Patent Publication Number: US-2023134103-A1

Title: Information processing apparatus and information processing method

Description:
TECHNICAL FIELD 
     The present disclosure relates to an information processing apparatus and an information processing method. 
     BACKGROUND ART 
     In the related art, an information processing apparatus that executes various types of information processing according to speech content of a user via an interactive voice user interface (UI) is known. In such an information processing apparatus, for example, an “intent” indicating the intention of a user and an “entity” serving as a parameter of an operation corresponding to the intent are estimated from the speech content of the user through a natural language understanding (NLU) process, and information processing is executed on the basis of the estimation result. 
     Note that, if the speech content of the user includes an unknown phrase (hereinafter, referred to as an “unknown word”), it is not possible to estimate the intent or the entity. Thus, in the development/design process of such an information processing apparatus, learning work of associating a linguistic phrase with a real target, such as entity registration of NLU and addition of tag information to an image, map coordinates, or the like, is manually performed, for example. 
     However, as a matter of course, there are a large number of linguistic phrases, and the linguistic phrases always change over time. Therefore, in the manual learning work as described above, enormous cost is required, and there is a limit to following a change in a phrase. 
     Therefore, there has been proposed an information processing apparatus that has a learning mode for learning an unknown word on the basis of speech content of a user and an execution mode for executing various types of information processing corresponding to the learned unknown word, and improves learning efficiency by causing the user himself/herself to perform learning work (refer to, for example, Patent Document 1). 
     CITATION LIST 
     Patent Document 
     
         
         Patent Document 1: International Publication No. WO 2009/028647 
       
    
     SUMMARY OF THE INVENTION 
     Problems to be Solved by the Invention 
     However, the above-described related art has room for further improvement in efficiently associating an unknown word with a real target without imposing a load on a user. 
     Specifically, in a case where the above-described related art is used, the user needs to explicitly switch between the learning mode and the execution mode to learn or execute the speech. Thus, the load is high for the user, and the learning efficiency is also low. 
     Therefore, the present disclosure proposes an information processing apparatus and an information processing method capable of efficiently associating an unknown word with a real target without imposing a load on a user. 
     Solutions to Problems 
     According to the present disclosure, there is provided an information processing apparatus including a first detection unit that detects an unknown word that is an unknown phrase from text input in a natural language; a second detection unit that detects occurrence of an event related to a known phrase included in the text; and an association unit that associates, with the unknown word, each of an observation context indicating a situation at the time of detection of the unknown word as a condition context and an observation context indicating a situation at the time of the occurrence of the event as a target context. 
     Furthermore, according to the present disclosure, there is provided an information processing apparatus including a first detection unit that detects an unknown word that is an unknown phrase from text input in a natural language; a second detection unit that detects occurrence of an event related to a known phrase included in the text; an association unit that associates, with the unknown word, each of an observation context indicating a situation at the time of detection of the unknown word as a condition context and an observation context indicating a situation at the time of the occurrence of the event as a target context; and an instruction unit that, in a case where the known phrase is included in new text and the condition context associated with the unknown word is observed, gives an instruction for generating a response using the unknown word. 
     Furthermore, according to the present disclosure, there is provided an information processing method including detecting an unknown word that is an unknown phrase from text input in a natural language; detecting occurrence of an event related to a known phrase included in the text; and associating, with the unknown word, each of an observation context indicating a situation at the time of detection of the unknown word as a condition context and an observation context indicating a situation at the time of the occurrence of the event as a target context. 
     Furthermore, according to the present disclosure, there is provided an information processing method including detecting an unknown word that is an unknown phrase from text input in a natural language; detecting occurrence of an event related to a known phrase included in the text; associating, with the unknown word, each of an observation context indicating a situation at the time of detection of the unknown word as a condition context and an observation context indicating a situation at the time of the occurrence of the event as a target context; and in a case where the known phrase is included in new text and the condition context associated with the unknown word is observed, giving an instruction for generating a response using the unknown word. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a schematic explanatory diagram (part 1) of an information processing method according to an embodiment of the present disclosure. 
         FIG.  2    is a schematic explanatory diagram (part 2) of the information processing method according to the embodiment of the present disclosure. 
         FIG.  3    is an explanatory diagram of terms. 
         FIG.  4    is a diagram illustrating a configuration example of an information processing system according to an embodiment of the present disclosure. 
         FIG.  5    is a block diagram illustrating a configuration example of an information processing apparatus according to an embodiment of the present disclosure. 
         FIG.  6    is a block diagram illustrating a configuration example of a sensor unit. 
         FIG.  7    is a block diagram illustrating a configuration example of an execution interaction control unit. 
         FIG.  8    is an explanatory diagram of unknown word information. 
         FIG.  9    is a processing explanatory diagram (part 1) in a case of human-directed speech. 
         FIG.  10    is a processing explanatory diagram (part 2) in the case of human-directed speech. 
         FIG.  11    is a processing explanatory diagram (part 3) in the case of human-directed speech. 
         FIG.  12    is a processing explanatory diagram (part 4) in the case of human-directed speech. 
         FIG.  13    is a processing explanatory diagram (part 5) in the case of human-directed speech. 
         FIG.  14    is a processing explanatory diagram (part 6) in the case of human-directed speech. 
         FIG.  15    is a processing explanatory diagram (part 7) in the case of human-directed speech. 
         FIG.  16    is a processing explanatory diagram (part 8) in the case of human-directed speech. 
         FIG.  17    is a flowchart illustrating a processing procedure in the case of human-directed speech. 
         FIG.  18    is an explanatory diagram of unknown word information according to a modification example. 
         FIG.  19    is a processing explanatory diagram (part 1) in a case of system-directed speech. 
         FIG.  20    is a processing explanatory diagram (part 2) in the case of system-directed speech. 
         FIG.  21    is a processing explanatory diagram (part 3) in the case of system-directed speech. 
         FIG.  22    is a flowchart illustrating a processing procedure in the case of system-directed speech. 
         FIG.  23    is a block diagram illustrating a configuration example of a server apparatus according to an embodiment of the present disclosure. 
         FIG.  24    is an explanatory diagram of a determination process executed by a determination unit. 
         FIG.  25    is a diagram (part 1) illustrating an interaction processing example of storing or discarding association. 
         FIG.  26    is a diagram (part 2) illustrating the interaction processing example of storing or discarding association. 
         FIG.  27    is a diagram (part 1) illustrating an application example of automatic update using an area of an image recognizer. 
         FIG.  28    is a diagram (part 2) illustrating the application example of automatic update using an area of the image recognizer. 
         FIG.  29    is a hardware configuration diagram illustrating an example of a computer that realizes functions of an information processing apparatus. 
     
    
    
     MODE FOR CARRYING OUT THE INVENTION 
     Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. Note that, in each of the following embodiments, the same parts are denoted by the same reference numerals, and redundant description will be omitted. 
     Furthermore, in the present specification and the drawings, a plurality of constituents having substantially the same functional configuration may be distinguished by attaching different hyphenated numerals after the same reference numerals. For example, a plurality of configurations having substantially the same functional configuration is distinguished as an information processing apparatus  10 - 1  and an information processing apparatus  10 - 2  as necessary. However, in a case where it is not particularly necessary to distinguish each of a plurality of constituents having substantially the same functional configuration, only the same reference numeral is attached. For example, in a case where it is not necessary to particularly distinguish the information processing apparatus  10 - 1  and the information processing apparatus  10 - 2 , they will be simply referred to as an information processing apparatus  10 . 
     Furthermore, the present disclosure will be described according to the following item order. 
     1. Outline 
     1-1. Problems in comparative example of present embodiment 
     1-2. Outline of present embodiment 
     2. Configuration of information processing system 
     2-1. Overall configuration 
     2-2. Configuration of information processing apparatus 
     2-3. Configuration of execution interaction control unit 
     2-4. Specific example of processing details (in case of human-directed speech) 
     2-5. Specific example of processing details (in case of system-directed speech) 
     2-6. Configuration of server apparatus 
     2-7. Application example of automatic update using area of image recognizer 
     3. Modification examples 
     3-1. Modification example in case of human-directed speech 
     3-2. Modification example in case of system-directed speech 
     3-3. Other modification examples 
     4. Hardware Configuration 
     5. Conclusion 
     1. Outline 
       FIG.  1    is a schematic explanatory diagram (part 1) of an information processing method according to an embodiment of the present disclosure. Furthermore,  FIG.  2    is a schematic explanatory diagram (part 2) of the information processing method according to the embodiment of the present disclosure. Note that  FIG.  1    illustrates a problem in a comparative example of the embodiment of the present disclosure. 
     1-1. Problems in Comparative Example of Present Embodiment 
     As illustrated in  FIG.  1   , an information processing system  1 ′ according to a comparative example includes one or more information processing apparatuses  10 ′ and a server apparatus  100 ′. The information processing apparatus  10 ′ is an apparatus used by an individual user, and executes various types of information processing according to speech content of the user via a voice UI. 
     Note that the information processing apparatus  10 ′ is a desktop personal computer (PC), a notebook PC, a tablet terminal, a mobile phone, a personal digital assistant (PDA), or the like. Furthermore, the information processing apparatus  10 ′ is, for example, a wearable terminal worn by the user, or an in-vehicle apparatus such as a navigation apparatus or a drive recorder mounted in a vehicle. 
     The server apparatus  100 ′ is configured as, for example, a cloud server, generates and updates a recognition model used for an NLU process or the like, and distributes the recognition model to the information processing apparatus  10 ′. As illustrated in  FIG.  1   , the recognition model includes, for example, learning information in which a linguistic phrase and a real target are associated with each other. 
     Incidentally, learning work of associating such a linguistic phrase with a real target is manually performed, for example, in a development/design process, an operation process, or the like of the information processing system  1 ′. However, as a matter of course, there are a large number of linguistic phrases, and the linguistic phrases always change over time. 
     Therefore, in the information processing system  1 ′, it can be said that it is necessary to always associate a new unknown word with a real target. In the manual learning work as described above, enormous cost is required, and there is a limit to following a change in a phrase. 
     Note that there is also the information processing apparatus  10 ′ that has a learning mode for learning an unknown word on the basis of speech content of a user and an execution mode for executing various types of information processing corresponding to the learned unknown word, and can cause the user himself/herself to perform learning work. However, in a case where such an apparatus is used, the user needs to explicitly switch between the learning mode and the execution mode to learn or execute speech, and the load on the user is high and the learning efficiency is low. 
     1-2. Outline of Present Embodiment 
     Therefore, in the information processing method according to the embodiment of the present disclosure, an unknown word that is an unknown phrase is detected from text input in a natural language, the occurrence of an event related to a known phrase included in the text is detected, and the unknown word is associated with each of an observation context indicating a situation at the time of detection of the unknown word as a condition context and an observation context indicating a situation at the time of occurrence of the above event as a target context. 
     Specifically, as illustrated in  FIG.  2   , in the information processing method according to the embodiment, an unknown word is dynamically associated with a real target in the actual use process of the voice UI (step S 1 ).  FIG.  2    illustrates a case where the information processing apparatus  10  is an information processing apparatus  10 - 1  corresponding to a navigation apparatus mounted in a taxi, for example. 
     In such a case, in a case where an unknown word estimated as an entity is detected from a conversation between a passenger and a driver of the taxi, the information processing apparatus  10  stores the speech intent in speech including the unknown word, and stores an observation context at the time of detection of the unknown word as a condition context. Here, the observation context is recognition information for recognizing a user and a situation in which the user is placed, and is, for example, sensing data from various sensing devices mounted in the taxi. 
     Then, in a case where the stored speech intent is executed, the information processing apparatus  10  associates the observation context at the time of execution with the unknown word as a target context corresponding to the real target of the unknown word. 
     As an example, in a case where the passenger says “turn right at the yellow signboard” and the phrase “yellow signboard” is detected as an unknown word, the information processing apparatus  10  stores “turn right” as the speech intent and stores an observation context at the time of detection of the phrase “yellow signboard” as a condition context. The condition context here is, for example, a current location indicated by a Global Positioning System (GPS) position when the phrase “yellow signboard” is detected. 
     Then, in a case where the information processing apparatus  10  detects that the taxi actually “turns right” from the observation context or the user&#39;s speech, the information processing apparatus associates the observation context at the time of detection with the phrase “yellow signboard” as a target context corresponding to the real target of the phrase “yellow signboard”. The target context here is, for example, the current location indicated by a GPS position at the time of execution of “turn right”. 
     Therefore, the information processing apparatus  10  can dynamically acquire the real target of the phrase “yellow signboard”. 
     Note that, for the unknown word associated with the target context, in a case where the unknown word is included in the user&#39;s speech or the like thereafter, if the above-described situation in which the speech intent and the condition context match is encountered, the target context associated with the unknown word is interpreted as a real target, and information processing corresponding thereto is executed. 
     For example, in the example of the “yellow signboard” described above, it is assumed that the same taxi is traveling along a route of “turning right” at an intersection with the “yellow signboard” at another opportunity after association. In such a case, if the taxi has reached the GPS position at the time of detecting the phrase “yellow signboard” on the way to the intersection, the information processing apparatus  10  performs navigation guidance such as “turn right at the yellow signboard” instead of “turn right 100 m ahead”. 
     Details of a learning example and an application example of an unknown word based on a conversation between users in a taxi, that is, speech directed to a person will be described later with reference to  FIGS.  3  to  18   . Furthermore, in the information processing method according to the embodiment, learning and application of an unknown word can be performed not only on the basis of speech directed to a person but also on the basis of speech directed to a system. Details thereof will be described later with reference to  FIGS.  19  to  22    as a modification example of speech directed to a system. 
     Then, in the information processing method according to the embodiment, the server apparatus  100  collects an association result executed in step S 1  and executes statistical processing (step S 2 ). Then, the server apparatus  100  applies the association result to the other information processing apparatus  10  according to the statistical result (step S 3 ). 
     For example, in the above-described example of the “yellow signboard”, upon detecting that the phrase is used (highly related) a predetermined number of times or more in the same condition context and target context within a certain period in the past, the server apparatus  100  distributes the association result of the “yellow signboard” to the entire system. Note that, in this case, the server apparatus  100  can also distribute a phrase tag for a position such as the “yellow signboard” to a map vendor or the like, for example. 
     Furthermore, if the entity of the “yellow signboard” is removed and is no longer there, the phrase “yellow signboard” is not said, and thus the number of pieces of association data of the “yellow signboard” is statistically reduced and is not distributed to the entire system. 
     Details of steps S 2  and S 3  executed by the server apparatus  100  will be described later with reference to  FIGS.  23  to  28   . 
     As described above, in the information processing method according to the embodiment, an unknown word that is an unknown phrase is detected from text input in a natural language, the occurrence of an event related to the known phrase included in the text is detected, and an observation context indicating a situation at the time of the detection of the unknown word is associated with the unknown word as a condition context, and the observation context indicating the situation at the time of the occurrence of the event is associated with the unknown word as a target context. 
     Therefore, according to the information processing method according to the embodiment, association between a phrase and a real target is automatically accumulated as a user uses the system via the voice UI, and thus it is possible to execute interpretation of speech following the change in a language that cannot be followed manually or information processing In other words, since a corresponding vocabulary of the voice UI system is updated by automatically following the use trend of the user&#39;s actual language phrase instead of a specification due to a developer&#39;s product-out, the convenience of the voice UI is enhanced. 
     That is, according to the information processing method according to the embodiment, it is possible to efficiently associate an unknown word with a real target without imposing a load on a user. 
     Hereinafter, a configuration example of the information processing system  1  to which the information processing method according to the above-described embodiment is applied will be described more specifically. 
     Note that, in the following description, a case where an unknown word is an entity that is a target/attribute of the speech intent “turn right”, such as the phrase “yellow signboard”, will be described as a main example, but the intent may be an unknown word. Such an example will be described later with reference to  FIG.  18   . 
     Furthermore, here, terms and the like used in the following description will be described.  FIG.  3    is an explanatory diagram of terms. 
     As illustrated in  FIG.  3   , it is assumed that the user has said “turn right at the yellow signboard”. In such a case, in the present embodiment, “turn” is extracted as the “intent” indicating the intention of the user through the NLU process. Furthermore, “right” and “yellow signboard” are extracted as “entities” serving as target/attribute of the intent, in other words, parameters of an operation corresponding to the intent. 
     Note that “right” can be estimated to be a parameter indicating a direction through the NLU process. Furthermore, “yellow signboard” can be estimated to be a parameter indicating a place (Place) through the NLU process, but is unknown as a phrase, for example. In such a case, in the following description, a portion corresponding to “turn” and “right”, that is, “turn right” will be referred to as “speech intent I u ”. That is, the speech intent I u  is a known portion of the user&#39;s speech that includes the intent. In contrast, a portion corresponding to the “yellow signboard” will be referred to as an “unknown word entity P u ”. 
     With respect to the intent estimated from the speech text of the user in the NLU process, the unknown word entity P u  refers to a phrase in a case where a phrase having an entity serving as a target/attribute of the intent does not exist in a dictionary registered in the NLU, or in a case where the phrase is registered in the dictionary of the NLU as a phrase but there is no phrase associated with the phrase in a real target that can be handled as a target/attribute of the intent in execution interaction control or there is a plurality of phrases, and thus the real target cannot be uniquely specified. In other words, the unknown word is a phrase that does not exist in dictionary information used in the NLU process for the speech text of the user, or a phrase that exists in the dictionary information but cannot uniquely specify a real target corresponding to the phrase in information processing based on the above text. 
     Furthermore, although not illustrated in the drawing, the above-described observation context will be hereinafter referred to as an “observation context C o ”. Similarly, the condition context will be hereinafter referred to as a “condition context C r ”. Furthermore, similarly, the target context will be hereinafter referred to as a “target context C t ”. 
     2. Configuration of Information Processing System 
     2-1. Overall Configuration 
       FIG.  4    is a diagram illustrating a configuration example of the information processing system  1  according to the embodiment of the present disclosure. As illustrated in  FIG.  4   , the information processing system  1  includes one or more information processing apparatuses  10  and a server apparatus  100 . Furthermore, as illustrated in  FIG.  4   , the information processing apparatus  10  and the server apparatus  100  are connected to each other via a network N such as the Internet or a mobile telephone network, and transmit and receive data to and from each other via the network N. 
     Similarly to the information processing apparatus  10 ′ described above, the information processing apparatus  10  is an apparatus used by each user, and executes various types of information processing according to speech content of the user via the voice UI. The information processing apparatus  10  is a desktop PC, a notebook PC, a tablet terminal, a mobile phone, a PDA, or the like. Furthermore, the information processing apparatus  10  is, for example, a wearable terminal worn by the user, or an in-vehicle apparatus such as a navigation apparatus or a drive recorder mounted in a vehicle. 
     In a case where the unknown word entity P u  is detected, each information processing apparatus  10  associates the observation context C o  at the time of the detection with the unknown word entity P u  as the condition context C r . Furthermore, in a case where execution of the speech intent I u  is detected, the information processing apparatus  10  associates the observation context C o  at the time of the detection with the unknown word entity P u  as the target context C t . Then, the information processing apparatus  10  transmits unknown word information that is the association result to the server apparatus  100 . 
     The server apparatus  100  is configured as, for example, a cloud server, and collects the unknown word information transmitted from each information processing apparatus  10 . Furthermore, the server apparatus  100  manages the collected unknown word information as big data and executes statistical processing on the unknown word information. Furthermore, the server apparatus  100  applies the unknown word information to the entire system according to a statistical result of the statistical processing. Note that a specific configuration example of the server apparatus  100  will be described later with reference to  FIGS.  23  to  28   . 
     Next,  FIG.  5    is a block diagram illustrating a configuration example of the information processing apparatus  10 . Furthermore,  FIG.  6    is a block diagram illustrating a configuration example of a sensor unit  3 . Furthermore,  FIG.  7    is a block diagram illustrating a configuration example of an execution interaction control unit  13   d.    
     Note that, in  FIGS.  5  to  7    (and  FIG.  23    that will be described later), only constituents necessary for describing features of the embodiment are illustrated, and descriptions of general constituents are omitted. 
     In other words, each constituent illustrated in  FIGS.  5  to  7    (and  FIG.  23   ) is functionally conceptual, and does not necessarily have to be physically configured as illustrated. For example, a specific form of distribution and integration of each block is not limited to the illustrated form, and all or some thereof can be functionally or physically distributed and integrated in any unit according to various loads, usage conditions, and the like. 
     Furthermore, in the description using  FIGS.  5  to  7    (and  FIG.  23   ), the description of the already described constituents may be simplified or omitted. 
     2-2. Configuration of Information Processing Apparatus 
     As illustrated in  FIG.  5   , the information processing apparatus  10  is connected to a voice input unit  2 , a sensor unit  3 , a display unit  4 , and a voice output unit  5 . The voice input unit  2  is realized by a voice input device such as a microphone. 
     The sensor unit  3  includes various sensors for recognizing a user and a situation in which the user is placed. As illustrated in  FIG.  6   , the sensor unit  3  includes, for example, a camera  3   a , a GPS sensor  3   b , an acceleration sensor  3   c , a biological information sensor  3   d , and a line-of-sight detection sensor  3   e.    
     The camera  3   a  uses, for example, a complementary metal oxide semiconductor (CMOS) image sensor, a charge coupled device (CCD) image sensor, or the like as an imaging element to capture an image for recognizing the user and the situation in which the user is placed. For example, the camera  3   a  is an in-vehicle camera provided to be able to image the inside and outside of a taxi. 
     The GPS sensor  3   b  is a GPS receiver, and detects a GPS position on the basis of a received GPS signal. The acceleration sensor  3   c  detects acceleration in each direction. As the acceleration sensor  3   c , for example, a triaxial acceleration sensor such as a piezoresistive type sensor or a capacitance type sensor may be used. 
     The biological information sensor  3   d  detects biological information of the user such as a pulse, respiration, and a body temperature of the user. The line-of-sight detection sensor  3   e  detects a line of sight of the user. Note that the configuration of the sensor unit  3  illustrated in  FIG.  6    is merely an example, and various sensors other than those illustrated in  FIG.  6    may be included as long as the sensors are used for recognizing a user and a situation in which the user is placed. 
     The sensor unit  3  inputs sensing data by these various sensor groups to the information processing apparatus  10  as the observation context C o  described above. 
     The description returns to  FIG.  5   . The display unit  4  is realized by an image output device such as a display. The voice output unit  5  is realized by a voice output device such as a speaker. 
     The information processing apparatus  10  includes a communication unit  11 , a storage unit  12 , and a control unit  13 . The communication unit  11  is realized by, for example, a network interface card (NIC) or the like. The communication unit  11  is connected to the server apparatus  100  in a wireless or wired manner via the network N, and transmits and receives information to and from the server apparatus  100 . 
     The storage unit  12  is realized by, for example, a semiconductor memory element such as a random access memory (RAM), a read only memory (ROM), or a flash memory, or a storage device such as a hard disk or an optical disc. In the example illustrated in  FIG.  5   , the storage unit  12  stores a recognition model  12   a  and unknown word information  12   b.    
     The recognition model  12   a  is a model group for voice recognition in an automatic speech recognition (ASR) process that will be described later, semantic understanding in an NLU process, interaction recognition in an execution interaction control process, and the like, and is generated by the server apparatus  100  as a learning model group using a machine learning algorithm such as deep learning or the like. 
     The unknown word information  12   b  will be described with reference to  FIG.  8   .  FIG.  8    is an explanatory diagram of the unknown word information  12   b . As illustrated in  FIG.  8   , the unknown word information  12   b  is information in which the speech intent I u , the condition context C r , and the target context C t  are associated with the unknown word entity P u . 
     As illustrated in the figure, the condition context C r  corresponds to the observation context C o  at the time of detection of the unknown word entity P u . Furthermore, the target context C t  corresponds to the observation context C o  at the time of execution of the speech intent I u . 
     The unknown word information  12   b  is registered for each unknown word entity P u  by the execution interaction control unit  13   d  that will be described later. 
     The description returns to  FIG.  5   . The control unit  13  is a controller, and is realized by, for example, a central processing unit (CPU), a micro processing unit (MPU), or the like executing various programs stored in the storage unit  12  by using a RAM as a work area. Furthermore, the control unit  13  may be realized by, for example, an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA). 
     The control unit  13  includes a voice recognition unit  13   a , a semantic understanding unit  13   b , a context recognition unit  13   c , an execution interaction control unit  13   d , a response generation unit  13   e , an output control unit  13   f , and a transmission unit  13   g , and realizes or executes a function or an action of information processing described below. 
     The voice recognition unit  13   a  performs the ASR process on voice data input from the voice input unit  2 , and converts the voice data into text data. Furthermore, the voice recognition unit  13   a  outputs the converted text data to the semantic understanding unit  13   b.    
     The semantic understanding unit  13   b  performs a semantic understanding process such as an NLU process on the text data converted by the voice recognition unit  13   a , estimates an intent and an entity (including an unknown word), and outputs an estimation result to the execution interaction control unit  13   d.    
     The context recognition unit  13   c  acquires the sensing data from the sensor unit  3 , and outputs the sensing data as the observation context C o  to the execution interaction control unit  13   d.    
     2-3. Configuration of Execution Interaction Control Unit 
     In a case where an entity of an unknown word is included in the estimation result from the semantic understanding unit  13   b , the execution interaction control unit  13   d  extracts the entity as the unknown word entity P u . Furthermore, the execution interaction control unit  13   d  associates the condition context C r  and the target context C t  with the unknown word entity P u  on the basis of the observation context C o  input from the context recognition unit  13   c , and generates the unknown word information  12   b.    
     A configuration example of the execution interaction control unit  13   d  will be described more specifically. As illustrated in  FIG.  7   , the execution interaction control unit  13   d  includes a detection of the unknown word detection unit  13   da , a registration unit  13   db , an execution detection unit  13   dc , an association unit  13   dd , and an instruction unit  13   de.    
     The detection of the unknown word detection unit  13   da  detects an unknown word from the intent and the entity (including the unknown word) estimated by semantic understanding unit  13   b . In a case where the detection of the unknown word detection unit  13   da  detects the entity of the unknown word, the registration unit  13   db  registers the entity as the unknown word entity P u  in the unknown word information  12   b . At the same time, the registration unit  13   db  registers the speech intent I u  of the phrase including the unknown word entity P u  in the unknown word information  12   b  in association with the unknown word entity P u . 
     Furthermore, the registration unit  13   db  registers the observation context C o  input from the context recognition unit  13   c  at the time of detection of such an unknown word in the unknown word information  12   b  in association with the unknown word entity P u  as the condition context C r . 
     The execution detection unit  13   dc  detects execution of the speech intent I u  registered in the unknown word information  12   b  on the basis of the observation context C o  input from the context recognition unit  13   c  or the intent and the entity input from the semantic understanding unit  13   b.    
     In a case where the execution detection unit  13   dc  detects the execution of the speech intent I u , the association unit  13   dd  associates the observation context C o  input from the context recognition unit  13   c  at the time of detection of the execution with the unknown word entity P u  of the unknown word information  12   b  as the target context C t . 
     In a case where the intent/entity (including the associated unknown word) input from the semantic understanding unit  13   b  and the observation context C o  input from the context recognition unit  13   c  match the speech intent I u  and the condition context C r  of the unknown word information  12   b , the instruction unit  13   de  instructs the response generation unit  13   e  to generate a response using the unknown word entity P u  associated with the speech intent I u  and the condition context C r . 
     The description returns to  FIG.  5   . The response generation unit  13   e  generates image information and voice information to be presented to the user in response to an instruction from the execution interaction control unit  13   d.    
     The output control unit  13   f  presents the image information generated by the response generation unit  13   e  to the user via the display unit  4 . Furthermore, the output control unit  13   f  performs a voice synthesis process on the voice information generated by the response generation unit  13   e  and presents the voice information to the user via the voice output unit  5 . 
     The transmission unit  13   g  appropriately transmits the unknown word information  12   b  to the server apparatus  100  via the communication unit  11 . Note that the term “appropriately” as used herein may be any time or may be periodic. Furthermore, the term “appropriately” may be every time the unknown word information  12   b  is updated. 
     2-4. Specific Example of Processing Details (Case of Human-Directed Speech 
     Next, the details of the processes described so far will be described more specifically by taking a conversation scene between the passenger and the driver of the taxi illustrated in  FIG.  2    as an example.  FIGS.  9  to  16    are processing explanatory diagrams (part 1) to (part 8) in a case of human-directed speech. 
     As illustrated in  FIG.  9   , it is assumed that the passenger says “turn right at the yellow signboard” (refer to  FIG.  3   ). Then, the information processing apparatus  10  detects an unknown word (step S 11 ), registers the unknown word entity P u  “yellow signboard” in the unknown word information  12   b , and registers the speech intent I u  “turn right” in association with the unknown word entity P u  “yellow signboard”. 
     Furthermore, the information processing apparatus  10  stores the observation context C o  at the time of detection of the unknown word as the condition context C r  in association with the unknown word entity P u  “yellow signboard” (step S 12 ). In the example in  FIG.  9   , the information processing apparatus  10  stores a GPS position (that is, the current location at the time of detection of the unknown word) indicated by an own vehicle position mark in the figure as the condition context C r . 
     Then, the information processing apparatus  10  detects execution of the speech intent I u  registered in the unknown word information  12   b  on the basis of the observation context C o  or the speech (step S 13 ). Note that, here, an example is illustrated in which execution of the speech intent I u  is detected from the driver&#39;s speech of “turning right”. 
     Then, the information processing apparatus  10  associates the observation context C o  at the time of detection of execution of the speech intent I u  with the unknown word entity P u  “yellow signboard” as the target context C t  (step S 14 ). In the example in  FIG.  9   , the information processing apparatus  10  associates a GPS position (that is, the current location at the time of detection of execution) indicated by a pin mark indicating an intersection position in the figure as the target context C t . 
     Then, after the unknown word information  12   b  related to such an unknown word entity P u  “yellow signboard” is generated, as illustrated in  FIG.  10   , if the speech intent I u  and the condition context C r  match (step S 15 ), the information processing apparatus  10  interprets the target context C t  as a real target and executes information processing corresponding thereto. 
     That is, in a case where the taxi is traveling along a route of “turning right” at the intersection where the “yellow signboard” is present, if the taxi has reached the GPS position indicated by the condition context C r  on the way to the intersection, the information processing apparatus  10  performs navigation guidance such as “turn right at the yellow signboard” as illustrated in the figure. 
     Note that, in this case, if the speech intent I u  is simply associated with the unknown word entity P u  “yellow signboard” as “turn”, the information processing apparatus  10  can perform navigation guidance of “turn left on the yellow signboard” in a case of turning left at the same intersection. 
     Furthermore, as another example, for example, when there is a place that the driver does not want to pass while driving in route search, and the like, by speaking “pass by the yellow signboard”, the GPS position indicated by the target context C t  of the “yellow signboard” can be designated as the waypoint of route search. 
     Note that, in  FIGS.  9  and  10   , the case where the condition context C r  is the GPS position at the time of detection of the unknown word has been described as an example. However, for example, as illustrated in  FIG.  11   , the condition context C r  may be within a predetermined range (Place) including the GPS position at the time of detection of the unknown word. 
     Furthermore, in a case where an attribute regarding a color of the unknown word entity P u  is extracted through the NLU process, such as “yellow” of “yellow signboard”, for example, it is predicted that the appearance of the color of the signboard changes depending on a time zone. Therefore, in such a case, as illustrated in the same figure, the condition context C r  may include, for example, a predetermined time zone (TimeZone) including the current time at the time of detection of the unknown word. Note that, in a case where a plurality of condition contexts C r  is associated with the unknown word entity P u , the information processing apparatus  10  determines the condition contexts C r  as an AND condition. 
     Furthermore, an attribute is not limited to the attribute regarding a color, and since the “signboard” of the “yellow signboard” usually has a flat display surface, as illustrated in  FIG.  12   , an attribute “directivity present” may be extracted through the NLU process. 
     In such a case, as illustrated in the same figure, the condition context C r  may include, in addition to within a predetermined range (Place) including the GPS position at the time of detection of the unknown word, for example, an advancing direction range (AngleRange) within a predetermined angle θ from the advancing direction at the time of detection of the unknown word. 
     In the case of  FIG.  12   , when the unknown word information  12   b  is applied, the phrase “yellow signboard” is used for a navigation speech, speech interpretation of the user, and the like only in within the predetermined range of (Place) and the advancing direction range of (AngleRange) illustrated in the same figure. That is, since a directional signboard is not seen from an advancing direction not matching the condition context C r , the phrase “yellow signboard” is not used in such a case. 
     In contrast, as illustrated in  FIG.  13   , the unknown word entity P u  is assumed to be a “brown chimney”. In such a case, with respect to the “chimney” of the “brown chimney”, an attribute “directivity absent” may be extracted through the NLU process. 
     In such a case, as illustrated in the same figure, the condition context C r  does not include the advancing direction range (AngleRange) within the predetermined angle θ from the advancing direction at the time of detection of the unknown word, unlike the case of “directivity present”. 
     That is, since the chimney is visible from any advancing direction and has no directivity, an advancing direction range is not limited. In the case of  FIG.  13   , when the unknown word information  12   b  is applied, the phrase “brown chimney” is used for a navigation speech, the speech interpretation of the user, and the like regardless of an advancing direction range as long as it is within a predetermined range of (Place) illustrated in the same figure. 
     Incidentally, an example in which the GPS position detected by the GPS sensor  3   b  is used as the condition context C r  and the target context C t  has been described above, but the present disclosure is not limited thereto. For example, an image captured by the camera  3   a  such as a drive recorder may be used as the condition context C r  and the target context C t . An example of such a case is illustrated in  FIGS.  14  to  16   . Note that, in  FIGS.  14  and  16   , a rectangular portion filled with a dotted pattern represents a “yellow signboard”. 
     Similarly to the case already illustrated in  FIG.  9   , it is assumed that an unknown word has been detected on the basis of the speech of the passenger “turn right at the yellow signboard” as illustrated in  FIG.  14    (step S 21 ). Then, it is assumed that the unknown word entity P u  “yellow signboard” is registered in the unknown word information  12   b , and the speech intent I u  “turn right” is registered in association with the unknown word entity P u  “yellow signboard”. 
     Then, in the case of the example in  FIG.  14   , the information processing apparatus  10  associates the captured image from the camera  3   a  at the time of the detection of the unknown word in step S 21  with the unknown word entity P u  “yellow signboard”, and stores the image as the condition context C r  (step S 22 ). 
     Then, the information processing apparatus  10  detects execution of the speech intent I u  registered in the unknown word information  12   b  on the basis of the observation context C o  or the speech (step S 23 ). 
     Then, in the case of the example in  FIG.  14   , the information processing apparatus  10  associates the captured image from the camera  3   a  at the time of detection of execution of the speech intent I u  in step S 23  with the unknown word entity P u  “yellow signboard” as the target context C t  (step S 24 ). 
     Then, after the unknown word information  12   b  regarding such an unknown word entity P u  “yellow signboard” is generated, as illustrated in  FIG.  15   , if the speech intent I u  and the condition context C r  match (step S 25 ), the information processing apparatus  10  interprets the target context C t  as a real target and executes information processing corresponding thereto. 
     In other words, in a case where the taxi is traveling along the route of “turning right” at the intersection where the “yellow signboard” is present, if the information processing apparatus  10  has recognized, from the captured image from the camera  3   a , a landscape corresponding to the captured image indicated by the condition context C r  on the way to the intersection by the taxi, the information processing apparatus  10  performs navigation guidance of “turn right at the yellow signboard”, for example, as illustrated in the same figure. 
     Then, in this case, the information processing apparatus  10  superimposes and displays an image of the target context C t  and an arrow on an image of the condition context C r , for example, as illustrated in  FIG.  16   . The display at this time may be projected on, for example, a windshield or the like such that the driver can visually recognize the display while driving. Therefore, the route guidance for the driver can be easily performed not only by voice but also visually. 
     Note that the display example illustrated in  FIG.  16    is merely an example, and the display form is not limited. For example, the arrow may not necessarily be displayed. Furthermore, the condition context C r  or the target context C t  is not limited to a captured image from the camera  3   a  as long as the context can be visually recognized by a user, and may be, for example, an image or the like displayed in a display region of a navigation screen. 
     Furthermore, in a case where a landscape corresponding to the captured image indicated by the condition context C r  is subjected to image recognition from the captured image from the camera  3   a , the information processing apparatus  10  does not necessarily interpret, for example, a color of “yellow signboard”. Therefore, there is an advantage that a processing load can be reduced. Note that, of course, a color may also be interpreted. 
     Next, a processing procedure in a case of human-directed speech executed by the information processing apparatus  10  according to the embodiment will be described with reference to  FIG.  17   .  FIG.  17    is a flowchart illustrating a processing procedure in the case of human-directed speech. 
     As shown in  FIG.  17   , first, the detection of the unknown word detection unit  13   da  detects the unknown word entity P u  in a conversation between people (step S 101 ). Then, the registration unit  13   db  stores the speech intent I u  of the speech including the unknown word entity P u  in the unknown word information  12   b  together with the unknown word entity P u  (step S 102 ). 
     Furthermore, the registration unit  13   db  stores the observation context C o  at the time of detection of the unknown word entity P u  as the condition context C r  in the unknown word information  12   b  (step S 103 ). 
     Subsequently, the execution detection unit  13   dc  detects execution of the speech intent I u  from the observation context C o  or the conversation (step S 104 ). Here, in a case where execution of the speech intent I u  has been detected (step S 104 , Yes), the association unit  13   dd  stores the observation context C o  at the time of execution of the speech intent I u  as the target context C t  in the unknown word information  12   b  (step S 105 ). 
     Then, the transmission unit  13   g  transmits the unknown word information  12   b , that is, the speech intent I u , the condition context C r , and the target context C t  for the unknown word entity P u  to the server apparatus  100  (step S 106 ), and ends the process. 
     Note that, in a case where execution of the speech intent I u  is not detected from the observation context C o  or the conversation (step S 104 , No), it is determined whether a certain period of time has elapsed or whether the condition is out of a condition range of the condition context C r  (step S 107 ). 
     Here, in a case where it is determined that the certain period of time has not elapsed and the condition is within the condition range of the condition context C r  (step S 107 , No), the process from step S 104  is repeatedly performed. On the other hand, in a case where it is determined that the certain period of time has elapsed or the condition is out of the condition range of the condition context C r  (step S 107 , Yes), the process is ended. 
     Incidentally, although the case where the entity is an unknown word as in the phrase “yellow signboard” has been mainly described so far, the intent may be an unknown word. Such a modification example will be described with reference to  FIG.  18   .  FIG.  18    is an explanatory diagram of unknown word information  12   b  according to a modification example. 
     For example, in semantic understanding of user&#39;s speech, there is a case where a verb portion that is estimated as an intent, such as “do that”, cannot be interpreted. In such a case, the information processing apparatus  10  registers the intent as the unknown word intent IP u  in the unknown word information  12   b  as illustrated in  FIG.  18   . 
     Then, as illustrated in  FIG.  18   , the information processing apparatus  10  associates a speech entity E u , the condition context C r , and an execution function with an unknown word intent IP u . The speech entity E u  is a phrase estimated as a known entity in the user&#39;s speech. 
     Then, the condition context C r  in such a case corresponds to the observation context C o  at the time of detection of the unknown word intent IP u , as illustrated in the same figure. Furthermore, the execution function corresponds to the observation context C o  at the time of execution of a function for the speech entity E u . 
     That is, in the example in  FIG.  18   , in a case where the unknown word intent IP u  is detected, the registration unit  13   db  registers the unknown word intent IP u  and the speech entity E u  in the unknown word information  12   b . Furthermore, the registration unit  13   db  registers the observation context C o  at the time of detection of the unknown word intent IP u  in association with the unknown word intent IP u  as the condition context C r . 
     Then, in a case where the execution detection unit  13   dc  detects that the function for the speech entity E u  has been executed on the basis of the observation context C o , the association unit  13   dd  associates the function with the unknown word intent IP u  as the execution function. Therefore, the information processing apparatus  10  can dynamically acquire the execution function of the unknown word intent IP u . 
     In addition to the association of the unknown word entity P u  with the target context C t , the unknown word intent IP u  is also accumulated in association with the execution function, so that the entire vocabulary that can be interpreted and expressed by the voice UI is automatically increased, and thus the interaction performance can be improved. 
     Note that by storing and accumulating attributes of a speaker in association as the condition context C r  of the unknown word intent IP u , it is useful for the system to interpret and express phrases having different expressions depending on attributes such as dialect (area), age, and gender. 
     2-5. Specific Example of Processing Details (Case of System-Directed Speech 
     Next, details of a process in a case of system-directed speech will be specifically described.  FIGS.  19  to  21    are processing explanatory diagrams (part 1) to (part 3) in the case of system-directed speech. Here, a case where a user U selects an image in an audio browser will be described as an example of the case of the system-directed speech. Furthermore, also here, a case where an unknown word is an entity will be described as a main example. 
     As illustrated in  FIG.  19   , it is assumed that the user U selects an image on an audio browser by using the information processing apparatus  10  such as a notebook PC that can use the audio browser. Note that the information processing apparatus  10  can be connected to the Internet and can also communicate with the server apparatus  100 . Furthermore, there may be an attendance O such as a family member or a friend around the user U. 
     In such a situation, as illustrated in  FIG.  20   , it is assumed that the user U has said “show me a photograph of OO”. Here, “OO” is a naming such as a name or a nickname that identifies a person appearing in the photograph, and is an unknown word. 
     Then, the information processing apparatus  10  detects the unknown word (step S 31 ), registers the unknown word entity P u  “OO” in the unknown word information  12   b , and registers the speech intent I u  “show the photograph” in association with the unknown word entity P u  “OO”. 
     Furthermore, the information processing apparatus  10  stores the observation context C o  at the time of detection of the unknown word in association with the unknown word entity P u  “OO” as the condition context C r  (step S 32 ). In the example in  FIG.  20   , the information processing apparatus  10  stores a uniform resource locator (URL) of a site that is being viewed by the user U as the condition context C r . 
     Then, the information processing apparatus  10  assigns numbers to all images that can be execution targets of the speech intent I u  on the same site and presents the images to the user U (step S 33 ). Then, an inquiry to prompt selection of an image is made to the user U (refer to “What number of photograph is it?” in the figure). 
     Then, if the user U selects an image in response to the inquiry (refer to “No. 1!” in the figure), the information processing apparatus  10  associates the observation context C o , that is, the selected image with the unknown word entity P u  “OO” as the target context C t  (step S 34 ). 
     Then, after the unknown word information  12   b  regarding such an unknown word entity P u  “OO” is generated, as illustrated in  FIG.  21   , if the speech intent I u  and the condition context C r  match (step S 35 ), the information processing apparatus  10  interprets the target context C t  as a real target and executes information processing corresponding thereto. 
     In other words, in a case where the user U says “show me the photograph of OO” while viewing the same site on another occasion or the like, the information processing apparatus  10  uses the unknown word entity P u  “OO” as a tag of the selected image (step S 36 ), and uses the unknown word entity P u  as a search tag of the image at the time of speech interpretation. 
     Furthermore, it is assumed that the unknown word information  12   b  is transmitted to the server apparatus  100 , and as a result of statistical processing performed in the server apparatus  100 , a predetermined number or more of unknown word entities P u  “OO” are registered for different public images. 
     In such a case, the server apparatus  100  executes machine learning using the unknown word entity P u  “OO” as a recognition label (step S 37 ), and generates and distributes an image recognizer as one of the recognition models  12   a  (step S 38 ). Steps S 37  and S 38  will be more specifically described later with reference to  FIGS.  27  and  28   . 
     As described above, with the processing details described with reference to  FIGS.  20  and  21   , it is possible to dynamically associate a real target with the unknown word entity P u  for the user U even in the case of the system-directed speech. 
     Note that, in  FIGS.  20  and  21   , the condition context C r  is the URL of the site that is being viewed by the user U, but the present disclosure is not limited thereto, and for example, a captured image of the attendance O or the like may be the condition context C r . Therefore, for example, it is possible to view content or the like in which the condition context C r  is that a specific member such as a family is present. 
     Next, a processing procedure in the case of system-directed speech executed by the information processing apparatus  10  according to the embodiment will be described with reference to  FIG.  22   .  FIG.  22    is a flowchart illustrating a processing procedure in the case of system-directed speech. 
     As shown in  FIG.  22   , first, the detection of the unknown word detection unit  13   da  detects unknown word entity P u  in response to an instruction for directing speech to the system (step S 201 ). Then, the registration unit  13   db  stores the speech intent I u  of the speech including the unknown word entity P u  in the unknown word information  12   b  together with the unknown word entity P u  (step S 202 ). 
     Furthermore, the registration unit  13   db  stores the observation context C o  at the time of detection of the unknown word entity P u  as the condition context C r  in the unknown word information  12   b  (step S 203 ). 
     Subsequently, the execution interaction control unit  13   d  assigns numbers to all the observation contexts C o  that can be execution targets of the speech intent I u  and presents the observation contexts C o  to the user (step S 204 ). Then, the execution detection unit  13   dc  detects that the user has selected one of the observation contexts C o  (step S 205 ). 
     Here, in a case where the user selects one of the observation contexts C o  (step S 205 , Yes), the instruction unit  13   de  executes the speech intent I u  with the candidate selected by the user (step S 206 ). Then, the association unit  13   dd  stores the observation context C o  selected by the user as the target context C t  in the unknown word information  12   b  (step S 207 ). 
     Then, the transmission unit  13   g  transmits the unknown word information  12   b , that is, the speech intent I u , the condition context C r , and the target context C t  for the unknown word entity P u  to the server apparatus  100  (step S 208 ), and ends the process. 
     Note that, in a case where the user does not select a context (step S 205 , No), it is determined whether a certain period of time has elapsed or the condition is out of the condition range of the condition context C r  (step S 209 ). Examples of the condition out of the condition range of the condition context C r  include a case where the user moves from a site to be viewed. 
     Here, in a case where it is determined that the certain period of time has not elapsed and the condition is within the condition range of the condition context C r  (step S 209 , No), the process from step S 205  is repeatedly performed. On the other hand, in a case where it is determined that the certain period of time has elapsed or the condition is out of the condition range of the condition context C r  (step S 209 , Yes), the process is ended. 
     2-6. Configuration of Server Apparatus 
     Next, a configuration example of the server apparatus  100  will be described.  FIG.  23    is a block diagram illustrating a configuration example of the server apparatus  100  according to the embodiment of the present disclosure. 
     As illustrated in  FIG.  23   , the server apparatus  100  includes a communication unit  101 , a storage unit  102 , and a control unit  103 . The communication unit  101  is realized by, for example, an NIC or the like. The communication unit  101  is connected to each of the information processing apparatuses  10  via the network N in a wireless or wired manner, and transmits and receives information to and from the information processing apparatus  10 . 
     Similarly to the storage unit  12  described above, the storage unit  102  is realized by, for example, a semiconductor memory element such as a RAM, a ROM, or a flash memory, or a storage device such as a hard disk or an optical disc. In the example illustrated in  FIG.  23   , the storage unit  102  stores an unknown word information database (DB)  102   a , statistical information  102   b , and a recognition model DB  102   c.    
     The unknown word information DB  102   a  is a database that accumulates the unknown word information  12   b  collected from each information processing apparatus  10  by a collecting unit  103   a  that will be described later. The statistical information  102   b  is information regarding a statistical result of statistical processing executed by a statistical processing unit  103   b  that will be described later. 
     The recognition model DB  102   c  is a database of the recognition model  12   a  generated by a learning unit  103   d  that will be described later and distributed to each information processing apparatus  10 . 
     Similarly to the control unit  13  described above, the control unit  103  is a controller, and is realized by, for example, a CPU, an MPU, or the like executing various programs stored in the storage unit  102  by using a RAM as a work area. Furthermore, similarly to the control unit  13  described above, the control unit  103  can be realized by, for example, an integrated circuit such as an ASIC or an FPGA. 
     The control unit  103  includes a collecting unit  103   a , a statistical processing unit  103   b , a determination unit  103   c , a learning unit  103   d , and a distribution unit  103   e , and realizes or executes a function or an action of information processing described below. 
     The collecting unit  103   a  collects the unknown word information  12   b  from each information processing apparatus  10  via the communication unit  101 , and accumulates the unknown word information  12   b  in the unknown word information DB  102   a . The statistical processing unit  103   b  executes predetermined statistical processing on the basis of the unknown word information  12   b  accumulated in the unknown word information DB  102   a , and outputs a statistical result as the statistical information  102   b.    
     The determination unit  103   c  determines an application range of the unknown word information  12   b  on the basis of the statistical information  102   b . Furthermore, the determination unit  103   c  determines whether it is necessary to update the recognition model  12   a  (for example, the image recognizer described above) on the basis of the statistical information  102   b.    
     In a case where the determination unit  103   c  determines that it is necessary to update the recognition model  12   a , the learning unit  103   d  executes a learning process using a predetermined machine learning algorithm on the basis of the unknown word information  12   b  accumulated in the unknown word information DB  102   a , and updates the recognition model  12   a  that is an update target in the recognition model DB  102   c.    
     The distribution unit  103   e  distributes the unknown word information  12   b  that is a distribution target in the unknown word information DB  102   a  to each information processing apparatus  10  via the communication unit  101  on the basis of the determination result from the determination unit  103   c . Furthermore, the distribution unit  103   e  distributes the recognition model  12   a  that is the distribution target in the recognition model DB  102   c  and is updated by the learning unit  103   d  to each information processing apparatus  10  via the communication unit  101 . 
     Next, a determination process executed by the determination unit  103   c  will be described with reference to  FIG.  24   .  FIG.  24    is an explanatory diagram of a determination process executed by the determination unit  103   c . Note that  FIG.  24    also illustrates an example of the statistical information  102   b . Furthermore, in the following description, a case where the selected image is associated with the unknown word entity P u  as the target context C t , which has been described in the case of a “system-directed speech”, will be described as an example. 
     As illustrated in  FIG.  24   , the statistical information  102   b  includes, for example, an “ID” item, a “P u ” item, a “C r ” item, a “C t ” item, and a “number of registrations” item. An identifier of each association result is stored in the “ID” item. The unknown word entity P u  is stored in the “P u ” item. The condition context C r  is stored in the “C r ” item. The target context C t  is stored in the “C t ” item. 
     An aggregation result of the number of respective association results registered within a certain period in the past is stored in the “number of registrations” item. The number of registrations may be paraphrased as the usage number. Note that the “predetermined number” in the figure is a specified number of the number of registrations. In a case where the number of registrations is equal to or larger than the predetermined number, the determination unit  103   c  applies the corresponding association result to the entire system. In  FIG.  24   , the predetermined number is set to “50” as an example. 
     Then, in the case of the example in  FIG.  24   , the determination unit  103   c  determines to apply the association results of the IDs “01” to “03” in which the number of registrations within a certain period in the past is equal to or larger than the predetermined number to the entire system. 
     Furthermore, in a case where the association result has high dependency on the specific condition context C r , the determination unit  103   c  determines to apply the association result without excluding the condition context C r . On the other hand, in a case where the association result has low dependency on the specific condition context C r , the determination unit  103   c  determines not to apply the association result to the condition context C r . 
     In the case of the example in  FIG.  24   , in the association results of the IDs “01” to “03”, the condition contexts C r  are diverse. Therefore, it can be said that the dependency on the condition context C r  is low, and the determination unit  103   c  excludes the condition context C r  from the condition in such a case. 
     Furthermore, the determination unit  103   c  determines to suppress the application of the association result of the ID “11” in which the number of registrations within a certain period in the past is smaller than the predetermined number to the entire system. 
     Note that, here, as illustrated in “erroneous registration?” in the figure, for the association result of the ID “12”, the unknown word entity P u  that is same as that of the IDs “01” to “03” is registered, but an image of a different person is associated as the target context C t . 
     As the erroneous registration, a case where a person makes a mistake without maliciousness, a case where a malicious person intentionally makes a mistake, and the like are conceivable. However, the determination unit  103   c  suppresses the application to the entire system in a case where the number of registrations within a certain period in the past is smaller than a predetermined number, and thus, it can be said that even if the malicious person makes a mistake, an association result is hardly applied to the entire system. 
     Note that, in an initial transient state in which the number of associations of the unknown word entity P u  with the specific image is small, for example, by storing or discarding the association through an interaction with the user U on the information processing apparatus  10  side, it is possible to reduce erroneous association. 
     A modification example thereof will be described with reference to  FIGS.  25  and  26   .  FIG.  25    is a diagram (part 1) illustrating an interaction processing example of storing or discarding association. Furthermore,  FIG.  26    is a diagram (part 2) illustrating an interaction processing example of storing or discarding association. 
     Note that  FIG.  25    corresponds to the continuation of  FIG.  20    already described. Furthermore, although the correct image to be associated with the unknown word entity P u  “OO” is the image of No. 1, for example, it is assumed that a small number (smaller than the predetermined number described above) of pieces of data is associated with an image of No. 4 with maliciousness. 
     In such a case, as illustrated in  FIG.  25   , in a case where the user U selects the image of No. 1 in response to an inquiry to prompt selection of an image to be associated with the unknown word entity P u  “OO”, the information processing apparatus  10  makes an inquiry of “Then, is No. 4 also OO?” to the user U, for example. 
     Here, since the user U selects the correct image in  FIG.  25   , it can be estimated that the user U is not at least a malicious person. Therefore, as illustrated in  FIG.  26   , in a case where the user U has expressed an intention of “No” in response to the inquiry in  FIG.  25   , the information processing apparatus  10  discards the association between the unknown word entity P u  “OO” and the image of No. 4. 
     Furthermore, in a case where the user U has expressed an intention of “Yes”, the information processing apparatus  10  stores the association between the unknown word entity P u  “OO” and the image of No. 4. Therefore, for example, it is possible to reduce erroneous association performed by a malicious person. 
     2-7. Application Example of Automatic Update Using Area of Image Recognizer 
     Next, steps S 37  and S 38  described with reference to  FIG.  21    will be described more specifically with reference to  FIGS.  27    and  28 . That is, the unknown word information  12   b  that is an association result is transmitted to the server apparatus  100 , and as a result of the statistical processing performed in the server apparatus  100 , a predetermined number or more of specific unknown word entities P u  are associated with different public images. 
     In such a case, as described above, the server apparatus  100  executes machine learning using the corresponding unknown word entity P u  as a recognition label, and generates and distributes an image recognizer as one of the recognition models  12   a.    
       FIG.  27    is a diagram (part 1) illustrating an application example of automatic update using an area of the image recognizer. Furthermore,  FIG.  28    is a diagram (part 2) illustrating an application example of automatic update using the area of the image recognizer. 
     Note that, in the description using  FIGS.  27  and  28   , different areas a and b will be described as an example. The area a is, for example, an area where a penetration rate of a liquid soap is high. On the other hand, the area b is, for example, an area where a penetration rate of a solid soap is high. 
     Then, here, it is assumed that a predetermined number or more of public different images with which the phrase “soap” is tagged (associated) exist, and machine learning using the phrase “soap” as a recognition label is performed. 
     In such a case, as shown in  FIG.  27   , first, in the area a, the phrase “soap” is more likely to be tagged with different public images of the liquid soap. Note that it is assumed that the condition context C r  of each image includes the area a. 
     Then, in a case where a predetermined number or more of images of the liquid soap tagged with the phrase “soap” are collected, the learning unit  103   d  of the server apparatus  100  executes machine learning using “soap” as a recognition label, and generates an image recognizer A. The server apparatus  100  distributes the image recognizer to each information processing apparatus  10  in the area a, and in the information processing apparatus  10  in the area a, in a case where an image of the liquid soap is input to the image recognizer A as a recognition target image, a recognition result of “soap” is obtained. 
     However, the image recognizer A is generated through machine learning executed using the image of the liquid soap as training data. Therefore, even if the image recognizer A is distributed to each information processing apparatus  10  in the area b, and an image of the solid soap is input as a recognition target image to the image recognizer A, it is not possible to obtain the recognition result of “soap”. 
     Therefore, if, for example, the “area a” is associated with the phrase “soap” as the condition context C r  in the corresponding unknown word information  12   b  of the unknown word information DB  102   a , the server apparatus  100  sets a distribution target of the image recognizer A to only the area a. 
     On the other hand, as shown in  FIG.  28   , in the area b, the phrase “soap” is more likely to be tagged with different public images of the solid soap. Therefore, in a case where a predetermined number or more of images of the solid soap tagged with the phrase “soap” are collected, the learning unit  103   d  of the server apparatus  100  executes machine learning using “soap” as a recognition label. However, if the image recognizer A (refer to  FIG.  27   ) having the same phrase “soap” as a recognition label already exists, the learning unit  103   d  executes update learning and outputs an image recognizer A′. 
     Then, the server apparatus  100  distributes the image recognizer to each information processing apparatus  10  in the area b, and in the information processing apparatus  10  in the area b, when an image of solid soap is input to the image recognizer A′ as a recognition target image, a recognition result of “soap” is obtained. 
     Furthermore, the server apparatus  100  may determine that the dependency on the “area a” associated with the phrase “soap” in the unknown word information  12   b  hitherto as the condition context C r  has decreased by executing the update learning on the basis of the image of the solid soap in the area b. Then, in this case, the server apparatus  100  excludes the “area a” from the condition. 
     Furthermore, if the “area a” is excluded from the condition context C r  as described above, the server apparatus  100  may set a distribution target of the image recognizer A′ to not only the area b but also, for example, all areas. Then, in a case where the server apparatus  100  distributes the image recognizer A′ to, for example, the area a, and the information processing apparatus  10  in the area a inputs an image of the liquid soap or the solid soap to the image recognizer A as a recognition target image, a recognition result of “soap” can be obtained in either case. 
     As described above, in a case where the dependency of the unknown word information  12   b  on the specific condition context C r  decreases as opportunity learning is repeated, the trend following performance of the recognition model  12   a  can be improved by excluding the corresponding condition context C r  from the condition and changing a distribution target of the recognition model  12   a  including the image recognizer according thereto. 
     3. Modification Examples 
     Note that, although the information processing method according to the embodiment for acquiring a real target of an unknown word has been described so far, various modification examples can be made in addition to the description. 
     3-1. Modification Example in Case of Human-Directed Speech 
     For example, acquisition of a real target of the unknown word entity P u  in the case of human-directed speech can also be applied to viewing of a television program or video content by a family or the like. At the time of such viewing, for example, it is assumed that a child or an elderly person says “I want to watch XX (appears)”. “XX” is a naming for an animation character or a performer. 
     In this case, the information processing apparatus  10  realized by, for example, a television set, a PC, or the like detects the unknown word entity P u  “XX”, and associates the attendance O at the place, a time zone, or the like as the condition context C r  with the unknown word entity P u “XX”. Then, in a case where a program is actually selected or video content is reproduced, the information processing apparatus  10  further associates the selected program or the reproduced video content as the target context C t . 
     Therefore, thereafter, in a case where there is speech of “I want to watch XX” from the same attendance O or in the same time zone, the information processing apparatus  10  can interpret the unknown word entity P u  “XX” as the program or the video content. 
     Furthermore, as another modification example, a scene in which a plurality of persons searches for a restaurant or the like may be exemplified. In such a case, for example, the information processing apparatus  10  realized by a smartphone or the like may set a context of a conversation between persons immediately before, the persons at the place, the place, and the like as the condition context C r . 
     As an example, it is assumed that one of members who are going to have a meal together in Shinagawa says “is there something delicious around here?”. Then, the information processing apparatus  10  detects the unknown word entity P u  “something delicious”, and associates the unknown word entity P u  “something delicious” with, for example, the attendance O, Shinagawa, or the like as the condition context C r . 
     Then, for example, in a case where another one of the members replies “let&#39;s go to the AA store” to the previous speech, the information processing apparatus  10  further associates the “AA store” as the target context C t . 
     Therefore, thereafter, in a case where the same member in Shinagawa says “something delicious”, the information processing apparatus  10  can interpret the unknown word entity P u  “something delicious” as the “AA store”, and can present the unknown word entity P u  as a first candidate in a restaurant search, for example. 
     3-2. Modification Example in Case of System-Directed Speech 
     Furthermore, for example, the acquisition of a real target of the unknown word entity P u  in the case of system-directed speech is not limited to the image search illustrated in  FIGS.  19  to  21   , and can be applied to various content searches. 
     In such a case, as illustrated in  FIG.  20   , the content selected by the user U from among a plurality of presented candidates is associated as the target context C t . 
     Furthermore, as another modification example, for example, a known phrase based on text selected by the user U may be associated with the unknown word entity P u  as the target context C t . In such a case, in a case where the unknown word entity P u  is detected, the information processing apparatus  10  can interpret the unknown word entity P u  with a known phrase that is a synonym. 
     Furthermore, as still another modification example, a case where the intent described with reference to  FIG.  18    is an unknown word may also be applied to the case of system-directed speech. 
     Even in the case of the system-directed speech, the information processing apparatus  10  associates the speech entity E u , the condition context C r , and the execution function with the detected unknown word intent IP u . Note that, in the case of the system-directed speech, similarly to the example illustrated in  FIG.  20   , the information processing apparatus  10  presents candidates of functions that can be executed by the system for the speech entity E u , and makes an inquiry to prompt the user U to select a function to be executed. 
     Then, if the user U selects the function to be executed in response to the inquiry, the information processing apparatus  10  associates the observation context C o , that is, the selected execution function with the unknown word intent IP u  as the target context C t . Therefore, the information processing apparatus  10  can dynamically acquire the execution function of the unknown word intent IP u  even in the case of the system-directed speech. 
     3-3. Other Modification Examples 
     Furthermore, in the above-described embodiment, the case where an unknown word is detected from text input in a spoken language has been described, but the present disclosure is not limited thereto, and the unknown word is only required to be input in a natural language. Therefore, for example, an unknown word may be detected from a message of a message application. In addition, for example, an unknown word may be detected from an article published on a Web. 
     Furthermore, among the processes described in the above embodiments, all or some of the processes described as being performed automatically may be performed manually, or all or some of the processes described as being performed manually may be performed automatically according to a known method. In addition, the processing procedure, specific name, and information including various types of data or parameters described in the above specification and the drawings may be freely changed unless otherwise specified. For example, the various types of information illustrated in each drawing are not limited to the illustrated information. 
     Furthermore, a constituent of each device illustrated in the drawings is functionally conceptual, and is not necessarily physically configured as illustrated in the drawings. That is, a specific form of distribution and integration of the respective devices is not limited to the illustrated form, and all or some thereof can be functionally or physically distributed and integrated in any unit according to various loads, usage conditions, or the like. For example, the unknown word detection unit  13   da  and the execution detection unit  13   dc  illustrated in  FIG.  7    may be integrated. Furthermore, the registration unit  13   db  and the association unit  13   dd  illustrated in the same  FIG.  7    may be integrated. 
     Furthermore, each function executed by the control unit  13  of the information processing apparatus  10  illustrated in  FIG.  7    may be executed by the server apparatus  100 . In such a case, the information processing apparatus  10  used by the user U includes the voice input unit  2 , the sensor unit  3 , the display unit  4 , the voice output unit  5 , and the communication unit  11 , transmits and receives information to and from the server apparatus  100  via the network N, and functions as a so-called voice UI device that presents an execution result of each function in the server apparatus  100  to the user U through interaction with the user U. 
     Furthermore, the above-described embodiments can be combined as appropriate in a region in which the processing details do not contradict each other. Furthermore, the order of each step illustrated in the sequence diagram or the flowchart of the present embodiment can be changed as appropriate. 
     4. Hardware Configuration 
     An information apparatus such as the information processing apparatus  10  and the server apparatus  100  according to the above-described embodiment is implemented by a computer  1000  having a configuration as illustrated in  FIG.  29   , for example. Hereinafter, the information processing apparatus  10  according to the embodiment will be described as an example.  FIG.  29    is a hardware configuration diagram illustrating an example of the computer  1000  that realizes the functions of the information processing apparatus  10 . The computer  1000  includes a CPU  1100 , a RAM  1200 , a ROM  1300 , a hard disk drive (HDD)  1400 , a communication interface  1500 , and an input/output interface  1600 . The respective units of the computer  1000  are connected via a bus  1050 . 
     The CPU  1100  operates on the basis of a program stored in the ROM  1300  or the HDD  1400 , and controls each unit. For example, the CPU  1100  loads a program stored in the ROM  1300  or the HDD  1400  to the RAM  1200 , and executes processes corresponding to various programs. 
     The ROM  1300  stores a boot program such as a basic input output system (BIOS) executed by the CPU  1100  when the computer  1000  is started, a program depending on hardware of the computer  1000 , and the like. 
     The HDD  1400  is a computer-readable recording medium that records a program executed by the CPU  1100 , data used by the program, and the like in a non-transitory manner. Specifically, the HDD  1400  is a recording medium that records an information processing program according to the present disclosure as an example of the program data  1450 . 
     The communication interface  1500  is an interface via which the computer  1000  is connected to an external network  1550  (for example, the Internet). For example, the CPU  1100  receives data from another apparatus or transmits data generated by the CPU  1100  to another apparatus via the communication interface  1500 . 
     The input/output interface  1600  is an interface connecting the input/output device  1650  to the computer  1000 . For example, the CPU  1100  receives data from an input device such as a keyboard or a mouse via the input/output interface  1600 . Furthermore, the CPU  1100  transmits data to an output device such as a display, a speaker, or a printer via the input/output interface  1600 . Furthermore, the input/output interface  1600  may function as a media interface that reads a program or the like recorded in a predetermined recording medium (medium). The medium is, for example, an optical recording medium such as a digital versatile disc (DVD) or a phase change rewritable disk (PD), a magneto-optical recording medium such as a magneto-optical disk (MO), a tape medium, a magnetic recording medium, a semiconductor memory, or the like. 
     For example, in a case where the computer  1000  functions as the information processing apparatus  10  according to the embodiment, the CPU  1100  of the computer  1000  executes the information processing program loaded to the RAM  1200  to realize the functions of the voice recognition unit  13   a , the semantic understanding unit  13   b , the context recognition unit  13   c , the execution interaction control unit  13   d , the response generation unit  13   e , the output control unit  13   f , the transmission unit  13   g , and the like. Furthermore, the HDD  1400  stores the information processing program according to the present disclosure and data in the storage unit  12 . Note that the CPU  1100  reads the program data  1450  from the HDD  1400  and executes the program data, but as another example, the program may be acquired from another device via the external network  1550 . 
     5. Conclusion 
     As described above, according to an embodiment of the present disclosure, the information processing apparatus  10  includes: the unknown word detection unit  13   da  (corresponding to an example of a “first detection unit”) that detects an unknown word that is an unknown phrase from text input in a natural language; the execution detection unit  13   dc  (corresponding to an example of a “second detection unit”) that detects the occurrence of an event related to a known phrase included in the text; and the association unit  13   dd  that associates, with the unknown word, each of the observation context C o  indicating a situation at the time of detection of the unknown word as the condition context C r  and the observation context C o  indicating a situation at the time of the occurrence of the event as the target context C t . Therefore, an unknown word can be efficiently associated with a real target without imposing a load on a user. 
     Although the respective embodiments of the present disclosure have been described above, the technical scope of the present disclosure is not limited to the above-described embodiments as it is, and various modifications can be made without departing from the concept of the present disclosure. Furthermore, constituents of different embodiments and modification examples may be combined as appropriate. 
     Furthermore, the effects of each embodiment described in the present specification are merely examples and are not limited, and other effects may be provided. 
     Note that the present technology can also have the following configurations. 
     (1) 
     An information processing apparatus including: 
     a first detection unit that detects an unknown word that is an unknown phrase from text input in a natural language; 
     a second detection unit that detects occurrence of an event related to a known phrase included in the text; and 
     an association unit that associates, with the unknown word, each of an observation context indicating a situation at the time of detection of the unknown word as a condition context and an observation context indicating a situation at the time of the occurrence of the event as a target context. 
     (2) 
     The information processing apparatus according to (1), in which 
     the first detection unit 
     detects, as the unknown word, a phrase that does not exist in dictionary information used in an NLU process for the text, or a phrase that exists in the dictionary information but does not uniquely specify a real target corresponding to the phrase in information processing based on the text. 
     (3) 
     The information processing apparatus according to (1) or (2), in which 
     the first detection unit 
     detects the unknown word from the text input through a conversation of a user. 
     (4) 
     The information processing apparatus according to (1), (2), or (3), in which 
     the first detection unit 
     detects the unknown word from the text input as a speech instruction from a user. 
     (5) 
     The information processing apparatus according to (2), in which 
     the second detection unit 
     detects execution of an intent extracted through the NLU process in a case where the unknown word detected by the first detection unit is a phrase extracted as an entity through the NLU process, and 
     the association unit 
     associates an observation context at the time of detection of the unknown word with the unknown word as the condition context, and associates an observation context at the time of execution of the intent with the unknown word as the target context. 
     (6) 
     The information processing apparatus according to (5), in which 
     in a case where a movement situation is observed, the association unit 
     associates position information indicating a predetermined range including a current position at the time of detection of the unknown word with the unknown word as the condition context, and associates an observation context indicating a current position at the time of execution of the intent with the unknown word as the target context. 
     (7) 
     The information processing apparatus according to (5) or (6), in which 
     the association unit associates an observation context indicating a time zone at the time of detection of the unknown word with the unknown word as the condition context. 
     (8) 
     The information processing apparatus according to (5), (6), or (7), in which 
     in a case where a movement situation is observed and an attribute of presence of directivity is extracted from the unknown word through the NLU process, the association unit 
     associates an observation context indicating an advancing direction range within a predetermined angle from an advancing direction at the time of detection of the unknown word with the unknown word as the condition context. 
     (9) 
     The information processing apparatus according to any one of (5) to (8), in which 
     the association unit 
     associates a captured image at the time of detection of the unknown word with the unknown word as the condition context, and associates a captured image at the time of execution of the intent with the unknown word as the target context. 
     (10) 
     The information processing apparatus according to (2), in which 
     in a case where the unknown word detected by the first detection unit is a phrase extracted as an entity through the NLU process, the second detection unit 
     presents all candidates that can be execution targets of an intent extracted through the NLU process to a user and detects that the user has selected one of the candidates, and 
     the association unit 
     associates an observation context at the time of detection of the unknown word with the unknown word as the condition context, and associates the candidate selected by the user with the unknown word as the target context. 
     (11) 
     The information processing apparatus according to (2), in which 
     in a case where the unknown word detected by the first detection unit is a phrase extracted as an intent through the NLU process, the second detection unit 
     detects execution of a function for an entity extracted through the NLU process, and 
     the association unit 
     associates an observation context at the time of detection of the unknown word with the unknown word as the condition context, and associates the function with the unknown word as the target context. 
     (12) 
     The information processing apparatus according to any one of (1) to (11), further including: 
     a transmission unit that transmits an association result from the association unit to a server apparatus, in which 
     in a case where it is determined that a predetermined number or more of the unknown words have not been used in the same condition context and the same target context as the association result within a past certain period on the basis of a statistical result of the association result, the server apparatus 
     suppresses distribution of the association result. 
     (13) 
     The information processing apparatus according to (12), in which 
     in a case where it is determined that dependency of the unknown word on a specific condition context has decreased on the basis of the statistical result of the association result, the server apparatus 
     cancels association of the specific condition context with the unknown word. 
     (14) 
     An information processing apparatus including: 
     a first detection unit that detects an unknown word that is an unknown phrase from text input in a natural language; 
     a second detection unit that detects occurrence of an event related to a known phrase included in the text; 
     an association unit that associates, with the unknown word, each of an observation context indicating a situation at the time of detection of the unknown word as a condition context and an observation context indicating a situation at the time of the occurrence of the event as a target context; and 
     an instruction unit that, in a case where the known phrase is included in new text and the condition context associated with the unknown word is observed, gives an instruction for generating a response using the unknown word. 
     (15) 
     The information processing apparatus according to (14), in which 
     in a case where the response using the unknown word is generated, the instruction unit 
     causes an image representing the condition context associated with the unknown word and an image representing the target context associated with the unknown word to be generated such that a user can visually recognize the images. 
     (16) 
     An information processing method including: 
     detecting an unknown word that is an unknown phrase from text input in a natural language; 
     detecting occurrence of an event related to a known phrase included in the text; and 
     associating, with the unknown word, each of an observation context indicating a situation at the time of detection of the unknown word as a condition context and an observation context indicating a situation at the time of the occurrence of the event as a target context. 
     (17) 
     An information processing method including: 
     detecting an unknown word that is an unknown phrase from text input in a natural language; 
     detecting occurrence of an event related to a known phrase included in the text; 
     associating, with the unknown word, each of an observation context indicating a situation at the time of detection of the unknown word as a condition context and an observation context indicating a situation at the time of the occurrence of the event as a target context; and 
     in a case where the known phrase is included in new text and the condition context associated with the unknown word is observed, giving an instruction for generating a response using the unknown word. 
     (18) 
     An information processing apparatus including: 
     an instruction unit that gives an instruction for generating a response according to a phrase on the basis of the phrase included in text input in a natural language, in which 
     the instruction unit gives an instruction for generating a response using an unknown word on the basis of a condition context that is associated with the unknown word that is an unknown phrase detected from the text and is an observation context indicating a situation at the time of detection of the unknown word and a target context that is an observation context indicating a situation at the time of occurrence of an event related to a known phrase included in the text in a case where the known phrase is included in new text and the condition context associated with the unknown word is observed. 
     (19) 
     An information processing method including: 
     giving an instruction for generating a response according to a phrase on the basis of the phrase included in text input in a natural language, in which 
     the giving an instruction includes giving an instruction for generating a response using an unknown word on the basis of a condition context that is associated with the unknown word that is an unknown phrase detected from the text and is an observation context indicating a situation at the time of detection of the unknown word and a target context that is an observation context indicating a situation at the time of occurrence of an event related to a known phrase included in the text in a case where the known phrase is included in new text and the condition context associated with the unknown word is observed. 
     (20) 
     A non-transitory computer readable recording medium storing a program causing a computer to execute: 
     detecting an unknown word that is an unknown phrase from text input in a natural language; 
     detecting occurrence of an event related to a known phrase included in the text; and 
     associating, with the unknown word, each of an observation context indicating a situation at the time of detection of the unknown word as a condition context and an observation context indicating a situation at the time of the occurrence of the event as a target context. 
     (21) 
     A non-transitory computer readable recording medium storing a program causing a computer to execute: 
     detecting an unknown word that is an unknown phrase from text input in a natural language; 
     detecting occurrence of an event related to a known phrase included in the text; 
     associating, with the unknown word, each of an observation context indicating a situation at the time of detection of the unknown word as a condition context and an observation context indicating a situation at the time of the occurrence of the event as a target context; and 
     in a case where the known phrase is included in new text and the condition context associated with the unknown word is observed, giving an instruction for generating a response using the unknown word. 
     REFERENCE SIGNS LIST 
     
         
           1  Information processing system 
           10  Information processing apparatus 
           11  Communication unit 
           12  Storage unit 
           12   a  Recognition model 
           12   b  Unknown word information 
           13  Control unit 
           13   a  Voice recognition unit 
           13   b  Semantic understanding unit 
           13   c  Context recognition unit 
           13   d  Execution interaction control unit 
           13   da  Unknown word detection unit 
           13   db  Registration unit 
           13   dc  Execution detection unit 
           13   dd  Association unit 
           13   de  Instruction unit 
           13   e  Response generation unit 
           13   f  Output control unit 
           13   g  Transmission unit 
           100  Server apparatus 
           101  Communication unit 
           102  Storage unit 
           102   a  Unknown word information DB 
           102   b  Statistical information 
           102   c  Recognition model DB 
           103  Control unit 
           103   a  Collecting unit 
           103   b  Statistical processing unit 
           103   c  Determination unit 
           103   d  Learning unit 
           103   e  Distribution unit