SYSTEMS AND METHODS FOR DETERMINING THE TOPIC STRUCTURE OF A PORTION OF TEXT

Patent №

US 7,130,837

Granted

2006-10-31

Filed 2002

Owner

XEROX CORPORATION

Lab

AI components

6

ml · nlp · vision · speech · kr · hardware

Assignment

Recorded

Dataset

AIPD

2023_r1 edition

Application

10103053

Systems and methods for determining the topic structure of a document including text utilize a Probabilistic Latent Semantic Analysis (PLSA) model and select segmentation points based on similarity values between pairs of adjacent text blocks. PLSA forms a framework for both text segmentation and topic identification. The use of PLSA provides an improved representation for the sparse information in a text block, such as a sentence or a sequence of sentences. Topic characterization of each text segment is derived from PLSA parameters that relate words to “topics”, latent variables in the PLSA model, and “topics” to text segments. A system executing the method exhibits significant performance improvement. Once determined, the topic structure of a document may be employed for document retrieval and/or document summarization.

AI classification

Natural language1.00
Machine learning1.00
Speech1.00
Knowledge representation0.99
Vision0.97
AI hardware0.97
Planning0.01
Evolutionary computation0.00

Ownership

XEROX CORPORATION

assignment · 130550850

Assignors

TSOCHANTARIDIS, IOANNIS, BRANTS, THORSTEN H., CHEN, FRANCINE R.

On an employer assignment, the assignors are typically the inventors.

© 2026 NYSGPT2525 LLC