Corpus quality analysis

• US 10,169,706 B2
• Filed: 08/31/2017
• Issued: 01/01/2019
• Est. Priority Date: 07/28/2014
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Allen, Corville O., Freed, Andrew R., Salmon, Richard A., Strack, Beata J.
• Primary Examiner(s): Burke, Jeff A
• Application Number:
  US15/692,089
• Publication Number:
  US 20180005117A1
• Time in Patent Office: 488 Days
• Field of Search: None
• CPC Class Codes: G06F 40/20 Natural language analysis semantic analysis of natural language G06F40/30
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation

Abstract

A mechanism is provided in a data processing system for corpus quality analysis. The mechanism applies at least one filter to a candidate corpus to determine a degree to which the candidate corpus supplements existing corpora for performing a natural language processing (NLP) operation. Responsive to a determination to add the candidate corpus to the existing corpora based on a result of applying the at least one filter, the mechanism adds the candidate corpus to the existing corpora to form modified corpora. The mechanism performs the NLP operation using the modified corpora.

Claims

1. A method, in a data processing system comprising a processor and a memory, the memory comprising instructions executed by the processor to specifically configure the processor to implement a corpus quality analysis system for corpus quality analysis, the method comprising:

applying, by the corpus quality analysis system, at least one filter to a candidate corpus to determine a degree to which the candidate corpus supplement existing corpora for performing a natural language processing (NLP) operation, wherein the at least one filter comprises a first filter to determine whether documents in the candidate corpus contain NLP features known to be helpful for performing the NLP operation, wherein applying the first filter comprises;

extracting a set of the most frequent NLP features from a combination of the current corpora and candidate corpus;

examining evidence and candidate answers for questions answered correctly and incorrectly using the combination of the current corpora and candidate corpus;

determining a set most effective features from the set of the most frequent NLP feature using a machine learning model based on the evidence and candidate answers;

determining a number of the set of most effective features that are present in the candidate corpus; and

comparing the number of the set of most effective features that are present in the candidate corpus to the set of prerequisites for adding the candidate corpus to the existing corpora;

responsive to a determination to add the candidate corpus to the existing corpora based on a result of applying the at least one filter, adding, by the corpus quality analysis system, the candidate corpus to the existing corpora to form modified corpora; and

performing, by a question answering system executing in the data processing system, the NLP operation using the modified corpora.

2. The method of claim 1, wherein the at least one filter comprises a second filter to determine whether information can be extracted accurately from the new corpus based on general quality metrics of the candidate corpus.

3. The method of claim 2, wherein applying the second filter comprises:

collecting the general quality metrics from the candidate corpus, wherein the general quality metrics comprise at least one of a number of good quality sentences, a number of acronyms, cluster of data types in an interested space, accuracy of an English Slot Grammar parser, a volume of data, or document structures; and

comparing the general quality metrics to a set of prerequisites for adding the candidate corpus to the existing corpora.

4. The method of claim 1, wherein the at least one filter comprises a third filter to determine whether the candidate corpus contains documents having attributes that match a set of evidence documents that are known to provide high-confidence evidence and contains documents that cover a set of questions not sufficiently covered by the current corpora.

5. The method of claim 1, wherein the at least one filter comprises a second filter to determine whether information can be extracted accurately from the new corpus based on general quality metrics of the candidate corpus, and a third filter to determine whether the candidate corpus contains documents having attributes that match a set of evidence documents that are known to provide high-confidence evidence and contains documents that cover a set of questions not sufficiently covered by the current corpora.

6. The method of claim 5, wherein applying the at least one filter comprises:

applying the second filter to the candidate corpus; and

responsive to the candidate corpus not passing the second filter, determining not to add the candidate corpus to the existing corpora.

7. The method of claim 6, wherein applying the at least one filter further comprises:

responsive to the candidate corpus passing the second filter, applying the third filter to the candidate corpus; and

responsive to the candidate corpus not passing the third filter, determining not to add the candidate corpus to the existing corpora.

8. The method of claim 7, wherein applying the at least one filter further comprises:

responsive to the candidate corpus passing the third filter, applying the first filter to the candidate corpus; and

responsive to the candidate corpus not passing the first filter, determining not to add the candidate corpus to the existing corpora.

9. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a question answering system, causes the question answering system to implement a corpus quality analysis system for corpus quality analysis, wherein the computer readable program causes the data processing system to:

apply, by the corpus quality analysis system, at least one filter to a candidate corpus to determine a degree to which the candidate corpus supplements existing corpora for performing a natural language processing (NLP) operation, wherein the at least one filter comprises a first filter to determine whether documents in the candidate corpus contain NLP features known to be helpful for performing the NLP operation, wherein applying the first filter comprises;

extracting a set of the most frequent NLP features from a combination of the current corpora candidate corpus;

examining evidence and candidate answers for questions answered correctly and incorrectly using the combination of the current corpora and candidate corpus;

determining a set of most effective features from the set of the most frequent NLP features using a machine learning model based on the evidence and candidate answers;

determining a number of the set of most effective features that are present in the candidate corpus; and

comparing the number of the set of most effective features that are present in the candidate corpus to the set of prerequisites for adding the candidate corpus to the existing corpora;

responsive to a determination to add the candidate corpus to the existing corpora based on a result of applying the at least one filter, add, by the corpus quality analysis system, the candidate corpus to the existing corpora to form modified corpora; and

perform, by a question answering system executing in the data processing system, the NLP operation using the modified corpora.

10. The computer program product of claim 9, wherein the at least one filter comprises a second filter to determine whether information can be extracted accurately from the new corpus based on general quality metrics of the candidate corpus, and wherein applying the second filter comprises:

collecting the general quality metrics from the candidate corpus, wherein the general quality metrics comprise at least one of a number of good quality sentences, a number of acronyms, cluster of data types in an interested space, accuracy of an English Slot Grammar parser, a volume of data, or document structures; and

comparing the general quality metrics to a set of prerequisites for adding the candidate corpus to the existing corpora.

11. The computer program product of claim 9, wherein the at least one filter comprises a second filter to determine whether information can be extracted accurately from the new corpus based on general quality metrics of the candidate corpus, and a third filter to determine whether the candidate corpus contains documents having attributes that match a set of evidence documents that are known to provide high-confidence evidence and contains documents that cover a set of questions not sufficiently covered by the current corpora.

13. The computer program product of claim 11, wherein applying the at least one filter comprises:

applying the second filter to the candidate corpus; and

responsive to the candidate corpus not passing the second filter, determining not to add the candidate corpus to the existing corpora.

14. The computer program product of claim 13, wherein applying the at least one filter further comprises:

responsive to the candidate corpus passing the second filter, applying the third filter to the candidate corpus; and

responsive to the candidate corpus not passing the third filter, determining not to add the candidate corpus to the existing corpora.

15. The computer program product of claim 14, wherein applying the at least one filter further comprises:

responsive to the candidate corpus passing the third filter, applying the first filter to the candidate corpus; and

responsive to the candidate corpus net passing the first filter, determining not to add the candidate corpus to the existing corpora.

12. An apparatus comprising:

a processor; and

a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to implement a corpus quality analysis system for corpus quality analysis, wherein the instructions cause the processor to;

apply, by the corpus quality analysis system, at least one filter to a candidate corpus to determine a degree to which the candidate corpus supplements existing corpora for performing a natural language processing (NLP) operation, wherein the at least one filter comprises a first filter to determine whether documents in the candidate corpus contain NLP features known to be helpful for performing the NLP operation, wherein applying the first filter comprises;

extracting a set of the most frequent NLP features from a combination of the current corpora and candidate corpus;

examining evidence and candidate answers for questions answered correctly and incorrectly using the combination of the current corpora and candidate corpus;

determining a set of most effective features from the set of the most frequent NLP features using a machine learning model based on the evidence and candidate answers;

determining a number of the set of most effective features that are present in the candidate corpus; and

comparing the number of the set of most effective features that are present in the candidate corpus to the set of prerequisites for adding the candidate corpus to the existing corpora;

responsive to a determination to add the candidate corpus to the existing corpora based on a result of applying the at least one filter, add, by the corpus quality analysis system, the candidate corpus to the existing corpora to form modified corpora; and

perform, by a question answering system executing in the data processing system, the NLP operation using the modified corpora.

16. The apparatus of claim 12, wherein the at least one filter comprises a second filter to determine whether information can be extracted accurately from the new corpus based on general quality metrics of the candidate corpus, and wherein applying the second filter comprises:

collecting the general quality metrics from the candidate corpus, wherein the general quality metrics comprise at least one of a number of good quality sentences, a number of acronyms, cluster of data types in an interested space, accuracy of an English Slot Grammar parser, a volume of data, or document structures; and

comparing the general quality metrics to a set of prerequisites for adding the candidate corpus to the existing corpora.

17. The apparatus of claim 12, wherein the at least one filter comprises a second filter to determine whether information can be extracted accurately from the new corpus based on general quality metrics of the candidate corpus, and a third filter to determine whether the candidate corpus contains documents having attributes that match a set of evidence documents that are known to provide high-confidence evidence and contains documents that cover a set of questions not sufficiently covered by the current corpora.

18. The apparatus of claim 17, wherein applying the at feast ore r comprises:

applying the second filter to the candidate corpus; and

responsive to the candidate corpus not passing the second filter, determining not to add the candidate corpus to the existing corpora.

19. The apparatus of claim 18, wherein applying the at least one filter further comprises:

responsive to the candidate corpus passing the second filter, applying the third filter to the candidate corpus, and

responsive to the candidate corpus not passing the third filter, determining not to add the candidate corpus to the existing corpora.

20. The apparatus of claim 19, wherein applying the at least one filter further comprises:

responsive to the candidate corpus passing the third filter, applying the first filter to the candidate corpus; and

responsive to the candidate corpus not passing the first filter, determining not to add the candidate corpus to the existing corpora.