SYSTEMS AND METHODS FOR AUTOMATIC SEMANTIC ROLE LABELING OF HIGH MORPHOLOGICAL TEXT FOR NATURAL LANGUAGE PROCESSING APPLICATIONS

• US 20080319735A1
• Filed: 06/22/2007
• Published: 12/25/2008
• Est. Priority Date: 06/22/2007
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Zitouni, Imed, Kambhatla, Nandakishore
• Granted Patent: US 8,527,262 B2
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

Systems and methods are provided for automated semantic role labeling for languages having complex morphology. In one aspect, a method for processing natural language text includes receiving as input a natural language text sentence comprising a sequence of white-space delimited words including inflicted words that are formed of morphemes including a stem and one or more affixes, identifying a target verb as a stem of an inflicted word in the text sentence, grouping morphemes from one or more inflicted words with the same syntactic role into constituents, and predicting a semantic role of a constituent for the target verb.

Claims

1. A method for processing natural language text, comprising:

receiving as input a natural language text sentence comprising a sequence of white-space delimited words including inflicted words that are formed of morphemes including a stem and one or more affixes;

identifying a target verb as a stem of an inflicted word in the text sentence;

grouping morphemes from one or more inflicted words with the same syntactic role into constituents; and

predicting a semantic role of a constituent for the target verb.

2. The method of claim 1, comprising performing an automated morphological analysis to segment the text sentence into a sequence of morphemes by separating stems and affixes of inflicted words.

3. The method of claim 1, comprising analyzing the words of the input text sentence on a character level to identify stems and affixes.

4. The method of claim 1, wherein predicting a semantic role of a constituent for the target verb is performed automatically using a semantic role labeling model that predicts semantic roles using a plurality of syntactic and lexical features extracted from the input text sentence.

5. The method of claim 1, wherein identifying a target verb and grouping morphemes is performed using an automated syntactic parsing process to build a parse tree where each node in the parse tree is a constituent.

6. The method of claim 5, wherein the automated syntactic parsing process is implemented using a parsing model trained on an annotated corpus of verb-argument structures.

7. The method of claim 1, wherein predicting a semantic role of a constituent for the target verb, comprises:

identifying a constituent that is an argument of the target verb; and

assigning a semantic role to a constituent that is identified as an argument of the target verb.

8. The method of claim 1, wherein predicting a semantic role of a constituent for a target verb, comprises:

determining a likelihood of a semantic role of a constituent for the target verb over a set of possible semantic roles given the identified target verb; and

assigning the constituent a semantic role label having the highest likelihood over the set of possible semantic roles.

9. The method of claim 1, wherein predicting a semantic role of a constituent of the target verb is performed using a statistical model trained to process a plurality of lexical and syntactic features.

10. The method of claim 1, wherein the input text sentence is Arabic text.

11. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for processing natural language text, the method steps comprising:

receiving as input a natural language text sentence comprising a sequence of white-space delimited words including inflicted words that are formed of morphemes including a stem and one or more affixes;

identifying a target verb as a stem of an inflicted word in the text sentence;

grouping morphemes from one or more inflicted words with the same syntactic role into constituents; and

predicting a semantic role of a constituent for the target verb.

12. The program storage device of claim 11, further comprising instructions for performing an automated morphological analysis to segment the text sentence into a sequence of morphemes by separating stems and affixes of inflicted words.

13. The program storage device of claim 11, comprising analyzing the words of the input text sentence on a character level to identify stems and affixes.

14. The program storage device of claim 11, wherein the instructions for predicting a semantic role of a constituent for the target verb includes instructions for automatically predicting semantic roles using a semantic role labeling model that predicts semantic roles using a plurality of syntactic and lexical features extracted from the input text sentence.

15. The program storage device of claim 11, wherein the instructios for identifying a target verb and grouping morphemes comprise instructions for using an automated syntactic parsing process to build a parse tree where each node in the parse tree is a constituent.

16. The program storage device of claim 15, wherein the instriction for automated syntactic parsing process comprise instructiosn for using a parsing model trained on an annotated corpus of verb-argument structures.

17. The program storage device of claim 11, wherein the instructions for predicting a semantic role of a constituent for the target verb, comprise instructions for:

identifying a constituent that is an argument of the target verb; and

assigning a semantic role to a constituent that is identified as an argument of the target verb.

18. The program storage device of claim 11, wherein the instructions for predicting a semantic role of a constituent for the target verb, comprise instructions for:

determining a likelihood of a semantic role of a constituent for the target verb over a set of possible semantic roles given the identified target verb; and

assigning the constituent a semantic role label having the highest likelihood over the set of possible semantic roles.

19. The program storage device of claim 11, wherein the instructions for predicting a semantic role of a constituent for the target verb, comprise instructions for using a statistical model trained to process a plurality of lexical and syntactic features.

20. The program storage device of claim 11, wherein the input text sentence is Arabic text.

21. A method for processing natural language text, comprising:

receiving as input a natural language text sentence comprising a sequence of white-space delimited words including at least one inflicted word comprising a stem and one or more affixes;

automatically segmenting the white-space delimited words into separate morphemes including prefixes, stems and suffixe;

automatically grouping morphemes into constituents and identifying morphemes that are target verbs; and

automatically predicting a semantic role of a constituent for a target verb using a trained statistical model.

22. A method for processing natural language text, comprising:

receiving as input a natural language text sentence comprising a sequence of white-space delimited words including at least one inflicted word comprising a stem and one or more affixes;

automatically performing a morphological analysis on the text sentence as a sequence of characters to extract morphological information;

automatically detecing stems of inflicted words that are target verbs and grouping stems and affixes of different words into constituent, using the extracted morphological information; and

automatically predicting a semantic role of each constituent for a target verb using a trained statistical model using a plurality of feature data including morphological features extracted during morphological analysis.