NATURAL LANGUAGE PROCESSING METHOD AND SYSTEM

• US 20110301941A1
• Filed: 03/18/2010
• Published: 12/08/2011
• Est. Priority Date: 03/20/2009
• Status: Abandoned Application

• Current Assignee: SYL Research Limited
• Original Assignee: SYL Research Limited
• Inventors: De Vocht, Petrus Matheus Godefridus
• Application Number:
  US12/996,742
• Publication Number:
  US 20110301941A1
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 40/216 using statistical methods
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

A computer implemented natural language processing method, the method including the steps of: analysing a sentence string within textual information to determine sub-components of the sentence string, assigning one or more unique tokens to each determined sub-component, determining a probability of use that a determined sub-component has one or more specific meanings, based on the determined probability of use, creating a valid set of unique tokens that are associated with the sentence string, and linking verb sub-components associated with one or more of the unique tokens in the valid set of unique tokens to a pre-defined limited sub-set of verbs to create an identification tuple that maps onto the sub-set of verbs.

Claims

1. A computer implemented natural language processing method, the method including the steps of:

analysing a sentence string within textual information to determine sub-components of the sentence string,

assigning one or more unique tokens to each determined sub-component,

determining a probability of use that a determined sub-component has one or more specific meanings,

based on the determined probability of use, creating a valid set of unique tokens that are associated with the sentence string, and

linking verb sub-components associated with one or more of the unique tokens in the valid set of unique tokens to a pre-defined limited sub-set of verbs to create an identification tuple that maps onto the sub-set of verbs.

2. The method of claim 1 further including the steps of retrieving a document via a document retrieval interface, and analysing the contents of the document to determine sentence strings within the document.

3. The method of claim 2, wherein the document retrieval interface is one of a document server, a scanner, an e-mail interface, a peer to peer interface, and a file transfer protocol interface.

4. The method of claim 2, wherein the step of analysing the document to determine sentence strings includes the step of detecting at least one of a full stop, capital letter, comma, semi-colon, colon or question mark.

5. The method of claim 2, further including the steps of converting the retrieved document to at least one of an HTML and XHTML format prior to analysing the document contents to determine sentence strings.

6. The method of claim 2, wherein the step of analysing the contents of the document to determine sentence strings further includes the step of first analysing the contents of the document to determine textual information.

7. The method of claim 1, wherein the step of analysing the sentence string to determine sub-components includes the step of detecting at least one of an anaphora and a conjunction.

8. The method of claim 1, wherein a sub-component is a single part of speech.

9. The method of claim 8, wherein the single part of speech is a single word.

10. The method of claim 8, wherein the single part of speech is a group of words considered to be a single part of speech.

11. The method of claim 1, wherein the step of assigning one or more unique tokens to a sub-component includes the step of determining a probability of use for the syntactic or semantic use of the sub-component.

12. The method of claim 11, wherein the syntactic use determination includes the steps of searching for the sub-component in a set of pre-stored sub-component records, and, upon finding a pre-stored sub-component record that is associated with the sub-component,

assigning a unique token that is associated with the found pre-stored sub-component record.

13. The method of claim 1, wherein the step of determining a probability of use includes the step of determining the semantic or syntactic use of the sub-component.

14. The method of claim 13, wherein the step of determining the semantic or syntactic use of the determined sub-component includes the step of analysing further sub-components that surround the determined sub-component to determine a probability of use of the determined sub-component by analysing a set of pre-stored sub-component records to determine if the further sub-components are related to the determined sub-component.

15. The method of claim 14, wherein the pre-stored sub-component records include at least one of synonyms, semantic markers, semantic verbs and lexical relationships associated with the determined sub-component.

16. The method of claim 15, wherein the lexical relationships include at least one of synonyms, hypernyms, meronyms, antonyms, holonyms, hyponyms and instances of the determined sub-component.

17. The method of claim 13, wherein the step of determining the semantic use of the determined sub-component includes the step of determining a probability of use by determining and analysing further sentence strings within the textual information to find further sentence strings that are relevant to the sentence string.

18. The method of claim 17 further including the step of determining a probability of use based on the distance between the determined relevant further sentence strings and the sentence string.

19. The method of claim 13, wherein the step of determining the semantic use of the determined sub-component includes the step of determining a probability of use by determining the likely subject matter of a document in which the sentence strings are located.

20. The method of claim 13, wherein the step of determining the semantic use of the determined sub-component includes the step of determining a probability of use by retrieving a pre-determined probability of use based on an analysed training set of data.

21. The method of claim 1 further including the step of storing the identification tuple.

22. The method of claim 1 further including the step of inserting a reference to one or more sentence strings in the identification tuple.

23. The method of claim 1, wherein a multiple-to-multiple relationship is created between a plurality of identification tuples when the identification tuples are associated with the same or similar sentence strings.

24. The method of claim 1 further including the step of applying rules to the identification tuple to take into account common sense knowledge based on everyday usage of language.

25. The method of claim 1 further including the step of determining an invalid sentence string analysis that does not provide a resultant set of unique tokens within a predefined probability of use.

26. The method of claim 25 further including the step of logging information to identify the invalid sentence structure and enabling the invalid sentence structure to be reviewed.

27. The method of claim 26 further including the step of displaying the invalid sentence structure and enabling the sentence structure to be manually corrected.

28. The method of claim 26 further including the step of displaying the invalid sentence structure and enabling a set of unique tokens to be manually assigned to sub-components of the sentence structure.

29. The method of claim 26 further including the step of displaying the sub-components of the invalid sentence structure and enabling the sub-component to be categorised syntactically or semantically.

30. The method of claim 1 wherein the sentence string analysis further includes the steps of determining statistical information within the sentence string.

31. The method of claim 30, wherein the statistical information determined is used in conjunction with further statistical information and statistical analysis functions to output statistically based results.

32. The method of claim 1 wherein the sentence strings form at least part of a natural language search query.

33. The method of claim 32, further including the steps of creating a search query identification tuple from the search query, and comparing the search query identification tuple against one or more further identification tuples to find answers to the search query.

34. The method of claim 33, wherein the one or more further identification tuples are created at the time the natural language search query is made.

35. The method of claim 33, wherein the one or more further identification tuples are stored based on analysis carried out on textual information prior to the natural language search query being made.

36. The method of claim 33, wherein the step of comparing includes the step of finding a link between verbs or nouns in the search query identification tuple and verbs or nouns in the one or more further identification tuples.

37. The method of claim 36, wherein the verbs or nouns in the search query identification tuple and further identification tuples are linked through a lexicon data entry that associates a limited sub-set of verb and noun synonyms for each verb.

38. The method of claim 36, wherein the step of comparing includes the step of calculating a rank value based on the link and the tense of the verbs in the search query identification tuple and the one or more further identification tuples.

39. The method of claim 36, wherein the step of comparing includes the steps of determining how many common parameters exist in the search query identification tuple and the one or more further identification tuples, and calculating a rank value based on the number of common parameters.

40. The method of claim 36, wherein the step of comparing includes the steps of determining how linguistically close the parameters within the search query identification tuple and the one or more further identification tuples relate, and calculating a rank value based on the closeness of the relationship.

41. The method of claim 33, wherein the search query identification tuple is analysed to determine which part of the tuple the answer to the query relates.

42. The method of claim 1 further including the step of utilising the identification tuple to automatically assign one or more classifications to the textual information.

43. The method of claim 1 wherein the textual information is retrieved from a pre-defined external source, and the method further includes the steps of:

monitoring textual data output by the external source to identify pre-defined words or sentences associated with pre-defined subject matter, and

analysing any detected pre-defined words or sentences to create the identification tuple.

44. The method of claim 1, whereupon determination that the determined sub component has more than one meaning the method further includes the step of assigning probability weightings to each meaning.

45. The method of claim 1 further including the steps of performing syntactic analysis on the sub-components to determine probabilities that the sub component is a particular part of speech, and subsequently performing semantic analysis to determine the semantics of the sub-component.

46. The method of claim 1 wherein the sub-set of verbs is a set of verbs related to a sub-component that is a verb.

47. The method of claim 1 further including the step of:

linking noun sub-components associated with one or more of the unique tokens in the valid set of unique tokens to a pre-defined limited sub-set of nouns to create an identification tuple that maps onto the sub-set of nouns.

48. The method of claim 47 wherein the sub-set of nouns is a set of homonyms related to a sub-component that is a noun.

49. A natural language processing system including:

a text processing module arranged to analyse a sentence string within textual information to determine sub-components of the sentence string,

a parsing and semantic processing module arranged to assign one or more unique tokens to each determined sub-component, determine a probability of use that a determined sub-component has one or more specific meanings, and based on the determined probability of use, create a valid set of unique tokens that are associated with the sentence string, and a lexicon module arranged to contain links for each verb sub-component such that each link associates a verb sub-component with a pre-defined limited sub-set of verbs to enable the parsing and logic module to create an identification tuple that maps onto the sub-set of verbs.

50. The system of claim 49 further including an interface module and an inference engine, wherein the system is arranged and configured to retrieve a document via a document retrieval interface, and analyze the contents of the document to determine sentence strings within the document.