Identifying word-senses based on linguistic variations

• US 9,619,460 B2
• Filed: 09/23/2016
• Issued: 04/11/2017
• Est. Priority Date: 02/13/2015
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Bishop, Timothy A., Boxwell, Stephen A., Brumfield, Benjamin L., Desai, Nirav P., Vernier, Stanley J.
• Primary Examiner(s): Chawan, Vijay B
• Application Number:
  US15/273,739
• Publication Number:
  US 20170004130A1
• Time in Patent Office: 200 Days
• Field of Search: 704 9, 704 10, 704257, 704 3, 707706, 707707, 707708, 707723, 707737, 707999003, 707999101, 709230, 715230
• CPC Class Codes: G06F 40/242 Dictionaries
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06F 40/289 Phrasal analysis, e.g. finite state techniques or chunking
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06Q 50/00 Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism healthcare informatics G16H
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 70/00 ICT specially adapted for the handling or processing of medical references
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

One or more words are received. A set of frequency of occurrence values of the received word(s) within a set of domain tables is determined. A domain table in the set of domain tables is associated to the received word(s), based on the set of frequency of occurrence values meeting a threshold value. A word-sense of the received word(s) is determined based on a corresponding word-sense in the associated domain table and/or corresponding domain dictionary.

Claims

1. A computer system for identifying word-senses, the computer system comprising:

one or more computer processors;

one or more computer-readable storage media;

program instructions stored on the computer-readable storage media for execution by at least one of the one or more processors, the program instructions comprising;

program instructions to generate, by a computer, a plurality of arrays of aggregated statistical information of words, their corresponding word-senses, and temporal properties within different professional fields using an n-gram viewer, wherein the aggregated statistical information comprises frequency of usage of words, frequency of occurrence of words, frequency of co-occurrence of words with other words, and their respective corresponding word-senses;

program instructions to generate, by the computer, a set of domain tables based on the generated plurality of arrays of aggregated statistical information, wherein each of the domain tables within the set of domain tables corresponds to a different professional field comprising medical, veterinary, legal, and engineering;

program instructions to receive, from a remote server through a network, a digital text stream comprising metadata and one or more words from a doctor, using the computer, the network being an internet connection;

program instructions to select, using the metadata, a medical frequency domain table, veterinary frequency domain table, and a word-sense domain table from the set of domain tables;

program instructions to determine a frequency of occurrence value for the received digital text stream within each of the selected domain tables;

program instructions to receive a threshold from the doctor;

program instructions to associate the medical frequency domain table with the received digital text stream in response to the frequency of occurrence value satisfying the received threshold;

program instructions to determine a word-sense of the received digital text stream, by determining a corresponding word sense to the received digital text stream within the medical frequency domain table;

program instructions to assign a confidence value to the word-sense based on a degree of frequency of occurrence of the received digital text stream within the medical domain, wherein the word-sense has a higher confidence value, when the frequency of occurrence of the received digital text stream is higher within the medical domain table; and

program instructions to present the word-sense and the confidence value to the doctor.