Method for text improvement via linguistic abstractions

• US 20100332217A1
• Filed: 06/29/2009
• Published: 12/30/2010
• Est. Priority Date: 06/29/2009
• Status: Abandoned Application

• Current Assignee: Avraham Shpigel, Daniel Radzinski, Peter Michael Paz, Shalom Wintner
• Original Assignee: Avraham Shpigel, Daniel Radzinski, Peter Michael Paz, Shalom Wintner
• Inventors: Radzinski, Daniel, Paz, Peter Michael, Wintner, Shalom, Shpigel, Avraham
• Application Number:
  US12/385,931
• Publication Number:
  US 20100332217A1
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
• CPC Class Codes: G06F 40/253 Grammatical analysis; Style critique
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

This invention provides hierarchical, gradual and iterative methods, systems, and software for improving and correcting natural language text. The methods comprise the steps of applying natural language processing (NLP) algorithms to a corpus of sentences so as to abstract each sentence; applying scoring and linguistic annotation to each abstract sentence; applying NLP algorithms to abstract input sentences; applying search algorithms to match an abstract input sentence to at least one abstract corpus sentence; and applying NLP algorithms to adapt said matched abstract corpus sentence to the input sentence.

Claims

1. A hierarchical, gradual and iterative method for improving text sentences, the method comprising the steps of:

a) processing a corpus of sentences so as to form abstracted corpus sentences;

b) abstracting at least one user inputted sentence so as to form at least one abstracted user input sentence; and

c) forming at least one improved user outputted sentence.

2. A method according to claim 1, wherein said processing comprises at least one of:

part of speech tagging, word sense disambiguation, identification of synonyms, identification of grammatical relations, and identification of phrase boundaries.

3. A method according to claim 1, wherein said abstracting comprises at least one of:

identification of sub-phrases and clauses, substituting wild-cards for each noun phrase (NP), substituting wild-cards for adjunct words and phrases, identification of synonyms for words, and combinations thereof.

4. A method according to claim 1, wherein said processing consists of handling sentence sub-phrases separately as standalone clauses.

5. A method according to claim 1, wherein said processing comprises partial abstraction of at least one phrase, full abstraction of at least one phrase;

abstracting of at least one word by replacing said words with corresponding synonym sets; and

breaking up at least one phrase to sub-phrases; and

combinations thereof.

6. A method according to claim 1, wherein said processing comprises applying said improvement method to sentences which have previously been improved.

7. A method according to claim 1, wherein said processing a corpus of sentences comprises scoring of each abstract sentence by at least one of:

frequency scoring of the abstract sentence, confidence scoring based on at least one confidence level of an NLP tool.

8. A method according to claim 1, wherein said processing a corpus of sentences comprises linguistic annotation comprising associating an abstracted sentence with a set of linguistic properties.

9. A method according to claim 8, wherein said linguistic properties comprise at least one of:

tense, voice, register, polarity, sentiment, writing style, domain, genre, syntactic sophistication, and combinations thereof.

10. A method according to claim 1, wherein said forming an improved user outputted sentence comprises searching for at least one corpus abstracted sentence that is matched to said user inputted abstracted sentence.

11. A method according to claim 10, wherein said searching step comprises at least one of:

maximizing compatibility with preferences of a user, minimizing changes between the abstracted input sentence and the abstracted corpus sentence, maximizing a score of abstracted sentences, maximizing a confidence level of the linguistic processing, and combinations thereof.

12. A method according to claim 1, wherein said forming at least one improved user outputted sentence comprises adaptation of said abstracted corpus sentence to said user inputted sentence, wherein said adaptation comprises at least one of:

replacing each wild-card noun phrase (NP) with concrete NPs from said inputted sentence, adapting a grammatical structure of a resulting sentence, replacing and adapting adjuncts, and reconstructing source sentence sub-phrases.

13. A method according to claim 12, wherein said adaptation of wild-card NPs comprises the steps of:

a) abstracting out-of-vocabulary words and phrases;

b) selecting NPs from a corpus based on frequency;

c) restoring abstracted out-of-vocabulary words or phrases; and

d) adapting NP properties.

14. A method according to claim 12, wherein adapting adjuncts is based on grammatical relations in the user inputted sentence.

15. A method according to claim 1, wherein said corpus comprises at least one of a corpus on a local PC, an organizational private corpus, and a remote network corpus on a remote server.

16. A method according to claim 1, wherein said user inputted sentence comprises at least one of a sentence in at least one document, a sentence in an email message, a sentence in a blog text, a sentence in a web page, and a sentence in any electronic text form.

17. A method according to claim 1, wherein said method is adapted to help people with reading disabilities by improving a source text wherein a syntactic sophistication is minimized.

18. A method according to claim 1, further comprising text evaluation, based upon counting a number of corrections required by improving source text using pre-defined parameter settings.

19. A method according to claim 1, further comprising ontology-based advertising enabled by at least one of the following steps:

a) improving an input sentence;

b) using input sentence elements as keywords and key phrases; and

c) displaying relevant advertising to a user.

20. A computer software product for improving text sentences, comprising a computer-readable medium in which program instructions are stored, which instructions, when read by a computer, cause the computer to:

a) process a corpus of sentences so as to form abstracted corpus sentences;

b) abstract at least one user inputted sentence so as to form at least one abstracted user input sentence; and

c) form at least one improved user outputted sentence.