Abstract:
A method and apparatus provides for interpreting a foreign word or phrase using a contextual likelihood model and a dictionary. An apparatus may translate foreign language text by taking context into account and displaying the translation with alternatives on an adaptive user interface display. The contextual likelihood model may be interlaced with a dictionary. In an embodiment, the interaction between the contextual likelihood model and a dictionary may result in an adaptive adjustment of the meanings or the order of meanings displayed. The order of meanings displayed may be representative of the calculated likelihoods.

Description:
FIELD OF INVENTION 
       [0001]    The invention relates generally to a method and apparatus for facilitating context based translation of different languages. 
       BACKGROUND 
       [0002]    Globalization is driving people actively or passively to use multiple languages in their daily lives. Thus, language translation has become an important task in many instances. For example, many people that are attempting to learn a second language often encounter new words when they read newspapers or magazines written in a foreign language. Without proper translation of the new words, the meaning of the newspaper or magazine may not be fully understood. Also, tourists or business travelers visiting foreign countries may not understand signs or markers as they may not be knowledgeable in a particular foreign language. A translation solution would allow tourists and/or business travelers to more fully enjoy their travels. 
         [0003]    Therefore, for the foregoing reasons, a method and apparatus for an improved language translation model for use in mobile devices would be advantageous. 
       SUMMARY 
       [0004]    Many of the aforementioned problems are solved by providing a method and apparatus for interpreting a word or phrase using a contextual likelihood model. One embodiment relates to a method comprising steps of receiving a signal having an environmental cue; parsing the environmental cue into word segments; receiving a selection of a parsed word segment for translation; determining at least one dictionary meaning of selected word segment; determining a likelihood of the at least one dictionary meaning; ranking the at least one dictionary meaning based on the determined likelihood; and displaying the ranked at least one dictionary meaning. An apparatus and computer-readable medium comprising executable instructions suitable for carrying out the method are also included. 
         [0005]    In an aspect of the invention, a contextual likelihood model may be interlaced with a dictionary. In an embodiment, the interaction between the contextual likelihood model and a dictionary may result in an adaptive adjustment of the meanings or the order of meanings displayed. The order of meanings displayed may be representative of the calculated likelihoods. 
         [0006]    In another aspect of the invention, the contextual likelihood model provides a scalable system in which an on-line contextual likelihood model may be used with mobile devices. In an embodiment, off-line training may be conducted through use of a language model, the output of which may be used to update the on-line contextual likelihood model. 
         [0007]    These as well as other advantages and aspects of the invention are apparent and understood from the following detailed description of the invention, the attached claims, and the accompanying drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0008]    The present invention is illustrated by way of example and not limited in the accompanying figures in which like reference numerals indicate similar elements. 
           [0009]      FIG. 1  illustrates a block diagram of a communication system in accordance with an aspect of the invention. 
           [0010]      FIG. 2  illustrates an apparatus in accordance with an aspect of the invention. 
           [0011]      FIG. 3  illustrates a flow diagram for a contextual likelihood model in accordance with an aspect of the invention. 
           [0012]      FIG. 4  illustrates exemplary user interface displays for a method of context translation in accordance with an aspect of the invention. 
           [0013]      FIG. 5  illustrates a method of contextual likelihood translation in accordance with an aspect of the invention. 
           [0014]      FIG. 6  illustrates a method of filtering and/or pre-pressing in accordance with an aspect of the invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0015]    In the following description of the various embodiments, reference is made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration various embodiments in which the invention may be practiced. It is to be understood that other embodiments may be utilized and structural and functional modifications may be made without departing from the scope of the present invention. 
         [0016]    In an aspect of the invention, optical character recognition (OCR) may be used to enable text input, recognition, and translation of text into another language. For example, optical character recognition may be applied to pictures taken with cameras or other devices, the pictures containing foreign language text. The use of optical character recognition may enable users to input text without having to be familiar with foreign language characters and/or sentence structure. In an embodiment, a dynamic user interface screen may display a ranked list of possible meanings of translated text. 
         [0017]    Aspects of the present invention may be utilized across a broad array of networks and communication protocols.  FIG. 1  illustrates an example of a wireless communication system  110  in which systems and methods according to at least some embodiments may be employed. One or more network-enabled mobile devices  112 , such as a personal digital assistant (PDA), cellular telephone, mobile terminal, personal video recorder, portable television, personal computer, digital camera, digital camcorder, portable audio device, portable radio, or combinations thereof, are in communication with a service source  122  through a broadcast network  114  (which may include the Internet or similar network) and/or a cellular network  116 . The mobile terminal/device  112  may include a digital broadband broadcast receiver device. The service source  122  may be connected to several service providers such as advertisement source  125  that may provide their actual program content or information or description of their services and programs to the service source  122  that further provides the content or information to the mobile device  112 . The several service providers including advertisement source  125  may include but are not limited to one or more television and/or digital television service providers, AM/FM radio service providers, advertisement servers and/or providers, SMS/MMS push service providers, Internet content or access providers. 
         [0018]    In one or more arrangements, broadcast network  114  may broadcast data from one or more service sources such as service source  122 . Service source  122  may obtain or receive data from a server or provider  125 . The data may then be received by mobile terminal  112  through the broadcast network  114  and stored in a database for display to a user of terminal  112 . One method of broadcasting data is using IP datacasting (IPDC). IPDC combines digital broadcasting and Internet Protocol. As such, a variety of information and services may be transmitted using such a network and protocol. 
         [0019]    The mobile device  112  may also send and receive messages to and from the service source  122  through the cellular network  116 . The cellular network  116  may include a wireless network and a base transceiver station transmitter  120 . The cellular network may include a second/third-generation (2G/3G) cellular data communications network, a Global System for Mobile communications network (GSM), a Universal Mobile Telecommunications System (UMTS) and/or other wireless communication network such as a WLAN network. In one or more aspects, communications through the cellular network  116  may allow a service source  122  to distribute on an individual basis. That is, rather than broadcasting data to an entire subscriber population, the service source  122  may obtain and distribute data based on user interests, usage statistics, a user&#39;s most frequent time of use and the like. Alternatively or additionally, mobile device  112  may access either the broadcast network  114  or cellular network  116  to retrieve information from a server or content provider  122 . 
         [0020]    In accordance with one aspect of the invention, mobile device  112  may include a wireless interface configured to send and/or receive digital wireless communications within cellular network  116  using base transceiver station transmitter  120 . The information received by mobile device  112  through the cellular network  116  or broadcast network  114  via a cellular network tower  118  may include user input or selection (for example, in an interactive transmission), applications, services, electronic images, content requests, audio clips, video clips, and/or WTAI (Wireless Telephony Application Interface) messages. As part of cellular network  116 , one or more base stations (not shown) may support digital communications with receiver device  112  while the receiver device is located within the administrative domain of cellular network  116 . 
         [0021]    As shown in  FIG. 2 , mobile device  112  may include processor  128  connected to user interface  130 , memory  134  and/or other storage, and display  136 . Mobile device  112  may also include battery  150 , speaker  152  and antennas  154 . User interface  130  may further include a keypad, touch screen, voice interface, four arrow keys, joy-stick, stylus, data glove, mouse, roller ball, touch screen, or the like. In addition, user interface  130  may include the entirety of or portion of display  136 . Mobile device  112  may also include a camera  151  to capture image data. 
         [0022]    Computer executable instructions and data used by processor  128  and other components within mobile device  112  may be stored in a computer readable memory  134 . The memory may be implemented with any combination of read only memory modules or random access memory modules, optionally including both volatile and nonvolatile memory. Software  140  may be stored within memory  134  and/or storage to provide instructions to processor  128  for enabling mobile device  112  to perform various functions. Alternatively, some or all of the computer executable instructions may be embodied in hardware or firmware (not shown). 
         [0023]    Mobile device  112  may be configured to receive, decode and process digital broadband broadcast transmissions through various receivers such as DVB receiver  141 , FM/AM Radio receiver  142 , WLAN transceiver  143 , and telecommunications transceiver  144 . In one aspect of the invention, mobile device  112  may receive radio data stream (RDS) messages. 
         [0024]      FIG. 3  illustrates a flow diagram in accordance with an aspect of the invention. In  FIG. 3 , a contextual likelihood model  302  may be based on a more powerful off-line language model  304 . In an embodiment, the contextual likelihood model may used in mobile or portable devices. Such mobile or portable devices may have limited processing or storing capacity. The contextual likelihood model  302  may be utilized on-line for adjusting a static order listing of determined dictionary meanings for a selected word or phrase. The contextual likelihood model  302  may provide a scalable apparatus for language translation. In an embodiment, the contextual likelihood model  302  may be capable of determining an exact meaning of a word or phrase based on context-based computing. In another embodiment, if the contextual likelihood model  302  does not provide a valid output, the context may be taken as additive training material to be used by the off-line language model  304 . Such training material may be used by the off-line language model  304  to update the on-line contextual likelihood model  302 . 
         [0025]    In an aspect of the invention, contextual likelihood model  302  may consider translation on a semi-sentence level or phrase segment level rather than on a paragraph or a full sentence level. In an embodiment, contextual likelihood model  302  may use categories of context from which a suitable meaning of a target word or phrase segment may be determined. The contextual likelihood model  302  may also include pre-processing and/or filtering steps to improve computational run time. For instance,  FIG. 6  illustrates that input text  602  may be filtered and/or pre-processed, at step  604 , in accordance with an aspect of the invention. The results of the filtering/pre-processing, filtered/pre-processed text  606 , may be used as input text for translation. 
         [0026]    In an embodiment, different languages may involve different pre-processing steps. For instance, in the Chinese language there exists some auxiliary words such as           and function word like          , etc., which are not of use in translation and may not be interpreted. In an embodiment, these words may be filtered out and then the likelihood value may be determined. For example,           may be a phrase or word segment needing translation. In an embodiment, the word segment may be parsed as           In this embodiment,           may be an auxiliary word, which can be removed. In an aspect of the invention, the likelihood model may only consider the relationship of the main words, such as          (watch),          (game). 
         [0027]    In  FIG. 3 , an input such as an optical character recognition input  306  may be received in accordance with an aspect of the invention. The OCR input  306  may be based on information received from camera  151  ( FIG. 2 ). Camera  151  may capture images of foreign text contained on a sign  153  ( FIG. 2 ) or any written material for translation purposes. This corresponds generally to a step of receiving a signal containing an environmental cue. For example, a user may select a sign written in a foreign language that needs to be translated to determine proper meaning in the user&#39;s native language. In an aspect of the invention, the user may directly enter the words or phrases to be translated through use of an input device such as keyboard. In another aspect of the invention, a user may take a picture of the words or phrases to be translated and through OCR the foreign words and phrases may be processed for translation in a mobile device. 
         [0028]    In an aspect of the invention, a user may select a particular portion of a phrase or a target word for translation as illustrated in step  308 . In an embodiment, a dictionary  310  may be used to determine the meaning of the selected phrase or target word. A contextual likelihood model  302  may determine a likelihood and confidence  312  for each of the determined meanings of the selected phrase or target word in order to provide a context based adjustment to the determined meaning as illustrated at step  314 . The calculated likelihoods and confidences for each of the determined meaning may be used to rank each of the determined meanings. The ranked translations of the selected phrase or target word may be displayed to the user. In an embodiment, if only one meaning has been determined then that meaning may be presented to the user as shown in step  316 . In another embodiment, if more than one meaning is determined for the phrase or target word then a ranked list of those meanings may be displayed to the user as illustrated in step  318 . In yet another embodiment, the ranked translations may be displayed along with examples of the usage corresponding to the context. This may further assist the user in determining if a proper translation has been determined and displayed. 
         [0029]    In another aspect of the invention, the contextual likelihood model provides a scalable system in which an on-line contextual likelihood model may be used with mobile devices. In an embodiment, as shown in step  317  if a confidence or likelihood value can not be determined for the translation then the information may be used for off-line training that may be conducted through use of a language model  304 , the output of which may be used to update the on-line contextual likelihood model  302 . In an embodiment, the contextual likelihood model  302  may be updated while on-line. 
         [0030]    In another aspect of the invention, training in the context of source language may also be performed. Such training may be useful to determine the meaning of the word (to be translated) in source language. For example, to translate word B in the context A+B+C, it may be helpful to calculate the context of B in the source language at first to determine the meaning of B in the context—i.e., D (in the source language). This may provide a translation of B in the target language like T 1 , T 2 , or T 3 , and translation of D like T 4 , T 1 , or T 5 . This may make it more efficient to identify that T 1  is likely to be the exact meaning of B in context in the target language. 
         [0031]    In yet another aspect of the invention, training may occur in both the source and target languages. For instance it may be useful to consider the categories at first as the number of categories is normally much smaller than the number of meanings. For example, word A may have translations in the target language M 1 , M 2 , M 3 , M 4 , and M 5  (five meanings). However, these five meanings may be classified into two categories (C 1 , C 2 ). A category may refer to syntactical functions and morphological features, or some other features.           may be used as an example. The example includes three words: A, B, and C. A may be          (economy), C may be           (development). Both A and B may be nouns (noun category). According to linguistic rules or statistic model, it may be concluded that B may also be a noun rather than an adjective (adjective category). In addition,           may have multiple translations. In an embodiment, one translation may be “technology” (noun), and another may be “technical” (adjective). In an embodiment, “technology” may therefore be ranked before “technical”. 
         [0032]      FIG. 4  illustrates exemplary user interface displays for a method of context translation in accordance with an aspect of the invention. In  FIG. 4 , an input 1                       402  is received for translation. In an embodiment, input 1   402  may be parsed into phrase segments using a dictionary and a forward/backward matching algorithm. The possible meanings of the phrase segments may be determined. The contextual likelihood model  302  may compute the likelihood of each determined meaning. For instance, there may be three different meanings for input 1   402  whose confidences are higher than a predefined threshold. These meanings may be displayed to a user on a display screen such as display screen  403  and then subsequently reordered based on their determined contextual likelihoods as shown on display screen  404 . For instance,            405  which is part of input 1   402  may be selected by a user for contextual translation. In an embodiment,            405  may have three potential meanings as displayed to a user on user interface screen  403 . The three potential translation options for            405  may include: 1) financial, 2) economy, and 3) worthwhile. In an aspect of the invention, words or phrases which are adjacent to            405  may be searched and translated. For example,            408  and            410  may be discovered and their dictionary meaning determined. For instance,            408  may be translated to mean “Beijing” and            410  may be determined to mean “technology.” 
         [0033]    In an aspect of the invention, a three-gram contextual likelihood model may be utilized. The three-gram contextual likelihood model may compute the likelihoods of “Beijing financial”, “Beijing financial technology”, and “financial technology” separately as L 1 , L 2 , and L 3 , respectively. Next, the final likelihood of “financial” may be determined based on the computed likelihoods L 1 , L 2 , and L 3 . For example, in an embodiment, the final likelihood of “financial” may be determined by the equation: 
         [0000]        L=w 1* L 1+ w 2* L 2+ w 3 L 3 
         [0034]    Where w 1 , w 2 , w 3  are weights which may be based on a training corpus. 
         [0035]    Similarly, in an embodiment of the invention, the final likelihoods of “economy” and “worthwhile” may also be determined using the above equation. The determined final likelihoods of the three translation options may be ranked in decreasing order as shown in display screen  404 . 
         [0036]    In another aspect of the invention, other translation options may also be utilized. For example, in translation            410  other options for translation may be presented along with the technology meaning. Such translation options may include technical and technique. 
         [0037]    Furthermore, as illustrated in display screen  404  in some embodiments there may be additional options for the same item. For example, the translation of            405  may include two possible meanings such as economy and economic. In an embodiment, these two possible meanings may also be used while computing likelihoods. 
         [0038]    In another aspect of the invention, a second input 2             432  may be illustrative of various aspects of the invention. In  FIG. 4 ,            434  which is part of input 2   432  may be selected by a user for contextual translation. In an embodiment,            434  may have three potential meanings as displayed to a user on display screen  403 . The three potential translation options for            434  may include: 1) financial, 2) economy, and 3) worthwhile. In an aspect of the invention, words or phrases which are adjacent to            432  may be searched and translated. For example,            436  and            438  may be discovered and their dictionary meaning determined. For instance,            436  may be translated to mean “car” and            438  may be determined to mean “very,” respectively. The contextual likelihood model  302  may compute the likelihood of financial by computing “car very financial” and “very financial.” Similarly, the contextual likelihood model  302  may also determine the likelihoods of “economy” and “worthwhile.” In an embodiment, the final likelihood of these options may be ranked in decreasing order as shown in display screen  440 . 
         [0039]    In another aspect of the invention, a threshold level may be determined to remove options with a determined low likelihood or confidence. As those skilled in the art will realize, the threshold level may be altered and/or adjusted. In an embodiment, confidence may be calculated as a function of likelihood, together with other information such as rules and templates. For instance, in the above input 2   432  exemplary embodiment, the “worthwhile” definition may be the only definition whose calculated likelihood can pass an established threshold and is therefore selected as the accurate meaning in the context as shown in display screen  440 . 
         [0040]      FIG. 5  illustrates a method for interpreting a word or phrase using a contextual likelihood model. In  FIG. 5 , at step  502  a signal having an environmental cue may be received. Next, in step  504  the environmental cue may be parsed into word segments. In step  506 , a selection of a parsed word segment may be received for translation. Next, in step  508  at least one dictionary meaning may be determined. In step  510 , a likelihood of the at least one dictionary meaning may be calculated. Next, in step  512  a ranking of the at least one dictionary meaning based on the determined likelihood may be generated. Finally, in step  514  the generated ranking may be displayed for the determined dictionary meanings. 
         [0041]    The present invention has been described in terms of preferred and exemplary embodiments thereof. Numerous other embodiments, modifications and variations within the scope and spirit of the appended claims will occur to persons of ordinary skill in the art from a review of this disclosure.