The following operators can be used to better focus your queries.
( ) , AND, OR, NOT, W/#
? single char wildcard, not at start
* multi char wildcard, not at start
(Cat? OR feline) AND NOT dog?
Cat? W/5 behavior
(Cat? OR feline) AND traits
Cat AND charact*
This guide provides a more detailed description of the syntax that is supported along with examples.
This search box also supports the look-up of an IP.com Digital Signature (also referred to as Fingerprint); enter the 72-, 48-, or 32-character code to retrieve details of the associated file or submission.
Concept Search - What can I type?
For a concept search, you can enter phrases, sentences, or full paragraphs in English. For example, copy and paste the abstract of a patent application or paragraphs from an article.
Concept search eliminates the need for complex Boolean syntax to inform retrieval. Our Semantic Gist engine uses advanced cognitive semantic analysis to extract the meaning of data. This reduces the chances of missing valuable information, that may result from traditional keyword searching.
Disclosed is a program for enabling to select the most appropriate Japanese equivalent word in machine translations.
English (United States)
This text was extracted from a PDF file.
At least one non-text object (such as an image or picture) has been suppressed.
This is the abbreviated version, containing approximately
54% of the total text.
Page 1 of 3
Statistical method for taxonomy oriented dictionary design and machine translation production system for multi-taxonomy documents
Calculate "Taxonomy Vector" of a source text First, calculate "Taxonomy Vector" of a source text. Taxonomy Vector represents what the text relates with in vector style. For example the element of the vector is Computer, Biology, Economy, Literature etc. If a source text describes economy, the vector element of Economy is large. Taxonomy Vector is calculated by what kind of categorizer.
Source text Categorizer Taxonomy
Calculate similarity level between the source text and dictionaries
Very many translation dictionaries are prepared. All of them have unique Taxonomy
Vector. Calculate similarity level between the source text and dictionaries using Taxonomy
Vector of the dictionaries and the source text. Any kind of methods to calculate similarity of two vectors are acceptable.
Translate the source text into a target language. Translation engine prioritizes the dictionary with the Taxonomy Vector which is similar to the vector of the source text.
[This page contains 3 pictures or other non-text objects]
Page 2 of 3
Dictionaries' Taxonomy Vector
Source text's Taxonomy Vector
Primary dictionary's vector
Secondary dictionary's vector
Correct the result of translation
Correct the result of translation if there are invalid translations and unknown words. Based on these corrections, create new dictionary. Optionally, the words which are not corrected can be added new dictionary as...