Patent Publication Number: US-2013231919-A1

Title: Disambiguating system and method

Description:
BACKGROUND 
     1. Technical Field 
     The present disclosure relates to language disambiguating systems and a method relating thereto. 
     2. Description of Related Art 
     When words or phrases are ambiguous, there is more than one interpretation. When translating from one language into another, there is a need to resolve any ambiguities to ensure full and correct understanding of sentences. 
     Therefore, it is desirable to provide a disambiguating system and method, which can overcome the above-mentioned problem. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a functional block diagram of a disambiguating system used in sentences according to a first embodiment. 
         FIG. 2  is a flowchart showing a disambiguating method implemented by the disambiguating system of  FIG. 1 . 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments of the disclosure will be described with reference to the accompanying drawings. 
       FIG. 1  shows a disambiguating system  10  in accordance with an embodiment of this disclosure. The disambiguating system  10  can be connected to an application system  20 , such as a translation machine. The application system  20  has a user interface for receiving user inputs, such as sentences which need to be disambiguated. The application system  20  receives outputs from the disambiguating system  10 , such as the result of disambiguating the sentences. 
     The disambiguating system  10  includes an interface  100 , a storage unit  200 , and a processor  300 . The disambiguating system  10  exchanges information with the application system  20  via the interface  100 . For example, the disambiguating system  10  receives sentences for disambiguation from the application system  20  via the interface  100 , and the application system  20  receives outputs from the disambiguating system  10  via the interface  100 . 
     The storage unit  200  stores a first database  2100  and a second database  2200 . The first database  2100  includes a dictionary of ambiguous language data, such as ambiguous words and/or phrases. The second database  2200  includes a collection of disambiguating algorithms, such as disambiguating algorithms based on professional semantics, colloquial semantics, and context. Each piece of ambiguous language data in the dictionary is associated with at least one disambiguating algorithm. 
     The processor  300  includes a recognition module  3100 , a disambiguating module  3200 , a selection module  3300 , and an output module  3400 . 
     The recognition module  3100  receives a sentence or other input from the application system  10  via the interface and recognizes if the sentence includes a piece of ambiguous language data which is defined in the dictionary. In detail, the recognition module  3100  searches each word and phrase of the sentence, and determines whether the words and/or phrases are ambiguous. For example, the recognition module  3100  searches and finds the phrase “underground factory” in the sentence. The sentence “[T]his is an underground factory and should be banned” is ambiguous as the phrase “underground factory” is defined in the dictionary as having a special meaning, and in the sentence “I went fishing for some sea bass” the word “bass” is also ambiguous. The word “mouse” is another example of a word with more than one meaning, in the sentence “I killed a mouse this morning”. 
     The first database  2100  also includes distinct and different definitions of the phrase “underground factory” and the words “bass” and “mouse” in the dictionary. For example, the phrase “underground factory” has two distinct definitions: (1) an illegal factory (colloquial semantics), and (2) a factory operating below the surface of the earth. The word “bass” also has two distinct definitions: (1) a type of fish, and (2) audible tones of low frequency. The word “mouse” also has two distinct definitions: (1) small rodent, and (2) a computer input device. 
     The first database  2100  also associates the phrase “underground factory” with the disambiguating algorithms based on colloquial semantics and context, and the words “bass”, and “mouse,” with the disambiguating algorithms based upon professional semantics and context. 
     The disambiguating module  3200  is to disambiguate the recognized piece of ambiguous language date to generate results of disambiguating, using the associated disambiguating algorithm(s) of the output from the recognition module  3100 . For example, the disambiguating module  3200  interprets the phrase “underground factory” as “an illegal factory” using the disambiguating algorithms based on colloquial semantics and context (the word “banned” in the context provides enough evidence to prompt disambiguation of the phrase “underground factory”). The disambiguating module  3200  interprets the word “bass” as a type of fish using the disambiguating algorithms based on professional semantics and context (the word “fishing” and “sea” in the context provide enough evidence to prompt disambiguation of the word “bass”). The disambiguating module  3200  interprets the word “mouse” as a computer input device using the disambiguating algorithms based on professional semantics and as a small rodent using disambiguating algorithm based on context (the word “killed” in the context provides evidence to prompt disambiguation of the word “mouse”). 
     The selection module  3300  selects an interpretation from results, using various methods such as decision tree. For example, the selection module  3300  selects “illegal factory” as the definition of the phrase “underground factory” because both the disambiguating algorithms based on colloquial semantics and context yield the same result of “illegal factory” . The selection module  3300  selects “a type of fish” as the appropriate definition of the word “bass” as both the disambiguating algorithms based on professional semantics and context result in the interpretation “a type of fish”. The selection module  3300  selects “a small rodent” instead of “a computer input device” as the interpretation of the meaning of the word “mouse” using decision tree method. 
     The output module  3400  outputs the interpretations. 
       FIG. 2  is a flowchart showing a disambiguating method implemented by the disambiguating system of  FIG. 1 . 
     In step S 21 , the recognition module  3100  receives a sentence from the application system  10  via the interface  100 . 
     In step S 22 , the recognition module  3100  recognizes if a piece of ambiguous language data which is defined in the dictionary is existed in the sentence. 
     In step S 23 , the disambiguating module  3200  disambiguates the recognized piece of ambiguous language data to produce one or more results of disambiguating, utilizing the at least one associated disambiguating algorithm, and generate results of disambiguating. 
     In step S 24 , the selection module  3300  selects an interpretation from the results. 
     In step S 25 , the output module  3400  outputs the interpretation to the application system  10  via the interface  100 . 
     In another embodiment, the first database  2100  and the second database  2200  can be updated by a user to edit (e.g., add, change, or delete) the language data and disambiguating algorithms. 
     Particular embodiments are shown here and described by way of illustration only. The principles and the features of the present disclosure may be employed in various and numerous embodiments thereof without departing from the scope of the disclosure as claimed. The above-described embodiments illustrate the scope of the disclosure but do not restrict the scope of the disclosure.