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WO2011013229A1 - Dispositif de recommandation de comportement - Google Patents

Dispositif de recommandation de comportement Download PDF

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Publication number
WO2011013229A1
WO2011013229A1 PCT/JP2009/063577 JP2009063577W WO2011013229A1 WO 2011013229 A1 WO2011013229 A1 WO 2011013229A1 JP 2009063577 W JP2009063577 W JP 2009063577W WO 2011013229 A1 WO2011013229 A1 WO 2011013229A1
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WO
WIPO (PCT)
Prior art keywords
unknown word
expression
unit
weight
action
Prior art date
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Ceased
Application number
PCT/JP2009/063577
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English (en)
Japanese (ja)
Inventor
昌之 岡本
貴之 飯田
匡晃 菊池
奈夕子 渡辺
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Toshiba Corp
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Toshiba Corp
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Publication date
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Priority to PCT/JP2009/063577 priority Critical patent/WO2011013229A1/fr
Publication of WO2011013229A1 publication Critical patent/WO2011013229A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/31—Indexing; Data structures therefor; Storage structures
    • G06F16/313—Selection or weighting of terms for indexing

Definitions

  • the present invention relates to an action recommendation that provides appropriate information to a user based on an unknown word.
  • the present invention has been made in view of the above, and an object thereof is to provide an action recommendation device capable of providing appropriate information to a user based on an unknown word.
  • the present invention is an action recommendation device, which extracts an unknown word that matches a predetermined condition from content including a document, An appearance frequency specifying unit that specifies the weight of one or more action expressions connected to the unknown word from the document including the unknown word, an existing specific expression, and 1 or 2 connected to the specific expression
  • the specific expression storage unit that associates and stores the behavior expression and the weight of the behavior expression in the document including the behavior expression, and the one or more of the behavior expressions that are connected to the unknown word.
  • a semantic estimation unit that estimates the meaning of the unknown word based on a similarity between a weight and the weight of one or more of the behavioral expressions that are connected to the specific expression; the behavioral expression; and a search method With correspondence
  • the search method storage unit and the specific expression storage unit stores the behavior expression
  • a search method selection unit for selecting the search method associated with the search method; a search unit for searching for the unknown word by the search method selected by the search method selection unit; and a search result output by the search unit And an output unit.
  • FIG. 1 is a block diagram showing the configuration of the behavior recommendation system 1.
  • the behavior recommendation system 1 extracts an unknown word from the mobile terminal 100 used by the user and text information in the content referred to by the mobile terminal 100, and performs processing for providing the user with a service suitable for the unknown word.
  • an action recommendation device 200 is an action recommendation device 200.
  • the unknown word is a word whose meaning is not registered in the behavior recommendation device 200 or the like. Specifically, there are combinations of plural nouns, words in katakana notation, and the like.
  • the mobile terminal 100 includes a communication unit 102, a display unit 104, a content extraction unit 106, and a selection unit 108.
  • the communication unit 102 transmits and receives various information via the Internet.
  • the communication unit 102 transmits the position information of the mobile terminal 100 obtained by GPS (Global Positioning System) to the behavior recommendation device 200.
  • the display unit 104 displays a web page acquired by the communication unit 102 via the Internet.
  • the web page includes still images, moving images, and the like in addition to text information.
  • the content extraction unit 106 extracts the display content displayed on the display unit 104.
  • the selection unit 108 selects information to be displayed on the display unit 104 in accordance with an input from the user. Note that the information selected by the selection unit 108 includes information provided by the behavior recommendation device 200.
  • the behavior recommendation device 200 includes a communication unit 202, an unknown word extraction unit 204, an appearance frequency specifying unit 206, an unknown word storage unit 208, a specific expression storage unit 210, a meaning estimation unit 212, and a search method storage unit 214. And a search method selection unit 216 and a search unit 218.
  • the communication unit 202 transmits and receives various information via the Internet.
  • the communication unit 202 receives, for example, a document that the user is browsing on the mobile terminal 100, that is, text information or display content of a web page displayed on the display unit 104.
  • the unknown word extraction unit 204 identifies a word that matches a predetermined notation condition as an unknown word from the text information and display content received by the communication unit 202 from the mobile terminal 100. Examples of the display condition include parenthesis notation, meaning that the meaning of a word is not registered in the behavior recommendation device 200, and the like.
  • the appearance frequency specifying unit 206 weights one or more action expressions having a linguistic connection relationship or a semantic connection relationship with an unknown word extracted by the unknown word extraction unit 204 for a predetermined document.
  • the action frequency is specified as Specifically, the appearance frequency specifying unit 206 specifies the appearance frequency of one or more action expressions that co-occur with an unknown word as an object.
  • the action expression is a verb.
  • Existing morphological analysis and syntax analysis techniques can be used to extract co-occurring behavioral expressions. For example, if an unknown word “vejimite” contains two sentences “eating vegemite” and “want to eat vegemite” in the target document, the action expression “eating”
  • the appearance frequency is specified as 2 times.
  • an unknown word is acquired using an action expression with the unknown word as the object, but a co-occurrence relationship not limited to the object or a connection expression other than the object may be used.
  • the appearance frequency itself is used as the weight of the action expression, a value obtained by normalizing the appearance frequency with the appearance frequency of all the action expressions may be used.
  • the document targeted when the appearance frequency specifying unit 206 specifies the appearance frequency of an unknown word is a web page arbitrarily acquired via a network.
  • the behavior recommendation device 200 or another device has a document storage unit that stores a plurality of documents, and the appearance frequency specifying unit 206 targets documents stored in the document storage unit. It is good also as specifying the appearance frequency of an unknown word.
  • the appearance frequency specifying unit 206 only needs to be able to specify the appearance frequency of unknown words in an arbitrarily selected document, and the target document is not particularly limited.
  • the appearance frequency specifying unit 206 stores the specified appearance frequency in the unknown word storage unit 208 together with the unknown word. As illustrated in FIG. 2, the unknown word storage unit 208 stores, for each unknown word, the co-occurring behavioral expression and the appearance frequency of the unknown word in association with each other. In this way, the appearance frequency specifying unit 206 makes it possible to identify unknown words for one or more action expressions such as “vejimite” 20 times for the action expression “eat” and 13 times for “paint”. Distribution of appearance frequency is obtained.
  • the specific expression storage unit 210 stores a noun as an existing specific expression, one or more action expressions co-occurring in the specific expression, and the appearance frequency of the specific expression in association with each other. ing.
  • Each unique expression belongs to a semantic class, and the semantic class is hierarchically structured. For example, the specific expression “butter” belongs to the semantic class “food”. Further, the semantic class “food” belongs to the semantic class “food”.
  • the specific expression storage unit 210 has an appearance frequency of 477 times that co-occurs with “use”, an appearance frequency of 354 times that co-occurs with “put”, and an appearance frequency of 309 times that co-occurs with “paint”.
  • a plurality of behavioral expressions co-occurring with a specific expression and the appearance frequency of each specific expression are stored in association with each other. That is, the specific expression storage unit 210 stores the specific expression “butter” and the distribution of the appearance frequencies for each of the plurality of behavior expressions that co-occur with the specific expression “butter”. The same applies to the other proper expressions “jam” and “hamburg”.
  • the behavioral expression and the appearance frequency of each semantic class are the sum of the appearance frequencies for a plurality of specific expressions belonging to each semantic class.
  • the frequency of appearance of the action expression “use” for the semantic class “food” is 477 times and 69 times of appearance of the action expressions “use” of the specific expressions “butter” and “jam” belonging to the meaning class “food”.
  • the action expression and the appearance frequency of the semantic class in the upper hierarchy are the sum of the appearance frequencies for the specific expressions belonging to the plurality of semantic classes belonging to the semantic class in the upper hierarchy.
  • the frequency of appearance of the action expression “use” in the semantic class “food” is the specific expressions “butter” and “jam” belonging to the lower semantic classes “food” and “dish” included in the semantic class “food”. ”,“ Hamburger ”and“ pasta ”are the total appearance frequencies of the action expression“ use ”.
  • the sum of the appearance frequencies of the behavior expressions of the lower semantic classes is used as the weight of the behavior expressions of the higher semantic classes.
  • the weights of the behavior expressions of the respective semantic classes are not normalized. It is also possible to have a plurality of operations in between, such as the sum of the normalized values and the normalized value.
  • the meaning estimation unit 212 distributes the appearance frequency of one or more action expressions for the unknown word obtained by the appearance frequency specifying unit 206 and one or more for the specific expressions stored in the specific expression storage unit 210. The appearance frequency distribution is compared, and the proper expression similar to the unknown word or the semantic class to which the unknown word belongs is estimated as the meaning of the unknown word.
  • the inner product of vectors is used for the evaluation of similarity. That is, the appearance frequency distribution for the behavioral expression is regarded as a word vector, and it is determined that the larger the inner product, the more similar. Specifically, the product of the appearance frequencies of the (identical) action expressions common to the unknown word and the specific expression to be compared is calculated. Then, the sum of all products obtained for each of the behavioral expressions common to the unknown word and the specific expression to be compared is calculated as the similarity.
  • a vector inner product is used as the similarity, but the Euclidean distance is not used even if a similarity calculation method other than the inner product itself is used, such as assigning a weight to a specific action expression.
  • the similarity calculation method may be used.
  • the search method storage unit 214 stores an action expression and a search method for an unknown word in association with each other.
  • the search method is a method for searching for an action recommended for the user of the mobile terminal 100. For example, if the unknown word co-occurs with the action expression “buy”, the corresponding search method is, for example, “search for a product site that uses an unknown word as a product and present the URL of the site”. is there. Thereby, the user can recommend an action of browsing the product site.
  • the search method selection unit 216 refers to the specific expression storage unit 210 and identifies an action expression corresponding to the meaning estimated by the meaning estimation unit 212.
  • the search method selection unit 216 further selects a search method associated with the identified action expression in the search method storage unit 214.
  • the search unit 218 performs a search for an unknown word to be processed by the search method selected by the search method selection unit 216, and transmits the search result to the mobile terminal 100 via the communication unit 202. That is, the communication unit 202 corresponds to an output unit that outputs search results.
  • FIG. 5 is a flowchart showing a behavior recommendation process by the behavior recommendation device 200.
  • the communication unit 202 of the behavior recommendation device 200 receives browsing information including text information such as a web page being browsed from the mobile terminal 100
  • the unknown word extraction unit 204 uses the text information included in the browsing information received by the communication unit 202.
  • An unknown word is extracted (step S100).
  • the appearance frequency specifying unit 206 targets the web page as a predetermined document.
  • the appearance frequency of the action expression co-occurring with the unknown word is specified (step S104).
  • the appearance frequency specifying unit 206 stores the specified behavioral expression and the appearance frequency in the unknown word storage unit 208 in association with the unknown word (step S106).
  • the semantic estimation unit 212 determines the distribution of the appearance frequency of one or more action expressions for the unknown word and the appearance frequency of one or more action expressions for the specific expression stored in the specific expression storage unit 210.
  • the similarity of the distribution is calculated, and the meaning is estimated based on the similarity (step S120).
  • the specific expression having the highest similarity is estimated as the meaning of the unknown word. For example, if the similarity between the unknown word “vejimite” and the specific expression “butter” stored in the specific expression storage unit 210 is highest, it is determined that the unknown word “vejimite” is a word close to “butter”. .
  • the semantic estimation unit 212 displays the specific expression.
  • the similarity between the semantic class to which the word belongs and the unknown word is calculated, and the meaning of the semantic class having the highest similarity with the unknown word is estimated as the meaning of the unknown word.
  • the degree of similarity with any specific expression such as “butter” or “jam”
  • the degree of similarity between the semantic class “food” and the unknown word is calculated.
  • the similarity between each semantic class in the same hierarchy as the semantic class “food” and the unknown word is lower than the threshold, the similarity between the semantic class and the unknown word in the higher hierarchy is calculated, and the unknown word and The meaning of the semantic class with the highest similarity is estimated as the meaning of the unknown word.
  • the degree of similarity is equal to or less than the threshold value, appropriate meaning estimation can be performed by increasing the abstraction level and estimating the meaning.
  • the search method selection unit 216 specifies an action expression associated with the meaning estimated by the meaning estimation unit 212 in the specific expression storage unit 210. Then, the search method storage unit 214 selects a search method associated with the action expression (step S122). Next, the search unit 218 searches for an object using the search method selected by the search method selection unit 216, and transmits the search result to the mobile terminal 100 (step S124). That is, behavior recommendation is performed to the user. This completes the action recommendation process.
  • step S120 the meaning estimation unit 212 estimates that the unknown word “vejimite” is a word having a meaning close to “butter”.
  • step S ⁇ b> 122 the search method selection unit 216 refers to the specific expression storage unit 210 and identifies an action expression associated with “butter” estimated to have a meaning similar to the unknown word “vegetite”. In the example shown in FIG. 3, a plurality of behavioral expressions such as “buy” and “eat” are specified.
  • the search method selection unit 216 further refers to the search method storage unit 214 and specifies a search method associated with these action expressions. In the example shown in FIG. 4, since the search methods are associated with “buy” and “eat” among the behavioral expressions associated with “butter”, these search methods are selected. .
  • the search method selection unit 216 may select a plurality of search methods or one search method.
  • the search method storage unit 214 may select all the search methods associated with the target behavioral expression.
  • step S124 the search unit 218 follows the search method and the unknown word “vejimite”. Is used as a query to search a merchandise sales site, and information indicating the URL is transmitted to the mobile terminal 100. In the mobile terminal 100, this URL is displayed on the display unit 104.
  • the selection unit 108 selects a URL by an input from the user, a web page corresponding to the URL is displayed on the display unit 104. Thereby, it is possible to recommend an action of purchasing “Vegemite” on the Internet to the user.
  • the search unit 218 searches for a “Vegemite” merchandise sales site, and acquires a sales store and its position. Then, when a sales store exists within a radius of 100 m from the current position of the mobile terminal 100, information related to the sales store is displayed on the display unit 104 of the mobile terminal 100. This makes it possible to recommend the user the action of purchasing “Vegemite” at the store.
  • the search unit 218 searches for a restaurant site where “vejimite” can be eaten, and acquires the restaurant name and its position. Then, when a restaurant exists within a radius of 100 m from the current position of the mobile terminal 100, information about the restaurant is displayed on the display unit 104 of the mobile terminal 100. As a result, it is possible to recommend the user an action of eating “vejimite” at a restaurant.
  • the search unit 218 creates a search table 220 as shown in FIG. 6 based on the search method selected by the search method selection unit 216 and the unknown word.
  • the search table 220 latitude and longitude as location information of a store, recommended contents, and recommended conditions are associated with unknown words and behavioral expressions.
  • the search unit 218 periodically compares the current position information received from the mobile terminal 100 via the communication unit 202, the position information associated with the unknown word in the search table 220, and the recommendation condition, and recommends If the condition is met, the recommended content is transmitted to the mobile terminal 100 via the communication unit 202. That is, action recommendation is performed when the recommendation condition is met.
  • an action recommendation screen as shown in FIG. 7 is displayed on the display unit 104 of the mobile terminal 100.
  • the user since the map including the current position and the position of the store or restaurant and the information indicating the name of the store or restaurant are displayed, the user can obtain action recommendation information related to the unknown word “vejimite”. it can.
  • the behavior recommendation system when the unknown word is extracted, the appearance frequency of the behavior expression co-occurring with the specific expression stored in the specific expression storage unit 210 and the appearance of the behavior expression co-occurring with the unknown word. It is possible to specify a specific expression that is similar in frequency, and estimate the meaning of the specific expression to be close to the meaning of the unknown word. Furthermore, behavior recommendation suitable for the estimated meaning can be performed to the user. In other words, even when the meaning of an unknown word cannot be directly determined by language processing, it is possible to estimate the meaning based on the usage of the unknown word and perform appropriate action recommendation.
  • the appearance frequency specifying unit 206 is the target.
  • the unknown word storage unit 208 specifies the appearance frequency of the action expression associated with the unknown word (step S110). As described above, when an unknown word is already stored in the unknown word storage unit 208, the processing efficiency can be improved by using the information stored in the unknown word storage unit 208.
  • the mobile terminal 100 and the behavior recommendation device 200 may be provided integrally. That is, the mobile terminal 100 may extract unknown words, estimate the meaning of unknown words, and perform processing related to behavior recommendation suitable for the meaning.
  • the appearance frequency specifying unit 206 specifies the appearance frequency of the action expression using a web page or the like viewed by the user of the mobile terminal 100 as a target document, and the unknown word storage unit 208 For each user identification information for identifying a user of the terminal 100, an action expression and an appearance frequency for an unknown word may be stored. Thereby, the action expression and appearance frequency which co-occur on an unknown word within the range browsed by the user can be obtained. Therefore, it is possible to identify an appropriate action expression and make an appropriate action recommendation.
  • the behavior recommendation device 230 of the behavior recommendation system 2 further includes a situation information specifying unit 232.
  • the communication unit 202 of the behavior recommendation device 230 receives status information indicating that the user is on a train or the like in addition to the position information from the mobile terminal 100.
  • the mobile terminal 100 specifies whether or not the user is on the train based on the position information of the mobile terminal 100 obtained by GPS and the detection result by the acceleration sensor provided in the mobile terminal 100, and gets on the train. Generate status information indicating that it is in progress.
  • the communication unit 202 also receives, from the mobile terminal 100, browsing time when the user browsed the web page as status information.
  • the status information specifying unit 232 specifies the status information received by the communication unit 202 from the mobile terminal 100.
  • the situation information specifying unit 232 may further specify information held by itself such as the current time as the situation information.
  • the appearance frequency specifying unit 234 specifies the appearance frequency of the action expression using the situation information specified by the situation information specifying unit 232 as a query together with the unknown word. For example, status information such as “morning” in the morning and “train” when in the train is added to the query. This makes it possible to extract the appearance frequency of behavioral expressions related to the situation information, for example, “Vegemite” is often eaten in the morning from expressions such as “I ate vegemite in the morning”.
  • the appearance frequency specifying unit 234 further stores the appearance frequency of the action expression in the unknown word storage unit 236 in association with the situation information. That is, in the unknown word storage unit 236, as shown in FIG. 9, the behavioral expressions “use”, “put”, and “paint” for the unknown word “vejimite” have appearance frequencies “morning”, “daytime”, “night”. "Is stored in association with each status information. As a result, as shown in FIG. 9, the frequency of appearance of action expressions corresponding to the situation information is obtained, such that “paint” often co-occurs in the morning and “buy” often co-occurs in the daytime. be able to.
  • the appearance frequency of the action expression for the specific expression is stored in association with each situation information.
  • the meaning estimation unit 240 generates the appearance frequency of the action expression for the unique expression associated with the situation information that matches the current situation information, and the appearance of the action expression for the unique expression of the unknown word obtained by the appearance frequency specifying unit 234. Based on the frequency similarity, the meaning of the unknown word is estimated.
  • the search method selection unit 242 specifies an action expression associated with the current situation information in the specific expression storage unit 238. Then, the search method storage unit 214 selects the search method associated with the action expression.
  • an action recommendation related to “painting” such as presentation of a URL of a cooking recipe including “vejimite” associated with “painting” that often co-occurs with “morning” is provided. It can be carried out. In the daytime, it is possible to make an action recommendation such as presenting the URL of the store where “Vegemite” is associated with “Buy” that often co-occurs with “Day”.
  • the remaining configuration and processing of the behavior recommendation system 2 according to the second embodiment are the same as the configuration and processing of the behavior recommendation system 1 according to the first embodiment.
  • the behavior recommendation device includes a control device such as a CPU, a storage device such as a ROM (Read Only Memory) and a RAM, an external storage device such as an HDD and a CD drive device, and a display device such as a display device. It has an input device such as a keyboard and a mouse, and has a hardware configuration using a normal computer.
  • a control device such as a CPU
  • a storage device such as a ROM (Read Only Memory) and a RAM
  • an external storage device such as an HDD and a CD drive device
  • a display device such as a display device. It has an input device such as a keyboard and a mouse, and has a hardware configuration using a normal computer.
  • the behavior recommendation program executed by the portable terminal and the behavior recommendation device of the present embodiment is a file in an installable or executable format, and is a CD-ROM, flexible disk (FD), CD-R, DVD (Digital Versatile Disk). And the like recorded on a computer-readable recording medium.
  • the behavior recommendation program executed by the mobile terminal and the behavior recommendation device of the present embodiment may be provided by being stored on a computer connected to a network such as the Internet and downloaded via the network. good. Moreover, you may comprise so that the action recommendation program performed with the action recommendation apparatus of this embodiment may be provided or distributed via networks, such as the internet. Moreover, you may comprise so that the action recommendation program of this embodiment may be previously incorporated in ROM etc. and provided.
  • the behavior recommendation program executed by the behavior recommendation device is a module including the above-described units (communication unit, unknown word extraction unit, appearance frequency identification unit, meaning estimation unit, search method selection unit, search unit) and the like.
  • a CPU processor
  • the present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying the constituent elements without departing from the scope of the invention in the implementation stage.
  • various inventions can be formed by appropriately combining a plurality of constituent elements disclosed in the above embodiments. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, constituent elements over different embodiments may be appropriately combined.

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Abstract

Un dispositif de recommandation de comportement est pourvu d'une section d'identification de fréquence d'apparition (206) qui identifie les coefficients de pondération d'expressions de comportement d'un mot inconnu d'un document contenant le mot inconnu ; d'une section de mémorisation d'expression caractéristique (210) qui mémorise une expression caractéristique existante, les expressions de comportement de l'expression caractéristique, et les fréquences d'apparition des expressions de comportement en association ; d'une section d'estimation de signification (212) qui estime la signification du mot inconnu en fonction du degré de similarité entre les fréquences d'apparition des expressions de comportement du mot inconnu et celles des expressions de comportement des expressions caractéristiques ; d'une section de mémorisation de procédé de recherche (214) qui mémorise les expressions de comportement et recherche des procédés en association ; d'une section de sélection de procédé de recherche (216) qui identifie l'expression de comportement associée à la signification estimée dans la section d'estimation de signification (212), et sélectionne le procédé de recherche associé à l'expression de comportement dans la section de mémorisation de procédé de recherche (214) ; et d'une section de sortie qui délivre le résultat de recherche.
PCT/JP2009/063577 2009-07-30 2009-07-30 Dispositif de recommandation de comportement Ceased WO2011013229A1 (fr)

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