Pre-processing for identifying nonsense passages in documents being ingested into a corpus of a natural language processing system

Patent metadata

Field	Value
Publication number	US-9842096-B2
Application number	US-201615152826-A
Country	US
Kind code	B2
Filing date	May 12, 2016
Priority date	May 12, 2016
Publication date	Dec 12, 2017
Grant date	Dec 12, 2017

Abstract

A mechanism is provided in a data processing system for identifying nonsense passages in documents being ingested into a corpus. A natural language processing pipeline configured to execute in the data processing system receives an input document to be ingested into a corpus. The natural language processing pipeline divides the input document into a plurality of input passages. A filter component of the natural language processing pipeline identifies whether each input passage is a nonsense passage based on a value of a metric determined according to a set of feature counts. The natural language processing pipeline filters each input passage in the plurality of input passages based on whether the input passage is identified as a nonsense passage or not identified as a nonsense passage to form a filtered plurality of input passages. The natural language processing pipeline adds the filtered plurality of input passages into the corpus.

First claim

What is claimed is:

1. A method, in a data processing system, for identifying nonsense passages in documents being ingested into a corpus, the method comprising:
receiving, by a natural language processing pipeline configured to execute in the data processing system, an input document to be ingested into a corpus;
dividing, by the natural language processing pipeline, the input document into a plurality of input passages;
identifying, by a filter component of the natural language processing pipeline, whether each input passage is a nonsense passage based on a value of a metric determined according to a set of feature counts, wherein identifying whether a given input passage is a nonsense passage comprises:
annotating, by an annotator in the natural language processing pipeline, the given input passage within the plurality of input passages with linguistic features to form an annotated passage;
counting, by metric counters component in the natural language processing pipeline, a number of instances of each type of linguistic feature in the annotated passage to form a set of feature counts;
determining, by the metric counters component of the natural language processing pipeline, a value for a metric based on the set of feature counts; and
comparing, by a comparator component of the natural language processing pipeline, the value for the metric to a predetermined model threshold;

filtering, by the natural language processing pipeline, each input passage in the plurality of input passages based on whether the input passage is identified as a nonsense passage or not identified as a nonsense passage to form a filtered plurality of input passages; and
adding, by the natural language processing pipeline, the filtered plurality of input passages into the corpus.

2. The method of claim 1 , wherein filtering each input passage comprises removing a given input passage responsive to the given input passage being identified as a nonsense passage.

3. The method of claim 1 , wherein filtering each input passage comprises marking a given input passage responsive to the given input passage being identified as a nonsense passage.

4. The method of claim 1 , wherein annotating the input passage comprises annotating the input passage for linguistic part-of-speech features.

5. The method of claim 1 , wherein the metric comprises a ratio of a number of instances of a first pert-of-speech to a number of instances of a second part-of-speech in the input passage.

6. The method of claim 1 , wherein the metric and the predetermined model threshold are defined in a profile data structure.

7. The method of claim 1 , further comprising:
responsive to receiving a candidate evidence passage for a candidate answer in a question answering system, determining whether the candidate evidence passage is a nonsense passage; and
filtering the candidate evidence passage out of natural language processing responsive to determining the candidate evidence passage is a nonsense passage.

8. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program comprises a natural language processing pipeline configured to execute on a data processing system to:
receive an input document to be ingested into a corpus;
divide the input document into a plurality of input passages;
identify whether each input passage is a nonsense passage based on a value of a metric determined according to a set of feature counts, wherein identifying whether a given input passage is a nonsense passage comprises:
annotating, by an annotator in the natural language processing pipeline, the given input passage within the plurality of input passages with linguistic features to form an annotated passage;
counting, by metric counters component in the natural language processing pipeline, a number of instances of each type of linguistic feature in the annotated passage to form a set of feature counts;
determining, by the metric counters component of the natural language processing pipeline, a value for a metric based on the set of feature counts; and
comparing, by a comparator component of the natural language processing pipeline, the value for the metric to a predetermined model threshold;

filter each input passage in the plurality of input passages based on whether the input passage is identified as a nonsense passage or not identified as a nonsense passage to form a filtered plurality of input passages; and
add the filtered plurality of input passages into the corpus.

9. The computer program product of claim 8 , wherein filtering each input passage comprises removing a given input passage responsive to the given input passage being identified as a nonsense passage.

10. The computer program product of claim 8 , wherein filtering each input passage comprises marking a given input passage responsive to the given input passage being identified as a nonsense passage.

11. The computer program product of claim 8 , wherein annotating the input passage comprises annotating the input passage for linguistic part-of-speech features.

12. The computer program product of claim 8 , wherein the metric comprises a ratio of a number of instances of a first part-of-speech to a number of instances of a second part-of-speech in the input passage.

13. The computer program product of claim 8 , wherein the metric and the predetermined model threshold are defined in a profile data structure.

14. The computer program product of claim 8 , wherein the natural language processing pipeline further causes the data processing system to:
responsive to receiving a candidate evidence passage for a candidate answer in a question answering system, determine whether the candidate evidence passage is a nonsense passage; and
filter the candidate evidence passage out of natural language processing responsive to determining the candidate evidence passage is a nonsense passage.

15. 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:
receive, by a natural language processing pipeline configured to execute in the data processing system, an input document to be ingested into a corpus;
divide, by the natural language processing pipeline, the input document into a plurality of input passages;
identify, by a filter component of the natural language processing pipeline, whether each input passage is a nonsense passage based on a value of a metric determined according to a set of feature counts, wherein identifying whether a given input passage is a nonsense passage comprises:
annotating, by an annotator in the natural language processing pipeline, the given input passage within the plurality of input passages with linguistic features to form an annotated passage;
counting, by metric counters component in the natural language processing pipeline, a number of instances of each type of linguistic feature in the annotated passage to form a set of feature counts;
determining, by the metric counters component of the natural language processing pipeline, a value for a metric based on the set of feature counts; and
comparing, by a comparator component of the natural language processing pipeline, the value for the metric to a predetermined model threshold;

filter, by the natural language processing pipeline, each input passage in the plurality of input passages based on whether the input passage is identified as a nonsense passage or not identified as a nonsense passage to form a filtered plu

Assignees

• IBM

Inventors

• Beller Charles E
• Drzewucki Michael
• Phipps Christopher
• Summers Kristen M
• Yu Julie T

Classifications

• G06F16/3329

  Natural language query formulation · CPC title

• G06F40/169 Primary

  Annotation, e.g. comment data or footnotes · CPC title

• G06F16/313

  Selection or weighting of terms for indexing · CPC title

• G06F16/367

  Ontology · CPC title

• G06F40/40

  Processing or translation of natural language (natural language analysis G06F40/20; semantic analysis G06F40/30) · CPC title