Abstract:
A language analysis method for an electronic device storing a basic corpus and a temporary corpus is provided. The language analysis method includes steps of receiving user inputs and generating signals; converting signals into textualized information; analyzing the textualized information; obtaining a first understanding result according to the basic corpus, the vocabulary segmentation results, and the sentence segmentation results; determining whether the first understanding result is an appropriate understanding according to the context; determining one or more anaphoric vocabularies when the first understanding result is an inappropriate understanding; determining a temporary understanding result of the one or more anaphoric vocabularies and a second understanding result of the textualized information according to the context; and determining a reply for the textualized information, according to the second understanding result, the basic corpus, and the temporary corpus. An electronic device using the language analysis method is also provided.

Description:
BACKGROUND 
     1. Technical Field 
     The present disclosure relates to an electronic device and a natural language analysis method thereof. 
     2. Description of Related Art 
     Electronic devices with human-machine dialogue function, such as mobile phones, laptops, and tablets, are capable of interactively talking to users. How to exactly understand the natural language of users has been the challenge in artificial intelligence discipline for a long time period. During the human-machine dialogue process, the electronic device segments a sentence spoken by a user into pieces of words and/or phrases, analyzes the meanings of the sentence to exclude unreasonable meaning(s), then creates machine readable language such as binary language, that is based on the sentence of the user. The electronic device then understands the sentence of the user by using the created machine readable language and a basic dictionary pre-stored therein, to obtain the meanings of the sentence of the user. Although the base dictionary includes language knowledge and grammar knowledge, we often use an expression, such as a word, or a phrase, to take the place of another expression, even when the two expressions have different word classes and/or different meanings. Thus, misunderstandings often happen because of these substitutions in human language, especially for the Chinese. 
     Therefore, what is needed is an electronic device and a natural language analysis method thereof to alleviate the limitations described above. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The components in the drawings are not necessarily drawn to scale, the emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Moreover, in the drawings, like reference numerals designate corresponding sections throughout the several views. 
         FIG. 1  is a block diagram of an electronic device in accordance with an exemplary embodiment. 
         FIG. 2  is a flowchart of a natural language analysis method for electronic devices, such as the one of  FIG. 1 , in accordance with the exemplary embodiment. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  is an exemplary embodiment of a block diagram of an electronic device  100 . The electronic device  100  can more accurately understand the language of users, and has a higher efficiency in translating human-machine dialogues. The electronic device  100  is a computing device such as a computer or a laptop. In alternative embodiments, the electronic device  100  can be other electronic devices with human-machine dialogue functions, such as a mobile phone, or a tablet, for example. 
     The electronic device  100  includes a storage unit  10 , an input unit  20 , a processor  30 , a buffer  40 , a display unit  50 , and an audio output unit  60 . The storage unit  10  stores a collection of dictionaries and other linguistic sources in one body (basic corpus  11 ) recording a vast amount of vocabularies, words and phrases, and the frequency of use of each word and each phrase. The basic corpus  11  is a collection of language use material which is selected and sequenced according to linguistic criterium. The basic corpus  11  is also a huge text database which is machine readable and is collected according to a given design criterium. In the embodiment, the basic corpus  11  is a text database storing a huge amount of Chinese natural language. In other embodiments, the text database of the languages stored in the basic corpus  11  can be varied according to need, the basic corpus  11  can be a text database storing a huge amount of the natural language of English, and of Japanese, for example. 
     The buffer  40  stores a temporary corpus  41  recording a mapping relationship between at least one substitute vocabulary and at least one temporary meaning. The temporary corpus  41  is collected during the human-machine dialogue process of the electronic device  100 . The temporary corpus  41  may record for example the temporary meaning “eat in the canteen” associated with the substitute vocabulary “eat canteen”. In an alternative embodiment, the temporary corpus  41  is stored in the storage unit  10 . 
     The input unit  20  generates signals in response to a user&#39;s spoken voice and/or written character input, and transmits the signals to the processor  30 . In the embodiment, the signals can be audio data (audio signals) and/or data concerning written characters (character signals). 
     The processor  30  includes a voice and character converting module  31 , a language analysis module  32 , a determination module  33 , and an intelligent conversation module  34 . When the electronic device  100  is powered on, the input unit  20  is activated and the user can talk to and give commands to the electronic device  100  via the input unit  20 . 
     The voice and character converting module  31  converts the audio signals and/or character signals from the input unit  20  into a textualized message in a predetermined language. In the embodiment, the textualized message can include one or more words, one or more phrases, one or more sentences, and/or one or more paragraphs of text, and the predetermined language is Chinese. In an alternative embodiment, the predetermined language can be English, or Japanese, or some other language. 
     The language analysis module  32  firstly segments the textualized message converted by the voice and character converting module  31  into one or more vocabularies, and obtains one or more vocabularized segments including the one or more vocabularies. Then the language analysis module  32  applies a sentence construction rule to the vocabularized segments of the textualized message, and obtains one or more sentence segments. The language analysis module  32  further retrieves the frequency of use of each segmented vocabulary, from the basic corpus  11 , to obtain a first language understanding result of the textualized message by analyzing the one or more vocabularized segments and the one or more sentence segments, based on the retrieved frequency of use of each segmented vocabulary. The language analysis module  32  also transmits the first language understanding result to the determination module  33 . 
     The determination module  33  analyzes a number of selected textualized messages converted within a predetermined time period, and including the original textualized message using a contextual understanding method, so as to determine whether the first language understanding result is a reasonable understanding, and transmits the determined result to the language analysis module  32  and the intelligent conversation module  34 . 
     If the determination module  33  determines the first language understanding result is a reasonable understanding, the intelligent conversation module  34  determines a reply message for the textualized message of the user based on the first language understanding result and the basic corpus  11 . 
     If the determination module  33  determines that the first language understanding result is an unreasonable understanding, the determination module  33  further determines one or more vocabularies which give rise to an unreasonable understanding. The determined vocabularies are substitute vocabularies. The determination module  33  also compares the determined substitute vocabularies with the substitute vocabularies recorded in the temporary corpus  41 , to determine whether the temporary corpus  41  does have a record of the determined substitute vocabularies. 
     If the temporary corpus  41  does not have a record of one or more of the determined substitute vocabularies, the language analysis module  32  further determines the temporary meaning of each determined substitute vocabulary not recognized in the temporary corpus  41  by analyzing the context, including the determined substitute vocabulary, thus to obtain a second understanding result of the textualized message. The language analysis module  32  also updates the temporary corpus  41  by storing the mapping relationship between the determined substitute vocabularies and the temporary meaning. The intelligent conversation module  34  then determines a message to reply to the textualized message (reply message) based on the second language understanding result, the updated temporary corpus  41 , and the basic corpus  11 . 
     If the temporary corpus  41  does have a record of all of the determined substitute vocabularies, the language analysis module  32  retrieves the temporary meaning of each determined substitute vocabulary from the temporary corpus  41 , and determines a third understanding of the textualized message. The intelligent conversation module  34  then determines a reply message based on the third language understanding result, the temporary corpus  41 , and the basic corpus  11 . 
     The voice and character converting module  31  further converts the reply message determined by the intelligent conversation module  34  into a textualized reply message and/or voice signals, and controls the display unit  50  to display the textualized reply message and/or the audio output unit  60  to playback the message vocally. 
     The buffer  40  further stores data temporarily, namely, the textualized reply message converted by the voice and character converting module  31 , the vocabularies and the vocabularized segments and the sentence segments segmented by the language analysis module  32 , and the first, second, and third understanding results obtained by the language analysis module  32 . 
     In an embodiment, when both the buffer  40  and the storage unit  10  fail to store a temporary corpus  41 , the determination module  33  further creates a temporary corpus  41  and stores a mapping relationship between the determined substitute vocabularies and the temporary meaning in the created temporary corpus  41 . The determination module  33  further deletes the temporary corpus  41  stored in the buffer  40  and/or the storage unit  10 , when the current human-machine dialogue between the electronic device  100  and the user is over. 
       FIG. 2  shows a flowchart of a natural language analysis method for the electronic device  100  of  FIG. 1 . The electronic device  100  stores a basic corpus  11  recording a vast amount of vocabularies: words and phrases, and the frequency of use of each word and each phrase. The method includes the following steps, each of which is related to the various components contained in the electronic device  100 . 
     In step S 20 , the input unit  20  generates signals in response to a user&#39;s spoken words and/or written character input. In the embodiment, the signals can be audio signals and/or character signals. 
     In step S 21 , the voice and character converting module  31  converts the signals from the input unit  20  into a textualized message in a predetermined language. In the embodiment, the textualized message can include a word, a phrase, a sentence, and/or a paragraph of a text, and the predetermined language is Chinese. 
     In step S 22 , the language analysis module  32  segments the textualized message into one or more vocabularies, obtains one or more vocabularized segments including the one or more vocabularies, further segments the textualized message based on the vocabularized segments and a sentence construction rule, and obtains one or more sentence segments. 
     In step S 23 , the language analysis module  32  retrieves the frequency of use of each segmented vocabulary, from the basic corpus  11 , thus to obtain a first language understanding result of the textualized message by analyzing the one or more vocabularized segments and the one or more sentence segments, based on the retrieved frequency of use of each segmented vocabulary. 
     In step S 24 , the determination module  33  selects and analyzes a number of textualized messages converted within a predetermined time period, including the original textualized message which is segmented by the language analysis module  32 , using a contextual understanding method, so as to determine whether the first language understanding result is a reasonable understanding. If no, the process goes to step S 25 , otherwise, the process goes to step S 28 . 
     In step S 25 , the language analysis module  32  determines one or more vocabularies (substitute vocabularies) which cause an unreasonable understanding, compares the determined substitute vocabularies with any substitute vocabularies recorded in the temporary corpus  41  to determine whether the temporary corpus  41  does record the determined substitute vocabularies. If no, the process goes to step S 26 , otherwise, the process goes to step S 27 . In an embodiment, when both the buffer  40  and the storage unit  10  do not have a temporary corpus  41 , the language analysis module  32  further creates a temporary corpus  41  and stores the mapping relationship between the determined substitute vocabularies and the temporary meaning in the created temporary corpus  41 . 
     In step S 26 , if the temporary corpus  41  does not have a record of one or more of the determined substitute vocabularies, the language analysis module  32  determines the temporary meaning of each determined substitute vocabulary by analyzing the context including the determined substitute vocabulary, obtains a second understanding result of the textualized message, and updates the temporary corpus  41  by storing the mapping relationship between the determined substitute vocabularies and the temporary meaning therein. 
     In step S 27 , if the temporary corpus  41  does have a record of all of the determined substitute vocabularies, the language analysis module  32  retrieves the temporary meaning of each determined substitute vocabulary from the temporary corpus  41 , and determines a third understanding of the textualized message. 
     In step S 28 , the intelligent conversation module  34  determines a reply message for the textualized message based on the third language understanding result, the temporary corpus  41 , and the basic corpus  11 . In an embodiment, the determination module  33  further deletes the temporary corpus  41  stored in the buffer  40  and/or the storage unit  10 , when the current human-machine dialogue between the electronic device  100  and the user is over. 
     In step S 29 , the voice and character converting module  31  converts the reply message determined by the intelligent conversation module  36  into a reply message and/or signals for the sound of a voice, and controls the display unit  50  to display the reply textualized message and/or the audio output unit  60  to play the sound of a voice. 
     The electronic device  100  has a better ability to understand the meanings of user&#39;s language, and a translation of any speech by the user to the electronic device  100  is more efficient. 
     Although the present disclosure has been specifically described on the basis of the embodiments thereof, the disclosure is not to be construed as being limited thereto. Various changes or modifications may be made to the embodiments without departing from the scope and spirit of the disclosure.