as a clustering criterion; and
from user input, receiving a threshold number of common linguistic and semantic features in an NLQ as a clustering criterion.

5. The method as recited in claim 2 , further comprising:
building a knowledge graph based on a selected corpus stored in the computer system, the knowledge graph having a set of co-occurrence scores on edges of the knowledge graph between respective events in the selected corpus placed at the nodes of the knowledge graph, wherein the co-occurrence scores indicate co-occurrence of entities within respective pairs of events in the selected corpus; and
using the knowledge graph to extract the second set of entities.

6. The method as recited in claim 2 , further comprising:
extracting a third set of entities from the second set of significant events and using the third set of entities to search for a third set of significant events in the selected corpus in a third search pass; and
producing a distribution profile for each NLQ based on a number of significant events retrieved in the first search pass, the second search pass and the third search pass.

7. The method as recited in claim 2 , further comprising:
determining a significance score for respective events retrieved by the search system according to a metric of mutual information (MMI); and
filtering the retrieved events according to respective significance scores to produce the first set of significant events.

8. The method as recited in claim 1 , wherein the first and second sets of entities have no common members.

9. Apparatus, comprising:
a processor;
computer memory holding computer program instructions executed by the processor for improved searching of a selected corpus by clustering a set of natural language queries (NLQ), the computer program instructions comprising:
program code, operative to use a set of NLQs by for searching a selected corpus to retrieve respective sets of significant events from the selected corpus;
program code, operative for each NLQ in the set of NLQs to use a first set of entities from the NLQ and to use the first set of entities to search for a first set of significant events in the selected corpus;
program code, operative to use a second set of entities from the first set of significant events to search for a second set of significant events in the selected corpus;

program code, operative to produce a distribution profile for each NLQ based on a number of common significant events retrieved using the first set of entities and a number of common significant events retrieved using the second set of entities;
program code, operative to cluster the set of NLQs into NLQ clusters according to the distribution profiles; and
program code, operative to use a respective NLQ cluster for a function in the search engine.

10. The apparatus as recited in claim 9 , wherein the first set of significant events in the selected corpus are determined in a first search pass and the second set of significant events in the selected corpus in a second search pass.

11. The apparatus as recited in claim 9 , wherein the clustering is also based in part on common linguistic and semantic features of respective NLQs.

12. The apparatus as recited in claim 11 , further comprising:
program code, operative to receive a threshold number of common significant events as a clustering criterion; and
program code, operative to receive a threshold number of common linguistic and semantic features in an NLQ as a clustering criterion.

13. The apparatus as recited in claim 10 , further comprising:
program code, operative to build a knowledge graph based on a selected corpus stored in the computer system, the knowledge graph having a set of co-occurrence scores on edges of the knowledge graph between in the selected corpus placed at the nodes of the knowledge graph, wherein the co-occurrence scores indicate co-occurrence of entities within respective pairs of events in the selected corpus; and
program code, operative to use the knowledge graph to extract the second set of entities.

14. The apparatus as recited in claim 10 , further comprising:
program code, operative to extract a third set of entities from the second set of significant events and using the third set of entities to search for a third set of significant events in the selected corpus in a third search pass; and
program code, operative to produce a distribution profile for each NLQ based on a number of significant events retrieved in the first search pass, the second search pass and the third search pass.

15. A computer program product in a non-transitory computer readable medium for use in a data processing system, the computer program product holding computer program instructions executed by the data processing system for improved searching of a selected corpus by performing clustering of natural language queries (NLQ), the computer program instructions comprising:
program code, operative to use a set of NLQs by for searching a selected corpus to retrieve respective sets of significant events from the selected corpus;
program code, operative for each NLQ in the set of NLQs to use a first set of entities from the NLQ and to use the first set of entities to search for a first set of significant events in the selected corpus;
program code, operative to use a second set of entities from the first set of significant events to search for a second set of significant events in the selected corpus;
program code, operative to produce a distribution profile for each NLQ based on a number of common significant events retrieved using the first set of entities and a number of common significant events retrieved using the second set of entities;
program code, operative to cluster the set of NLQs into NLQ clusters according to the distribution profiles; and
program code, operative to use a respective NLQ cluster for a function in the search engine.

16. The computer program product as recited in claim 15 , wherein the first set of signi

Assignees

• IBM

Inventors

• Chandrasekaran Swaminathan
• Kozhaya Joseph N
• Krishnamurthy Lakshminarayanan

Classifications

• G06F16/3344 Primary

  using natural language analysis · CPC title

• G06F16/906

  Clustering; Classification · CPC title

• G06F16/35

  Clustering; Classification · CPC title