Predicting lexical answer types in open domain question and answering (QA) systems

• US 9,015,031 B2
• Filed: 07/18/2012
• Issued: 04/21/2015
• Est. Priority Date: 08/04/2011
• Status: Active Grant

• Current Assignee: Hyundai Motor Company (Hyundai Motor Group), Kia Corp.
• Original Assignee: International Business Machines Corporation
• Inventors: Ferrucci, David A., Gliozzo, Alfio M., Kalyanpur, Aditya A.
• Primary Examiner(s): Abebe, Daniel D
• Application Number:
  US13/552,260
• Publication Number:
  US 20130035930A1
• Time in Patent Office: 1,007 Days
• Field of Search: None
• US Class Current: 704/7
• CPC Class Codes: G06F 16/90332 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

In an automated Question Answer (QA) system architecture for automatic open-domain Question Answering, a system, method and computer program product for predicting the Lexical Answer Type (LAT) of a question. The approach is completely unsupervised and is based on a large-scale lexical knowledge base automatically extracted from a Web corpus. This approach for predicting the LAT can be implemented as a specific subtask of a QA process, and/or used for general purpose knowledge acquisition tasks such as frame induction from text.

Claims

1. A computer-implemented method of inferring a lexical answer type from a question, said method comprising:

extracting at least one syntactic frame from a question string;

querying a lexical knowledge database to obtain at least one replacement term for a focus of said at least one syntactic frame, wherein said focus is a part of the question indicating a lexical answer type to the question,

substituting at least one of said replacement terms with a generalized type information term using said database;

ranking said replacement terms; and

selecting a top-ranked replacement term as an inferred lexical answer type to said question.

2. The method according to claim 1, wherein said syntactic frames comprise terms selected from the group consisting of:

a subject term;

a verb term;

an object term; and

an indirect object term.

3. The method according to claim 1, wherein extracting a set of syntactic frames from a question string comprises:

using a dependency parser to identify terms in a grammatical structure from said question string to be used as said extracted syntactic frame.

4. The method according to claim 1, wherein said lexical knowledge database comprises syntactic frames representing knowledge extracted from a corpus of data.

5. The method according to claim 1, wherein said entity type knowledge comprises frames having terms with is_a relationships.

6. The method according to claim 1, wherein querying said lexical knowledge database to obtain said at least one replacement term comprises:

applying a Generalized Frame Model to obtain said at least one replacement term.

7. The method according to claim 1, further comprising:

filtering out said replacement terms if said replacement term is not related to the context of said question.

8. The method according to claim 6, wherein said lexical knowledge database includes frame structures identified from a corpus of text, a frame structure having one or more slots-value pairs, a slot representing a syntactic role identified by a dependency relation, wherein said applying a Generalized Frame Model comprises:

specifying a frame cut comprising a sub-set of frames having non-empty slot-values for a given subset of slot-values pairs;

specifying a frame abstraction to determine a desired relationship among selected selected slot values from said given subset of slot-values pairs; and

,

generating, from said frame cut, plural frame vectors defining a multi-dimensional vector space from which relationships among selected selected slot values is determined,; and

,

processing said frame vectors to determine said desired relationship among selected selected slot values as defined by said abstraction.

9. The method according to claim 7, wherein said desired relationship includes an inferred type of a slot representing one of:

a syntactic subject role or syntactic object role in a corresponding verb and noun phrase.

10. A method for predicting a lexical answer types (LAT) in a question, the method comprising:

applying a frame-extraction utility to a question text to identify all frames involving a question focus, each frame having one or more slots-value pairs with a slot representing a syntactic role identified by a dependency relation, and including a question focus slot;

for each identified frame, creating a query frame structure having a focus slot variable, for each query frame structure, finding in a data corpus, a slot filler for the focus slot variable, said slot filler being part of a question focus from which said LAT is determined,

obtaining a score associated with each said slot filler found for each identified frame

ranking said slot fillers according to said scores; and

selecting a top-ranked slot filler as a predicted LAT to said question,

wherein a programmed processor device performs one or more of said applyuing, creating, finding, obtaining, ranking and selecting.

11. The method as in claim 10, wherein said data corpus includes frame structures identified from a corpus of text, a frame structure having one or more slots-value pairs, a slot representing a syntactic role identified by a dependency relation, said finding a slot filler comprising:

specifying a frame cut comprising a sub-set of frames having non-empty slot-values for a given subset of slot-values pairs;

specifying a frame abstraction to determine a desired relationship among selected selected slot values from said given subset of slot-values pairs; and

,

generating, from said frame cut, plural frame vectors defining a multi-dimensional vector space from which relationships among selected selected slot values is determined; and

,

processing said frame vectors to determine said desired relationship among selected selected slot values as defined by said abstraction.

12. The method as claimed in claim 11, wherein said desired relationship includes an inferred type for a slot filler of said focus slot representing one of:

a syntactic subject role or syntactic object role in corresponding verb and noun phrases.

13. The method as claimed in claim 11, wherein said finding slot fillers for the focus slot variable comprises:

tabulating raw-frequency counts of slot-fillers found from said frame cut.

14. The method as claimed in claim 11, wherein a specified frame cut includes an is_a frame cut including a sub-set of frames having is_a relationships representing associations between slot values and their lexical types, wherein, for each slot filler, said method comprising generating, a predicted LAT from said is_a frame cut.

15. The method as claimed in claim 11, wherein said finding slot fillers for the focus slot variable further comprises:

filtering the slot filler results of predicted LATs by Latent Semantic Analysis (LSA)-based topic similarity with a context of said question.

16. The method as claimed in claim 15, wherein said filtering comprises:

computing a similarity between said slot filler and the question text; and

discarding any slot-filler whose similarity is below a threshold value.

17. The method as claimed in claim 14, further comprising:

filtering the slot filler results of predicted LATs by Latent Semantic Analysis (LSA)-based topic similarity with a context of said question; and

,

for each slot filler, obtaining a predicted LAT from said is_a frame cut.