# Patent

## Title
Determining similar phrases

## Abstract

Techniques for determining phrases that are related or similar to a source phrase are described herein. These techniques may include analyzing a corpus of text to locate a first set of phrases that differ from a received source phrase by one word. The techniques may then combine these words of the first set of phrases that differ from the source phrase to create a second set of phrases. The techniques may then filter out a portion of the second set of phrases before outputting another portion for use as an identifier, for association with a user account or for any other use.

## Background
RELATED APPLICATIONS
This application is related to U.S. Provisional Application No. 60/823,611, filed on Aug. 25, 2006, and U.S. patent application Ser. No. 11/548,111, filed on Oct. 10, 2006, both entitled UTILIZING PHRASE TOKENS IN TRANSACTIONS and both incorporated herein by reference.
BACKGROUND
Companies utilizing e-commerce sites strive to make their sites easier for users to locate and purchase items. In order to ease users' ability to purchase items, for instance, these companies may configure their sites to accept many forms of payment. While many of these strategies have increased the effectiveness of these sites, companies continually strive to further enhance user experiences.



BRIEF DESCRIPTION OF THE DRAWINGS
The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different figures indicates similar or identical items.
FIG. 1 illustrates an example architecture that includes a phrase-generation service for generating phrases. Once the phrases are generated, some of the phrases may be suggested to one or more users.
FIG. 2 illustrates another example architecture that includes the phrase-generation service of FIG. 1. Here, the architecture includes a content provider that stores information about the users to whom the phrases are suggested.
FIG. 3 illustrates an example user interface served by the content provider of FIGS. 1 and 2. Here, the example user interface includes multiple phrases that the phrase-generation service of FIGS. 1 and 2 has generated.
FIG. 4 illustrates an example user interface after the user has entered words into a text box of the user interface from FIG. 3. As illustrated, in response to the entering of the words, the user interface now includes different phrases that are related to the entered words, as well as an indication that the entered words are available as a phrase.
FIG. 5 illustrates another example user interface after the user has entered words into a text box of the user interface from FIG. 3. Again, this user interface now includes different phrases that are related to the entered words, as well as an indication that the misspelled word may not be used as a part of a selected phrase.
FIG. 6 illustrates another example user interface after the user has entered words into a text box of the user interface from FIG. 3. Again, this user interface now includes different phrases that are related to the entered words, as well as an indication that the entered words have previously been selected as a phrase by another user.
FIG. 7 illustrates another example user interface served by the content provider of FIGS. 1 and 2. This interface illustrates each phrase that has been associated with a particular user.
FIG. 8 illustrates example components of the phrase-generation service of FIGS. 1 and 2.
FIG. 9 illustrates an example flow diagram for generating and suggesting phrases.
FIG. 10 illustrates a flow diagram of an example process for determining phrases that are related to statistically improbable phrases, each of which comprises a phrase that appears in a source more than a predetermined threshold number of times.
FIGS. 11-12 illustrate a flow diagram of an example process for determining one or more phrases that are related or similar to an inputted source phrase.
FIGS. 13-14 illustrate a flow diagram of another example process for determining one or more phrases that are related or similar to an inputted source phrase. Here, the process includes identifying one or more derived source phrases that comprise the inputted source phrase and at least one additional word.
FIGS. 15-16 illustrate a flow diagram of another example process for determining one or more phrases that are related or similar to an inputted source phrase. Here, the process includes identifying derived source phrases that comprise different portions of the inputted source phrase and one or more additional words.
FIG. 17 illustrates a flow diagram of yet another example process for determining one or more phrases that are related or similar to an inputted source phrase. Here, the process utilizes the process of FIGS. 11-12 and the process of FIGS. 13-14.
FIG. 18 illustrates an example feedback loop for learning characteristics about the phrases selected by users and, in response to the learning, altering the characteristics of phrases suggested to users.
FIGS. 19-26 illustrate example processes for employing the techniques described below.



DETAILED DESCRIPTION
This disclosure is directed, in part, to generating phrases. These generated phrases may be for association with an entity, such as a user or a user account. These phrases may additionally or alternatively be output for use as identifiers. In one particular instance, a user may select a generated phrase for association with the user and with an aspect of a user account that is used to execute a payment transaction. For instance, the selected phrase may be associated with a payment instrument associated with the user account, a location (e.g., a shipping address or a digital location) associated with the user account, a delivery method associated with the user account, or any other aspect of the user account. By associating the phrase in this manner, the user may then use the phrase to conduct transactions with use of the phrase. For instance, the user could purchase or receive an item from a merchant with the use of the phrase, or the user could similarly engage in any other sort of transaction with use of the phrase. As such, these generated phrases may operate similarly or the same as the transaction phrase tokens described in the related applications, incorporated by reference above.
In some instances, a phrase that is associated with a payment instrument is free from information identifying the aspect of the user account, such as the payment instrument associated with user account. Therefore, the user associated with the phrase may more freely share the phrase than an actual identifier of the payment instrument. That is, the user may more freely share the phrase that is associated with the payment instrument when compared with the sharing of credit card numbers, bank account numbers, gift card numbers, and the like.
In some instances, the generated phrases comprise a set of numeric or alphanumeric characters having a secondary meaning to the user (e.g., “Grace's Textbooks,” “Griffin's Toys,” etc.). Furthermore, in some instances, each of the generated phrases may comprise at least one word. In still other instances, each of the phrases may comprise between two and seven words, and may be free of numbers, symbols and the like. As such, these phrases may comprise a number of grammatically correct words.
To generate these phrases, the described techniques may analyze one or more sources. These sources may include books, magazines, online content, audio content, video content and/or any other source from which words may be extracted or assembled. With use of the sources, the described techniques may extract phrases or may extract words for use in creating phrases.
Once the first corpus of phrases has been created by analyzing the sources, the techniques may filter the phrases to create a second corpus of phrases comprising fewer phrases than the first corpus of phrases. While the phrases may be filtered in any number of ways, in some instances the techniques attempt to filter out phrases that users would perceive as less interesting, harder to remember, difficult to pronounce, and/or the like. As such, the second corpus of phrases may comprise phrases that make “good” phrases for selection. Stated otherwise, users may be more likely to perceive these phrases as interesting, easier to remember, or the like as compared to the filtered-out phrases.
After filtering and creation of the second corpus of phrases, the described techniques may suggest phrases to one or more users. These users may then, for instance, select one or more of these phrases for association with the user and, potentially, with a payment instrument of the user or another aspect of a user account. Conversely, these phrases may be used as a writing tool (e.g., to help an author with lyrics for a song, poem, or the like), as a naming tool (e.g., to help a user think of names for an entity, such as a band, business, restaurant, or the like) or in any other manner where phrase generation may be helpful.
In some instances, the techniques suggest phrases that are personalized to a user. For instance, the techniques may determine one or more keywords that are associated with a user. These keywords may be manually inputted by a user or the keywords may be determined based on known information about the user (e.g., name, address, purchase history, etc.). After determination of the keyword(s), the techniques may determine phrases of the second corpus of phrases that are associated with the keyword(s). After making this determination, the techniques may suggest one or more of these phrases. For instance, if a user is known to be a skier (and, hence, is associated with a keyword “ski”), then phrases such as “powder hound,” “black diamond,” and “Winter Olympics” may be suggested to the user.
In addition to generating and suggesting generated phrases, the techniques may also determine phrases that are related or similar to an inputted source phrase. This may include analyzing a corpus of text to locate a first set of phrases that differ from a received source phrase by one word. The techniques may then combine these words of the first set of phrases that differ from the source phrase to create a second set of phrases. The techniques may then filter out a portion of the second set of phrases before outputting another portion for use as an identifier, for association with a user account or for any other use as discussed immediately above.
The discussion begins with a section entitled “Illustrative Architectures,” which describes two non-limiting environments in which a phrase-generation service generates and provides phrases for user selection. A section entitled “Illustrative User Interfaces” follows. This section depicts and describes examples of user interfaces (UIs) that may be served to and rendered at the devices of the users of FIG. 1. Next, a section entitled “Illustrative Phrase-Generation Service” describes example components of a service that generates and provides phrases for use in the illustrated architectures of FIGS. 1 and 2 and elsewhere. A fourth section, entitled “Illustrative Flow Diagrams,” follows and illustrates example processes with reference to multiple sub-sections. These sub-sections include “Generating and Suggesting Phrases,” “Determining Related or Similar Phrases with an Index,” “Determining Related or Similar Phrases Based on Part-of-Speech Combinations” and “Implementing a Feedback Loop.” The discussion then concludes with a section entitled “Illustrative Processes” and a brief conclusion.
This brief introduction, including section titles and corresponding summaries, is provided for the reader's convenience and is not intended to limit the scope of the claims, nor the proceeding sections. Furthermore, the techniques described above and below may be implemented in a number of ways and in a number of contexts. Several example implementations and contexts are provided with reference to the following figures, as described below in more detail. However, the following implementations and contexts are but a few of many.
Illustrative Architectures
FIG. 1 illustrates an example architecture 100 in which phrases may be generated and suggested to one or more users. Users may then select one or more of these phrases for use as identifiers or otherwise. In some instances, a user may select a phrase for association with the user and with one or more aspects of a corresponding user account. In some instances, these one or more aspects may be helpful in allowing the user or another user to execute (e.g., initiate, complete, participate in, or otherwise aid) a payment transaction with use of the phrase. As such, the phrase may be associated with a payment instrument associated with the user or user account, a location associated with the user or user account, or a delivery method associated with the user or user account. As such, the user or another entity may conduct transactions with use of the phrase, while any cost associated with the transaction may be charged to the underlying associated payment instrument. As discussed in detail below, the selected phrase may be free from information identifying the associated aspect of the user account (e.g., the payment instrument) and may, in some instances, comprise one or more words that collectively have a secondary meaning to the user.
In the illustrated embodiment, the techniques are described in the context of users 102(1), . . . , (N) operating computing devices 1904(1), . . . , (N) to access a content provider 106 over a network 108. For instance, user 102(1) may use device 1904(1) to access provider 106 for purposes of consuming content offered by the content provider or engaging in a transaction with the content provider.
In architecture 100, content provider 106 may comprise any sort of provider that supports user interaction, such as social networking sites, e-commerce sites, informational sites, news and entertainment sites, and so forth. Furthermore, while the illustrated example represents user 102(1) accessing content provider 106 over network 108, the described techniques may equally apply in instances where user 102(1) interacts with the content provider over the phone, via a kiosk, or in any other manner. It is also noted that the described techniques may apply in other client/server arrangements, as well as in non-client/server arrangements (e.g., locally-stored software applications, set-top boxes, etc.).
Here, user 102(1) accesses content provider 106 via network 108. Network 108 may include any one or combination of multiple different types of networks, such as cable networks, the Internet, and wireless networks. User computing devices 1904(1)-(N), meanwhile, may be implemented as any number of computing devices, including as a personal computer, a laptop computer, a portable digital assistant (PDA), a mobile phone, a set-top box, a game console, a personal media player (PMP), and so forth. User computing device 1904(1) is equipped with one or more processors and memory to store applications and data. An application, such as a browser or other client application, running on device 1904(1) may facilitate access to provider 106 over network 108.
As illustrated, content provider 106 may be hosted on one or more servers having processing and storage capabilities. In one implementation, the servers might be arranged in a cluster or as a server farm, although other server architectures may also be used. The content provider is capable of handling requests from many users and serving, in response, various content that can be rendered at user computing devices 1904(1)-(N) for consumption by users 102(1)-(N).
Architecture 100 also includes a phrase-generation service 110 configured to generate and provide phrases for selection by users 102(1)-(N). These phrases may be used when users 102(1)-(N) engage in transactions (e.g., with content provider 106) or otherwise. As discussed above, in some instances, user 102(1) may select a phrase for association with the user and with one or more aspects of a user account, such as a payment instrument of the user or with another user or entity. User 102(1) may then use this phrase to conduct a transaction (e.g., purchase, rent, lease, or otherwise consume content) from content provider 106 and/or with other content providers.
As illustrated, phrase-generation service 110 includes one or more processors 112 as well memory 114. Memory 114 includes a phrase-generation module 116, a phrase-suggestion module 118 and a phrase-association module 120. Phrase-generation module 116 functions to analyze one or more sources 122 to determine phrases and/or to determine words for creation into phrases. Sources 122 may include books, newspapers, magazines, audio content, video content and/or any other source from which a corpus of text may be determined. Books may include, for instance, fiction books, non-fiction books, encyclopedias, dictionaries, and any other type of bound or electronic book. Once module 116 has created and/or mined a corpus of phrases, these phrases may be stored in a database 124 for suggestion. A database 126, meanwhile, represents that some of these generated phrase may be associated with users, as discussed below.
Phrase-suggestion module 118 takes phrases from database 124 and suggests one or more phrases to one or more users. In some but not all instances, phrase-suggestion module 118 suggests phrases that are personalized to the user to whom the phrases are suggested. FIG. 1, for instance, illustrates that computing device 1904(1) of user 102(1) renders a user interface 128 that includes three suggested phrases provided by module 118. While user interface 128 may include content provided by content provider 106, this interface may also include the phrases suggested by module 118 of phrase-generation service 110. Furthermore, in some instances, some or all of the phrases that user interface 128 illustrates have been personalized to user 102(1) who, in the illustrated example, is named Brian. The suggested phrases on user interface 128 include “Terminator,” “Brian the Plummer,” and “Perceived Lumberjack.” While module 118 visually suggests these phrases to user 102(1) in the current example, module 118 may suggest these phrases in any other manner in other implementations (e.g., orally over the phone, in person, via an email or text message, etc.).
FIG. 1 also illustrates that computing device 1904(2) of user 102(2) renders a user interface 130. Again, user interface 130 includes personalized phrases that phrase-suggestion module 118 provides. Here, module 118 suggests the following phrases for user 102(N), whose example name and city of residence is Susan and Atlanta, Ga., respectively: “Running Tomato,” “Fifth Concerto,” “Absurdly Agile,” and “Sleepless in Atlanta.”
In addition, FIG. 1 illustrates that memory 114 may store or otherwise have access to a similar-phrase module 132. Similar-phrase module 132 may function to receive a source phrase and determine one or more other phrases that are related or similar to the source phrase. As described below, module 132 may make this determination with use of a variety of techniques. Furthermore, module 132 may receive a source phrase in a number of ways, including directly from a user, in response to a user selecting a phrase or otherwise.
For instance, a user could input a particular source phrase to similar-phrase module 132 in hopes of receiving a set of similar phrases. Conversely, when a user selects a particular phrase, phrase-generation service 110 may assume that this chosen phrase was indeed an interesting or liked phrase, as illustrated by the selection of the phrase by the user. As such, service 110 may instruct module 132 to determine a set of phrases that are similar to this selected phrase. In still other instances, service 110 may ask one or more users to rate a sampling of phrases. Service 110 may then take high-ranking phrases and may use module 132 to locate more phrases similar to the highly-rated phrases. While a few examples have been listed, similar-phrase module 132 may determine phrases similar to a source phrase for a variety of similar or different reasons.
Returning to FIG. 1, once user 102(1) or user 102(N) select a phrase, phrase-association module 120 may associate this selected phrase with the corresponding user. Furthermore, in some instances, phrase-association module 120 associates the selected phrase with a payment instrument of the user. In still other instances, phrase-generation service 110 may not include phase-association module 120. Instead, phrase-generation service may merely generate and provide phrases for output for a variety of other reasons, such as for helping users choose an identifier (e.g., for a rock band), for helping users write poems or songs, or for any other reason. Furthermore, while content provider 106 and phrase-generation service 110 are illustrated in the current example as separate entities, provider 106 and service 110 may comprise the same entity or may employ similar or the same functionality in other embodiments. Furthermore, the described techniques themselves may be implemented in a vast number of other environments and architectures.
FIG. 2 illustrates another architecture 200 in which phrase-generation service 110 may generate and suggest or otherwise provide the generated phrases. In this example, users 102(1)-(N) select phrases suggested by service 110 for the purpose of conducting transactions with content provider 106. Furthermore, architecture 200 represents that phrase-generation service 110 may suggest phrases that are personalized to particular users based on information stored at or accessible by content provider 106. For instance, content provider 106 may have access to previously-known information about user 102(1), which service 110 may use to suggest phrases to user 102(1). In still other instances, service 110 may personalize these phrases based on information that user 102(1) manually or explicitly provides.
As illustrated, the servers of content provider 106 include one or more processors 202 and memory 204. Memory 204 stores or otherwise has access to an item catalog 206 comprising one or more items that provider 106 may provide to users 102(1)-(N) via a transaction with users 102(1)-(N). An item includes anything that the content provider wishes to offer for purchase, rental, subscription, viewing, informative purposes, or some other form of consumption. In some embodiments, the item may be offered for consumption by the content provider itself, while in other embodiments content provider 106 may host items that others are offering via the provider. An item can include a product, a service, a digital download, a news clip, customer-created content, information, or some other type of sellable or non-sellable unit.
Memory 204 also includes or has access to a user-accounts database 208. Database 208 includes information about users (e.g., users 102(1)-(N)) who have a user account at content provider 106 or who have otherwise engaged in some sort of transaction with provider 106. As discussed above, service 110 may suggest personalized phrases to users based on information stored in or accessible by user-accounts database 208.
An example user account 208(1) is associated with user 102(1) and, as illustrated, aspects of the user account may include a name of the user 210, one or more addresses 212 of the user, one or more phrases 214 that have previously been associated with the user, one or more payment instruments 216 of the user that have been associated with account 208(1), a purchase history or information about a purchase history 218 of the user, items that have been recommended or information about items that have been recommended 220 for the user, and lists or information about lists 222 that are associated with the user. Account 208(1) may also include one or more specified delivery methods 224 and/or one or more specified digital locations 226. It is specifically noted that while a few example aspects or elements of user account 208(1) have been provided other embodiments may provide more or fewer elements.
Here, user name 210 may specify the legal name of user 102(1) (e.g., “Brian Brown”). Additionally or alternatively, user name 210 may specify a screen name (e.g., “BBrown18”) that user 102(1) uses when communicating with content provider 106. Furthermore, addresses 212 may include a shipping address of user 102(1), a billing address of user 102(1), or any other address associated with user 102(1). Again, phrases 214 may include any phrases (e.g., “Brian's Fun Cash”) that have previously been associated with user account 208(1), as well as potentially with one or more of the user's associated payment instrument(s) 216.
Purchase history 218, meanwhile, may include information about previous transactions between user 102(1) and content provider 106. Purchase history 218 may also, potentially, include information about transactions between user 102(1) and other content providers. This information may include names of items that user 102(1) purchased, tags that users or provider 106 has associated with these items (and tags related to those tags), categories in item catalog 206 in which the purchased items reside, a time of the day or a season when user 102(1) typically purchases items, or any other information evinced explicitly or implicitly from the user's documented purchase history.
Similarly, recommended items 220 may include any information about items that other users, provider 106, or any other entity has previously recommended for user 102(1). Again, this information may include a name of the items, categories of the items, tags and related tags that are associated with the items, and any other information that may be evinced from these recommendations. Content provider 106 may store similar information about any lists 222 that user 102(1) has created or is otherwise associated with (e.g., names and categories of items on the lists, tags associated with the items and the lists, etc.).
Next, user account 208(1) may specify delivery method(s) 224 selected by the associated user. These methods may specify that the user prefers to send and/or receive items via standard mail, express mail, two-day mail, overnight mail, or via any other delivery method. Digital location(s) 226, meanwhile, may specify a location where the associated user wishes to receive (and/or send) digital items, such as to a particular computing device or location on a server. For instance, if the user purchases or is gifted a digital item, such as a song, digital location 226 may specify a device or destination at which the user would like to receive the song. Furthermore, digital location(s) 226 may specify where the user wishes to receive money that is gifted to the user. For instance, this may include a bank account, an online user account, or any physical or electronic location where the user may receive money.
With reference now to phrase-generation service 110, FIG. 2 illustrates that service 110 may maintain an index 228 that logically maps keywords to phrases of phrase database 124 (and/or of phrase database 126). These keywords may comprise the words that together comprise the generated phrases, as well any other word. As illustrated, index 228 maps the keyword “Aardvark” to “Wily Mammal,” “Burrowing a Shovel,” and “Earth Hog,” amongst others. Similarly, index 228 maps the keyword “Apple” to “Newtonian Physics,” “Spinning Core,” and “Funny Feisty Mango,” amongst others.
As discussed above, phrase-generation service 110 may suggest personalized phrases to users based on known information about the users or based on manual inputs from the users. For instance, content provider 106 may provide information about user 102(1) from any of the information stored in user account 208(1). Service 110 may then view this received information as one or more keywords for insertion into index 228. Service 110 may then map the keyword(s) to one or more phrases in the index, which service 110 may then suggest to user 102(1).
For instance, envision that purchase history 218 of user account 208(1) shows that user 102(1) has purchased a large amount of snow-skiing equipment from content provider 106. Envision also that this purchased equipment falls into categories of the item catalog entitled “sports” and “ski.” Also, the purchased equipment is associated with the tags “Powder Ski” and “downhill.” Upon receiving this information, phrase-generation service 110 may treat the information as keywords for insertion into index 228. For instance, service 110 may insert some or all of the received words as keywords into index 228 for the purpose of determining phrases that are associated with these keywords.
In some instances, service 110 ranks the returned phrases that are associated with the inputted keywords. For instance, service 110 may rank highest the phrases that are associated with each (or most) of the keywords while ranking lowest the phrases that are associated with only one of the keywords. Additionally or alternatively, service 110 may rank these phrases based on the technique employed to create the phrase, as discussed below. In any event, service 110 may output the personalized phrases to user 102(1), which are likely to relate to skiing given the keywords inputted into index 228.
While the above example discussed that service 110 could input keywords associated with purchase history 218, any other information known about user 102(1) may be similarly inputted. For instance, name 210 or address 212 of user 102(1) could be used as a keyword for insertion into index 228, as may be any information known about user 102(1), illustrated in user account 208(1) or otherwise.
Furthermore, phrase-generation service 110 may also provide personalized phrases based on manual or explicit input from users 102(1)-(N). For instance, user 102(1) may provide one or more keywords to service 110, which may then analyze index 228 to determine phrases that are associated with the provided keywords. For instance, if user 102(1) were to provide the keyword “Schwarzenegger,” service 110 may return the phrase “Terminator,” amongst others. In still other instances, service 110 may provide, to user 102(1), a mix of phrases that are personalized based on known information about user 102(1), personalized based on manually-inputted keywords, and phrases that are not personalized.
Illustrative User Interfaces
FIG. 3 illustrates an example user interface 300 served by content provider 106 of FIG. 2. Here, the example user interface includes multiple phrases 302 that phrase-generation service 110 of FIGS. 1 and 2 has generated. Furthermore, service 110 has personalized these generated and suggested phrases to user 102(1), although in other embodiments phrases 302 may not be personalized or these phrases may comprise a mix of personalized and non-personalized phrases.
In the current example, content provider 106 provided previously-known information about user 102(1) (from user account 208(1)) to service 110. As discussed above, service 110 inputted this information as keywords into index 228 in order to suggest phrases 302. As discussed above, user account 208(1) indicates that user 102(1) has previously purchased skiing items and, as such, may be interested in skiing generally. Phrase-generation service 110 has accordingly entered these skiing-related keywords into index 228 and has returned phrases 302 that relate to skiing (e.g., “Ski Bum,” “Black Diamond,” “Powder,” etc.).
User 102(1) may selected one of suggested phrases 302 or, conversely, may type one or more words into a text box 304 for use as a phrase. In the former instance, when user 102(1) selects a phrase from phrases 302, the selected phrase may appear in text box 304. Additionally, the words that compose the selected phrase may themselves be inserted as keywords into index 228 of service 110, and the illustrated list of phrases 302 may change to reflect phrases that are related to the new keywords, as discussed in detail below.
Similarly, if user 102(1) types one or more words into text box 304, then service 110 may again input these words as keywords into index 228. Alternatively, service 110 may provide these words to similar-phrase module 132 for the purpose of determining and presenting similar phrases using the techniques described below with reference to FIGS. 10-17. Similar to the discussion above, in the example of inputting these words into index 228 the list of phrases 302 may change to reflect phrases that are related to the newly-inputted keywords. Again, the following figures and accompanying discussion illustrate and describe this in detail.
Once user 102(1) has typed or selected a phrase that he or she wishes to keep, user 102(1) may select an icon 306 (entitled “Add this Phrase to Your Account”) and the phrase that currently resides in text box 304 may be associated with user account 208(1). In certain embodiments, this phrase may also be associated with one or more payment instruments (e.g., payment instruments 216 or one or more other payment instruments) as described U.S. Provisional Application No. 60/823,611 and U.S. patent application Ser. No. 11/548,111.
In these instances, user 102(1) may use the associated phrase as a proxy for the associated or linked payment instrument. For example, this phrase may link with a payment instrument (e.g., a credit card) of user 102(1) or some other person or entity. Therefore, user 102(1) (and potentially one or more other users) may employ this phrase as a payment method for future purchases or other types of transactions. Additionally, user 102(1) (or some other user) may choose to associate certain rules to the selected phrase. These rules may, for instance, dictate when and how user 102(1) (and, potentially, other users) may use the phrase for purchases or otherwise. As illustrated, user interface 300 includes an area 308 where user 102(1) may decide whether the selected phrase can be used to both place and receive orders, or whether the phrase can only be used to receive gifts from other people.
Some rules may dictate which user actually controls the phrase (e.g., the person associated with the underlying payment instrument, such as the credit card holder). For instance, a mother may create a phrase for her daughter (“Grace”) entitled “Grace's Textbooks”. Once the mother creates or approves creation of the phrase, Grace may then specify this phrase as a payment method. By identifying this phrase as a payment method, the daughter thus identifies the mother (i.e., the person associated with the linked payment instrument) as the payer for these purchases.
Similar to the discussion above, this phrase may be associated with predefined rules. For instance, the mother may create a rule that pre-approves purchases of certain categories of items, such as textbooks. The mother may also employ other rules, such as dollar amounts, time limits, and the like. In these instances, when the daughter uses the phrase as a payment method, the transaction processing service 110 may compare the parameters of the requested purchase with the rules associated with the phrase. The service may then complete or cancel the requested purchase according to the phrase's rules. Conversely or additionally, content provider 106 may contact the user that controls use of the phrase (here, the mother) to request that he or she approve or deny the requested purchase. The user that controls the use of the phrase may, in some cases, be requested to authenticate themselves in some way, such as through a username and password in order to approve the transaction of the token user. However, because these phrases, such as phrases 302 shown in user interface 300, may merely be a string of characters that is free from information identifying a linked payment instrument, these phrases may be more freely shared than compared with other types of payment instrument identifiers, such as credit card numbers, bank account numbers, and the like.
After user 102(1) selects a rule from area 308, user 102(1) may also enter a default shipping address 310 and a default payment method 312 for association with the selected phrase. Default shipping address 310 specifies a default location where content provider 106 (or other content providers) should ship purchased items that user 102(1) (or another user) purchases with use of the selected phrase. Default payment method 312 similarly specifies a default payment instrument for use in these transactions. Finally, user interface 300 includes an area 314 that allows user 102(1) to choose where a content provider should send approval messages in response to these transactions, should an explicit approval be needed.
FIG. 4 illustrates a user interface 400 served by content provider 106 after user 102(1) has typed words (“Trail Running Preferred”) into text box 304. In response, phrase-generation service 110 has inserted these entered words as keywords into index 228 and has mapped these keywords to generated phrases. Service 110 has then provided these phrases 402 that are related to the entered keywords for rendering on user interface 400. As illustrated, phrases 402 tend to relate to some portion of “Trail Running Preferred,” as they include “Amazingly Swift,” “Unending Marathon,” “Nervous Shoes” and others. Again, phrase-generation service 110 may also determine and present other similar phrases based on the techniques described below with reference to FIGS. 10-17.
User interface 400 also includes an indication 404 that the words that user 102(1) entered into text box 304 are themselves available as a phrase. That is, user 102(1) may choose to associate the phrase “Trail Running Preferred” with user account 208(1) via selection of icon 306 (“Add this Phrase to Your Account”). Conversely, user 102(1) may choose to select one of phrases 402 for insertion into text box 304, in which case the user could then associate this phrase with user account 208(1) via selection of icon 306. User 102(1) could also choose to select an even newer set of phrases that are presented in response to the selection of the phrase from phrases 402, and so on.
FIG. 5 illustrates another user interface 500 that content provider 106 serves in response to user 102(1) entering “Fields of Onions” into text box 304. Again, user interface 500 includes a list of phrases 502 that relate to the entered words. However, user interface also includes an indication 504 that the word “fields” may not be used as a part of a phrase, as this string of characters does not comprise a grammatically correct English word. As such, user 102(1) may need to type the proper spelling (“Fields”) into text box 304 if user 102(1) wishes to use “Fields of Onions” as a phrase. Of course, while some embodiments require that selected phrases comprise grammatically correct words, other embodiments may allow for any string of alphanumeric characters. In some instances, phrases comprise at least two words separated by a space (as shown in FIGS. 3-5), while in other instances these words may not be separated (e.g., “SkiBum”).
FIG. 6 illustrates another user interface 600 that content provider 106 serves in response to user 102(1) entering “Brian Brown” into text box 304. Similar to the discussion above, user interface 600 includes a list of phrases 602 related to the entered words. However, user interface also includes an indication 604 that the phrase “Brian Brown” has been taken by another user and, as such, user 102(1) may not select this phrase. As discussed in detail below, phrase-generation service 110 may also not suggest or allow selection of a phrase that sounds the same or similar to a taken phrase.
FIG. 7 illustrates an example user interface 700 entitled “Your Phrases,” that content provider 106 serves in response to user 102(1) choosing to view his or her phrases 702. As illustrated, user 102(1) has five phrases associated with user account 208(1), with each phrase being associated with a particular payment instrument. With use of user interface 700, user 102(1) may choose to edit settings associated with one or more of his or her associated phrases. User 102(1) may also choose to “Add a New Phrase” to user account 208(1) via selection of an icon 704.
Illustrative Phrase-Generation Service
FIG. 8 illustrates example components of phrase-generation service 110 of FIGS. 1 and 2. As illustrated and as discussed above, service 110 includes phrase-generation module 116, phrase-suggestion module 118, phrase-association module 120 and similar-phrase module 132. Service 110 may also include a phrase-filtering module 802 and a feedback engine 804.
Phrase-generation module 116 functions to generate phrases, as discussed above, and store these generated phrases in a first corpus of phrases 806 that is stored on or accessible by service 110. Phrase-filtering module 802 functions to filter out one or more phrases from the first corpus, thus defining a second corpus of phrase 808 that typically comprises fewer phrases than first corpus 806. Phrase-suggestion module 118 may then suggest phrases of second corpus 808 to user 102(1)-(N), while phrase-association module 120 may associate selected phrases with these users. Finally, feedback engine 804 may monitor the characteristics of the phrases actually being selected by users 102(1)-(N) and, in response, cause phrase-suggestion module 118 to suggest phrases that more closely match these characteristics.
Phrase-generation module 116 may generate first corpus of phrases 806 by a variety of techniques. As illustrated, module 116 includes a phrase-mining module 810 and a phrase-construction module 812. Phrase-mining module 810 functions to mine multiple sources to determine phrases within the sources. Phrase-construction module 812, meanwhile, uses words found within the sources to construct phrases for storage in first corpus 806. In each instance, the sources from which the modules determine these phrases and words may include books, magazines, newspapers, audio content, video content or any other source that may comprise a corpus of text.
To find mined phrases, Phrase-mining module 810 includes a contiguous-words module 814 and a statistically-improbable-phrase (SIP) module 816. Contiguous-words module 814 analyzes corpuses of text from the sources to find phrases comprising two or more contiguous words. In some instances, module 814 locates words that are directly contiguous and, as such, are free from separation by punctuation. Module 814 may then store some or all of the determined phrases within first corpus 806. In one embodiment, contiguous-words module 814 locates phrases that comprise between two and seven directly-contiguous words.
For example imagine that module 814 mines a book that includes the following sentence: “The night was cold and dreary; the wind howled through rattling trees.” Here, module 814 may determine and store the following phrases within corpus of phrases 806:

    
    
        The Night
        The Night Was
        The Night Was Cold
        The Night Was Cold And
        The Night Was Cold And Dreary
        Night Was
        Night Was Cold
        Night Was Cold And
        Night Was Cold And Dreary
        Was Cold
        Was Cold And
        Was Cold And Dreary
        Cold And
        Cold And Dreary
        And Dreary
        The Wind
        The Wind Howled
        The Wind Howled Through
        The Wind Howled Through Rattling
        The Wind Howled Through Rattling Trees
        Wind Howled
        Wind Howled Through
        Wind Howled Through Rattling
        Wind Howled Through Rattling Trees
        Howled Through
        Howled Through Rattling
        Howled Through Rattling Trees
        Through Rattling
        Through Rattling Trees
        Rattling Trees
    
    


As this list illustrates, module 814 may mine phrases comprising contiguous words that are not separated by punctuation. By doing so, module 814 is more likely to determine phrases that make grammatical sense, and each of the phrases comprise words that have been placed next to one another in an authored work. In instances where the source comprises an edited work, such as book, magazine, or the like, module 814 may additionally rely on the fact that a copy editor or the like has proofread this source and, as such, may determine to a high degree of certainty that the determined phrases comprise words that make sense when placed next to one another.
Next, SIP module 816 mines one or more sources to determine whether any of these sources contain one or more statistically improbable phrases (SIPs). Statistically improbable phrases comprise phrases of two or more words (possibly capped at a number, such as seven) that occur in a corpus of text a statistically improbable number of times. For instance, a biography about Beethoven may contain the phrase “Fifth Concerto” a statistically improbable number of times. As such, SIP module 816 may determine that “Fifth Concerto” is a SIP for this biography and, as such, may store this and other SIPs in first corpus of phrases 806.
Phrase-construction module 812, meanwhile, constructs phrases from words and includes a database of templates 818, a part-of-speech (POS) identifier 820 and a class identifier 822. Database 818 may include varying types of templates, such as part-of-speech templates and personalized templates. Each part-of-speech template may comprise one or more placeholders, with each placeholder specifying a part-of-speech. With this template, phrase-construction module 812 may apply words having the associated part(s)-of-speech to the template to create one or more phrases. To do so, module 812 includes a part-of-speech (POS) identifier 820 to identify a part of speech for each word mined from a source.
For instance, if module 812 extracts the word “running” from a source, POS identifier 820 determines that this word comprises a verb and, more specifically, that this word comprises a gerund. With use of these POS or POS/class templates, module 812 may construct some or all of the possible phrases based on the templates and based on all of the words that module 812 has extracted from sources. Furthermore, because phrase-generation module 116 may analyze dictionaries, module 812 may create all or nearly all of the possible two-word phrases, three-word phrases, and so on.
With this in mind, envision that database 818 includes the following template that specifies the following placeholders:

    
    
        [verb] [noun]
    
    


Phrase-construction module 812 may apply words having associated parts of speech to this template to create one or more phrases. For instance, module 812 may create (and store in corpus 806) phrases such as “Running Chair,” “Dancing Pencil,” “Vault Phone,” “Act Hat.”
Additionally, some or all of the part-of-speech templates within database 818 may include one or more placeholders that specify a describable class either in lieu of in or in addition to a particular part of speech. This describable class may specify any sort of characteristic that, when taken together with a part of speech, specifies a subset of the words associated with that particular part of speech. As such, phrase-construction module 812 may include a class identifier 822 that functions to categorize words within respective classes. Class identifier 822 may categorize these words based on lists that an operator of service 110 has manually entered, by reference to sources such as encyclopedias or dictionaries, or by any other suitable manner.
With this in mind, envision that database 818 specifies the following templates:

    
    
        [gerund that describes movement] [noun that describes a fruit]
        [noun that describes an occupation] [adverb] [verb that describes eating]
        [preposition] the [noun that describes an article of clothing]
    
    


With use of the first template, module 812 may construct the following phrases, among many others: “Walking Mango,” “Flying Pineapple,” “Jogging Orange,” and “Rolling Apple.” With use of the second template, module 812 may construct, among many others, the following phrases: “Seamstress Quickly Bites” and “Plumber Patiently Chews.” Finally, with use of the third template, module 812 could construct the following phrases “Beneath the Sweater,” “Against the Clogs,” or “During the Poncho.”
In some embodiments, multiple different part-of-speech and/or part-of-speech and class combinations may be made based at least in part on the parts of speech listed below in Table 1.











TABLE 1







Abbreviation:
Part of Speech:
Examples:









CC
Conjunction,
and, or




coordinating




CD
Adjective, cardinal
3, fifteen




number




DET
Determiner
This, each, some



EX
Pronoun, existential there
There



FW
Foreign words




IN
Preposition/Conjunction
for, of, although,





that



JJ
Adjective
happy, bad



JJR
Adjective, comparative
happier, worse



JJJS
Adjective, superlative
happiest, worst



LS
Symbol, list item
A, A.



MD
Verb, modal
can, could, 'll



NN
Noun
aircraft, data



NNP
Noun, proper
London, Michael



NNPS
Noun, proper, plural
Australians,





Methodists



NNS
Noun, plural
women, books



PDT
Determiner, prequalifier
quite, all, half



POS
Possessive
‘s, ‘



PRP
Determiner, possessive
mine, yours




second




PRPS
Determiner, possessive
their, your



RB
Adverb
often, not, very,





here



RBS
Adverb, superlative
Fastest



RBR
Adverb, comparative
Faster



RP
Adverb, particle
up, off, out



SYM
Symbol
“



TO
Preposition
To



UH
Interjection
oh, yes, mmm



VB
Verb, infinitive
take, live



VBD
Verb, past tense
took, lived



VBG
Verb, gerund
taking, living



VBN
Verb, past/passive
taken, lived




participle




VBP
Verb, base present form
take, live



VBZ
Verb, present 3SG -s
takes, lives




form




WDT
Determiner, question
which, whatever



WP
Pronoun, question
who, whoever



WPS
Determiner, possessive &
Whose




question




WRB
Adverb, question
when, how,





however



PP
Punctuation, sentence
., !, ?




ender




PPC
Punctuation, comma
,



PPD
Punctuation, dollar sign
&



PPL
Punctuation, quotation
“




mark left




PPR
Punctuation, quotation
”




mark right




PPS
Punctuation, colon,
;, . . . , -




semicolon, ellipsis




LRB
Punctuation, left bracket
(, {, [



RRB
Punctuation, right bracket
), }, ]










Furthermore, Table 2 illustrates illustrative parts-of-speech and class combinations that may be used in creating POS/class templates.








TABLE 2





Combinations:
Description:







noun.act
nouns denoting acts or actions


noun.animal
nouns denoting animals


noun.artifact
nouns denoting man-made objects


noun.attribute
nouns denoting attributes of people and objects


noun.body
nouns denoting body parts


noun.cognition
nouns denoting cognitive processes and contents


noun.communication
nouns denoting communicative processes and 



contents


noun.event
nouns denoting natural events


noun.feeling
nouns denoting feelings and emotions


noun.food
nouns denoting foods and drinks


noun.group
nouns denoting groupings of people or objects


noun.location
nouns denoting spatial position


noun.motive
nouns denoting goals


noun.object
nouns denoting natural objects (not man-made)


noun.person
nouns denoting people


noun.phenomenon
nouns denoting natural phenomena


noun.plant
nouns denoting plants


noun.possession
nouns denoting possession and transfer of possession


noun.process
nouns denoting natural processes


noun.quantity
nouns denoting quantities and units of measure


noun.relation
nouns denoting relations between people or things 



or ideas


noun.shape
nouns denoting two and three dimensional shapes


noun.state
nouns denoting stable states of affairs


noun.substance
nouns denoting substances


noun.time
nouns denoting time and temporal relations


adj.all
all adjective clusters


adj.pert
relational adjectives (pertainyms)


adv.all
all adverbs


verb.body
verbs of grooming, dressing and bodily care


verb.change
verbs of size, temperature change, intensifying, etc.


verb.cognition
verbs of thinking, judging, analyzing, doubting


verb.communication
verbs of telling, asking, ordering, singing


verb.competition
verbs of fighting, athletic activities


verb.consumption
verbs of eating and drinking


verb.contact
verbs of touching, hitting, tying, digging


verb.creation
verbs of sewing, baking, painting, performing


verb.emotion
verbs of feeling


verb.motion
verbs of walking, flying, swimming


verb.perception
verbs of seeing, hearing, feeling


verb.possession
verbs of buying, selling, owning


verb.social
verbs of political and social activities and events


verb.stative
verbs of being, having, spatial relations


verb.weather
verbs of raining, snowing, thawing, thundering


adj.ppl
participial adjectives









While Table 1 lists illustrative parts of speech that may be used to create templates within template database 818, it is to be appreciated that other embodiments may use more or fewer parts of speech. Similarly, while Table 2 lists illustrative part-of-speech/class combinations, other embodiments may use many other such combinations.
With these illustrative tables in mind, in one embodiment phrase-construction module 812 uses the following the parts-of-speech templates to construct phrases comprising parts-of-speech and/or parts-of-speech/class combinations:

    
    
        [JJ] [NN]
        [JJ] [NNS]
        [NN] and [NNS]
        [NN] and [NN]
        [RB] [VRB]
        [VRB] the [NNS]
        [VRB] the [NN]
    
    


In addition to POS and POS/class templates, database 818 may also include one or more personalized (PRSLZD) templates. These templates may include one or more placeholders and/or one or more fixed or standard words. For instance, a personalized template may comprise one of the following:

    
    
        [user's first name]'s Cash Barrel
        Sleepless in [user's city of shipping address]
        I love [keyword associated with user]
        [keyword associated with user] [user's first name]
    
    


With reference to the first template, module 812 could reference user account 208(1) to determine the first name of user 102(1). After doing so, module 812 could construct the phrase “Brian's Cash Barrel” for suggestion and/or association with user 102(1). Similarly, module 812 could analyze a user account associated with user 102(N) to determine that this user's shipping address is in Atlanta, Ga. Module 812 could then construct a phrase “Sleepless in Atlanta” for this user. With reference to the third template, meanwhile, module 812 could construct a phrase “I Love Skiing” for a user who has previously purchased skis. Finally, the module could also construct the phrase “Skier Steve” for this same user, if his name were Steve.
Returning attention to FIG. 8, once phrase-generation module 116 has created first corpus of phrases 806, phrase-filtering module 802 may filter out a portion of these phrases to define second corpus 808. In some instances, phrase-filtering module 802 attempts to filter out less-desirable phrases from users' standpoints to define a corpus of “good” phrases. That is, module 802 attempts to filter out phrases such that the remaining phrases are perceived, on the whole, as more interesting to most users than compared to the filtered-out phrases. It is specifically noted that while the illustrated embodiment of phrase-generation service 110 filters out phrases, in other embodiments service 110 may instead positively identify “good” phrases. Furthermore, in some instances, certain phrases of first corpus 806 are not subject to the filtering process. In one embodiment, SIPs and personalized templates are not processed through at least a portion of the filtering process.
As illustrated, phrase-filtering module 802 includes a part-of-speech (POS) filter 824, a blacklist filter 826, a multidimensional filter 828 and a whitelist filter 830. Part-of-speech filter 824 functions to filter out at least a portion of the phrases of first corpus 806 based on the part-of-speech combinations of the phrases. That is, POS filter 824 may include a POS list that either specifies which POS combinations should be filtered out or a list that specifies which POS combinations should not be filtered out (in which case POS filter 824 may filter out those phrases whose combination does not fall on the list). In either instance, POS filter 824 removes a great many phrases of first corpus 806. Part-of-speech filter 824 may recognize the POS combinations of the phrases with use of existing algorithms and statistical models, such as the hidden Markov model (HMM).
Table 3, below, lists examples of templates that may be considered “good” POS combinations in some embodiments. Of course, templates that result in “good” phrases may vary based on context. In this instance, POS filter 824 may compare each phrase of first corpus 806 and may filter out those phrases that do not have a POS combination listed in Table 3.










TABLE 3







CD-NNS
IN-NNS
IN-PRP
JJ-NN


JJ-NNS
JJ-VBG
JJR-NN
JJR-NNS


JJS-NN
JJS-NNS
NN-NN
NN-NNS


NN-POS-NN
NN-POS-NNS
NNS-POS-NN
NNS-POS-NNS


NNS-VB
NNS-VBD
NNS-VBG
NNS-VBN


NNS-VBP
PRPS-NN
PRPS-NNS
RB-JJ


RB-JJR
RB-JJS
RB-RBR
RB-VBD


RB-VBG
RB-VBN
RB-VBZ
RBR-NN


RBR-NNS
RBR-NNS
RBR-VBG
TO-VB


UH-NN
VB-NNS
VB-RB
VB-RB-VB


VBD-NN
VBD-NNS
VBD-RB
VBG-JJR


VBG-NN
VBG-NNS
VBG-RB
VBN-JJS


VBN-NN
VBN-NNS
VBN-RB
VBZ0NN


VBZ-RB
WPS-NNS
CD-IN-CD
CD-JJ-NNS


CD-JJS-NNS
CD-NN-NNS
CD-NNS-JJR
CD-NNS-RBR


CD-VBD-NNS
CD-VBG-NNS
CD-VBN-NNS
DET-JJ-NN


DET-JJ-NNS
DET-JJS-NN
DET-JJS-NNS
DET-NN-NN


DET-NN-POS-
DET-VBG-NN
DET-VBG-NNS
DET-VBZ-RB-


NNS


NN


JJ-CC-JJ
JJ-JJ-NN
JJ-JJ-NNS
JJ-JJ-UH


JJ-JJR-NNS
JJ-JJR-RB
JJ-JJS-NN
JJ-NN-NN


JJ-NN-NNS
JJ-NN-POS-NN
JJ-NN-POS-NNS
JJ-NN-POS-VB


JJ-PRP-VBP-
JJ-PRP-VBZ-JJ
JJ-TO-VBP
JJR-CC-JJR


NN





JJR-IN-NN
JJR-JJ-NN
JJR-JJ-NNS
JJR-NN-NN


JJR-NN-NNS
JJR-NN-POS-
JJR-NN-POS-
JJR-NNS-POS-



NN
NNS
NNS


JJR-RB-JJ
JJR-RB-RB
JJR-TO-NNS
JJR-WRB-JJ


JJS-CC-JJS
JJS-IN-NN
JJS-JJ-NN
JJS-JJ-NNS


JJS-NN-NN
JJS-NN-NNS
JJS-NN-POS-NN
JJS-NN-VBP-





VBN


JJS-RB-JJ
JJS-TO-JJ
JJS-TO-VB
JJS-VBD-NNS


JJS-VBG-NN
JJS-VBN-NN
MD-NN-VB
MD-NNS-NN


MD-NNS-VB
MD-NNS-VBP
MD-PRP-NN
MD-PRP-VB


MD-RB-RB-JJ
MD-RB-RB-
MD-RB-VB
MD-RB-VB-JJ



VBP




MD-RB-VB-
MD-RB-VB-
MD-RB-VB-
MD-RB-VB-


JJR
NN
NNS
VBG


MD-RB-VB-
MD-RB-VBP
MD-RB-VBP-NN
MD-VB-RB-


VBN


VBD


MD-VB-RB-
MD-VB-VBN-
MD-VB-VBN-JJ
MD-VB-VBN-


VBN
CD

JJR


MD-VB-VBN-
MD-VB-VBN-
MD-VB-VBN-
MD-VBP-NNS


NN
NNS
VBN



NN-CC-NN
NN-CC-NNS
NN-IN-NNS
NN-NN-POS-





NN


NN-POS-CD-
NN-POS-JJ-NN
NN-POS-JJR-NN
NN-POS-JJR-


NNS


NNS


NN-POS-JJS-
NN-POS-JJS-
NN-POS-NN-NN
NN-POS-RB-JJ


NN
NNS




NN-POS-RB-
NN-POS-VBD-
NN-POS-VBG-
NN-POS-VBG-


NN
NN
JJR
NNS


NN-POS-VBN-
NN-RB-VB-JJ
NN-RB-VB-NN
NN-RB-VB-


NN


NNS


NN-VBG-UH
NN-VBZ-NNS
NN-VBZ-RB-JJ
NN-VBZ-RB-





VBG


NN-WP-VBZ-
NNS-CC-NNS
NNS-DET-VBZ-
NNS-IN-NNS


JJ

NN



NNS-JJS-NN
NNS-JJS-NNS
NNS-MD-RB-
NNS-NN-MD-




VBP
VBD


NNS-NN-POS-
NNS-NN-VBP-
NNS-POS-CD-
NNS-POS-JJ-


NN
VBG
NNS
NN


NNS-TO-VBP
NNS-VBG-NN
NNS-VBP-RB-JJ
NNS-VBP-RB-





NNS


PRP-JJ-UH
PRP-MDNN-
PRP-VBD-RB-
PRP-VBD-RB-



NN
NNS
VB


PRP-VBN-NNS
PRP-VBP-JJ-
PRP-VBP-JJR-JJ
PRP-VBP-NN-



NNS

NNS


PRP-VBP-
PRP-VBP-
PRP-VBP-RB-
PRP-VBP-RB-


PRPS-NN
PRPS-NNS
JJR
NNS


PRP-VBP-RB-
PRP-VBP-VBG-
PRP-VBZ-PRPS-
PRP-VBZ-RB-


VBG
NN
NN
JJ


PRP-VBZ-RB-
PRP-VBZ-RB-
PRP-VBZ-RB-
PRPS-JJ-NN


JJR
NN
NNS



PRPS-JJ-NNS
PRPS-JJ-UH
PRPS-JJS-NNS
PRPS-NN-NNS


PRPS-NN-
PRPS-NN-POS-
PRPS-NNS-NN
PRPS-VBD-NN


POS-NN
NNS




PRPS-VBG-NN
RB-CC-RB
RB-JJ-NN
RB-JJ-NNS


RB-JJR-VBD
RB-JJR-VBN
RB-MD-VB-NNS
RB-RB-JJ


RB-RB-VBN
RB-RBR-NN
RB-VB-RB-VBN
RB-VB-RB-





VBP


RB-VBD-RB-
RB-VBD-RB-
RB-VBP-RB-JJ
RB-VBP-RB-


JJ
NNS

VBN


RBR-CC-JJR
RBR-IN-NNS
RBR-NN-NN
TO-VBD-NN


TO-VBP-RB
UH-IN-JJ
UH-NN-POS-NN
UH-TO-NN


VB-CC-VB
VB-DET-NNS
VB-DET-UH
VB-DET-VBZ-





NNS


VB-JJ-NNS
VB-JJR-NNS
VB-NN-NNS
VB-NN-POS-





NN


VB-NN-POS-
VB-NNS-POS-
VB-NNS-POS-
VB-NNS-RB


NNS
NN
NNS



VB-NNS-RBR
VB-PRP-MD-
VB-PRP-VBP-JJ
VB-PRP-VBP-



NN

NN


VB-PRP-VBP-
VB-PRP-VBZ-
VB-PRPS-NN
VB-RB-JJ-RB


NNS
JJ




VB-RB-NNS-
VB-RB-PRP-
VB-RB-RB-JJ
VB-RB-RB-


VBP
VB

VBN


VB-RB-VB-
VB-RB-VBD-
VB-RB-VBD-
VB-VBN-RBS


NNS
NN
NNS



VB-WP-VBZ-
VB-WP-VBZ-
VBD-CC-VBD
VBD-NN-JJR


JJ
VBN




VBD-NNS-
VBD-RB-JJ-NN
VBD-RB-JJ-RB
VBD-RB-VB-JJ


POS-NNS





VBD-RB-VB-
VBG-CC-VBG
VBG-CD-NNS
VBG-DET-NN


NNS





VBG-DET-UH
VBG-IN-NNS
VBG-JJR-NNS
VBG-NN-MD-





NN


VBG-NN-MD-
VBG-NN-POS-
VBG-NN-POS-
VBG-NN-VBP-


VBD
NN
NNS
NN


VBG-NN-VBP-
VBG-NNS-
VBG-PRP-RBR
VBG-PRP-


NN
POS-NNS

VBP-JJ


VBG-PRP-VBP-
VBG-PRP-
VBG-PRP-VBZ-
VBG-PRPS-NN


NN
VBZ-JJ
NN



VBG-PRPS-
VBG-RB-NNS
VBG-RB-RB
VBG-RB-VBZ-


NNS


NNS


VBG-RBR-NNS
VBG-UH-NN
VBG-VB-VBZ-
VBG-VBD-




NN
NNS


VBG-VBN-NNS
VBG-VBN-RBR
VBG-VBZ-NNS
VBG-VBZ-RB-





JJ


VBG-WP-VBZ-
VBN-CC-VBN
VBN-DET-NNS
VBN-IN-NNS


VBN





VBN-JJR-NNS
VBN-JJS-NNS
VBN-NN-POS-
VBN-NN-POS-




NN
NNS


VBN-PRP-JJR
VBN-PRP-
VBN-PRP-VBZ-
VBN-PRPS-NN



VBP-JJ
NN



VBP-RB-NNS-
VBP-RB-PRP-
VBP-RB-PRP-
VBP-RB-PRP-


JJ
JJ
VBD
VBG


VBP-RB-VBG-
VBZ-JJ-NNS
VBZ-PRP-VBP-JJ
VBZ-PRP-


NN


VBZ-JJ


VBZ-RB-PRP-
VBZ-RB-RB-
VBZ-RB-VBG-JJ
VBZ-RB-VBG-


JJ
VB

NN


VBZ-RB-VBG-
VBZ-WP-
WDT-VBD-RB-
WP-JJ-NN


NNS
VBZ-JJ
VB



WP-JJ-NNS
WP-VBZ-JJ-NN
WP-VBZ-JJR
WP-VBZ-JJR-





JJ


WP-VBZ-JJS
WP-VBZ-NN
WP-VBZ-NN-
WP-VBZ-NN-




POS-NN
RB


WP-VBZ-NNS
WP-VBZ-
WP-VBZ-RBS-JJ
WP-VBZ-VBG-



PRPS-NN

JJ


WP-VBZ-JJS
WP-VBZ-NN
WP-VBZ-NN-
WP-VBZ-NN-




POS-NN
RB


WP-VBZ-NNS
WP-VBZ-
WP-VBZ-RBS-JJ
WP-VBZ-VBG-



PRPS-NN

JJ


WPS-JJ-NN
WPS-JJ-NNS
WRB-NN-MD-
WRB-NN-




NN
VBP-JJ


WRB-NN-VBP-
WRB-NN-
WRB-NNS-VB
WRB-PRP-


VBG
VBP-VBN

VBP-JJ


WRB-PRP-
WRB-VBZ-
WRB-VBZ-
WRB-VBZ-


VBP-NNS
JJ-NN
PRPS-NN
PRPS-NNS









While Table 3 illustrates a few POS combinations that may be identified as good combinations, it is specifically noted that this list is merely illustrative. Furthermore, because phrases of the first and second corpuses may comprise any number of words (one or more), POS filter 824 may employ POS lists for each length of phrases. In one particular embodiment, phrases of the first and second corpuses of phrases are two to seven words in length.
Phrase-filtering module 802 further includes blacklist filter 826. Filter 826 may filter out phrases from first corpus 806 that the blacklist specifies or phrases that include words that appear on the blacklist. For instance, this filter may filter out trademarks, slogans, vulgarities, foreign words, or any other type of word or phrase that service 110 wises to specify for exclusion from second corpus 808.
For instance, blacklist filter 826 may filter out phrases that have already been taken. That is, filter 826 may filter out phrases that have already been associated with a user. In these instances, filter 826 may additionally include a homophone detector 832. Homophone detector 832 functions to determine all possible pronunciations of taken phrases and compare these pronunciations to all possible pronunciations of phrases that have not yet been associated with users. If any of the pronunciations sound the same or so similar as to cause confusion, then filter 826 may also filer out the phrases that sound similar to the already-taken phrases.
Multidimensional filter 828, meanwhile, functions to filter out phrases based on one or more of a variety of factors. In some instances, multidimensional filter 828 scores each phrase of the first corpus of phrases based on one or more of these factors. Filter 828 then filters out those phrases that score below a threshold or may use this scoring in other ways. For instance, filter 828 may use these scores as weights in order to filter out more phrases having lower scores than compared with higher scores.
As illustrated, filter 828 may include a phrase-commonality module 834, a word-commonality module 836, a word-count module 838, a readability module 840, a scoring module 842 and a ranking module 844. Phrase-commonality module determines, for each phrase of the first corpus, a commonality of the phrase. Each determined commonality may be relative to one another and may be based on how often the phrase is found during the original mining of the multiple sources (e.g., books, magazines, etc.) by phrase-generation module 116. Similarly, word-commonality module 836 may determine a commonality for each word of each phrase and assign a score to each phrase based on the determined word commonalities. Next, word-count module 838 may determine a number of words that compose each phrase of first corpus 806. Readability module 840 may determine a readability of the phrase based on, for instance, a number of syllables in the phrase and/or a number of syllables per word of the phrase. In some instances, this readability analysis may be based on indexes such as the Flesch-Kincaid Index or the Coleman-Liau Index.
With this information (and potentially other information), scoring module 842 may assign a score to each phrase of the first corpus. Ranking module 844 may then rank the phrases based on the determined scores. Filter 828 may filter out phrases based on the ranking or, conversely, may positively identify phrases to pass through to second corpus 808.
Because filter 828 is multidimensional, the scores of the phrases that are filtered out and/or the scores of the phrases that are passed through may be based on experimental data. For instance, phrase-generation service 110 may initially provide phrases to a plurality of test users to determine which phrases those users find interesting. Envision, for instance, that these users generally find uncommon, easy-to-read phrases comprising two common words to be interesting, while also finding four-word, difficult-to-read phrases with uncommon words uninteresting. In this example, ranking module 844 may rank much higher phrases that have scores similar to the former interesting phrases as compared with phrases that score close to the latter phrases.
Stated otherwise, the scores that scoring module 842 provides may segregate the phrases of first corpus 806 into regions. Ranking module 844 may then rank discrete regions or may rank these phrases along a continuous curve. In either instance, phrases failing in regions determined to be generally non-interesting may be filtered out (or weighted less) than phrases falling in regions determined to generally produce interesting phrases.
While illustrated filter 828 is shown to score phrases based on phrase and word commonality, word count and readability, other implementations may use more or fewer parameters. For instance, in some implementations filter 828 factors in a source of the phrase. That is, the filter may take into account the technique used to generate the particular phrase. For instance, if the testing users appeared to prefer SIPs to constructed phrase, then scoring module 842 may score SIPs higher than constructed phrases. This analysis may also be true within a particular generation technique. For instance, if users preferred one POS/class template over another, then constructed phrases built with this particular template may score higher than the latter constructed phrases (and, hence, service 110 may suggest these phrases more often).
Other factors that filter 828 may take into account include a longest consonant run of a phrase, whether the phrase has two or more words that begin with the same letter (alliteration), the length of the phrase in characters, or any other factor that may influence perceived interestingness of a phrase to users.
In some embodiments, the most relevant factors of an interestingness of a phrase include the technique that generated the phrase, the commonality of the phrase, the commonality of the word(s) of the phrase, the readability of the phrase, and a word count of the phrase. In one set of testing, the following testing results shown in Table 4 were found. In these results, the “technique” column represents the generation technique, while the “number of observations” represents the number of users that rated phrases that fall into the corresponding bucket. The interestingness column, meanwhile, represents the percentage of these phrases perceived as interesting to the testing users.













TABLE 4






Phrase
Word

Word
# of



Technique
Commonality
Commonality
Readability
Count
Observations
Interestingness





















Contiguous
common
common
hard
2
139
37%


SIP
common
common
easy
2
1839
33%


SIP
uncommon
common
easy
2
145
32%


Constructed
common
common
easy
2
106
32%


Contiguous 
common
common
easy
2
1813
29%


Contiguous
uncommon
common
easy
2
241
29%


SIP
common
common
hard
2
737
26%


Contiguous
common
common
easy
3
1120
25%


Contiguous 
common
common
easy
 4+
835
24%


Constructed
uncommon
common
easy
2
425
24%


Constructed
uncommon
common
hard
2
147
24%


Constructed
uncommon
common
easy
3
112
22%


Contiguous 
common
uncommon
easy
2
386
21%


SIP
common
common
easy
3
622
20%


SIP
common
uncommon
easy
2
851
20%


Contiguous
uncommon
common
easy
3
457
20%


SIP
uncommon
common
easy
3
189
18%


Contiguous
uncommon
common
easy
 4+
673
18%


SIP
common
uncommon
hard
2
673
17%


Contiguous 
common
uncommon
easy
3
133
17%


Constructed
uncommon
uncommon
easy
2
2044
16%


Constructed
uncommon
uncommon
hard
2
1304
15%


SIP
common
uncommon
easy
3
121
14%


Constructed
uncommon
uncommon
easy
3
628
14%


SIP
uncommon
uncommon
hard
2
267
13%


Contiguous 
uncommon
uncommon
easy
3
214
13%


SIP
common
common
hard
3
242
12%


SIP
uncommon
uncommon
easy
2
302
12%


Contiguous
uncommon
uncommon
easy
2
144
12%


Contiguous 
uncommon
uncommon
easy
4+
161
12%


SIP
common
uncommon
hard
3
110
 5%









In this particular instance, main observations of the testing were the following:

    
    
        Constructed phrases may do well if they use common words;
        SIPs of two words may be more interesting that three-word SIPs;
        Common phrases containing common words may generally be perceived as interesting at a relatively high rate;
        Uncommon phrases with uncommon words may rarely be perceived as interesting; and
        Hard-to read phrases that are based on two or more contiguous words may be deemed as interesting at a relatively high rate.
    
    


Furthermore, Table 5 lists four types of phrases that scoring module 842 may score well in some instances, as well as example phrases that reside within these buckets. Of course, it is noted that these types of phrases may or may not score well in other instances.








TABLE 5





Attributes
Example Phrases







Two or more contiguous words,
Political Firewall


common phrase, common words, high 
Presidential Elections


syllables per word, two words
Management Prowess



Political Realism



Unimaginably Violent


SIPs, common phrase, common
Divine Speaker


words, low syllables per word, 
White Cobra


two words
Paper Machine



Sickly Skin



Cash Reservoir


SIPs, uncommon phrase, common 
Contrary Grace


words, low syllables per word, 
Abstaining Monks


two words
Therapist Judges



Chance Rhyme



Crummy Golfers


Constructed, common phrase, 
Purchased Review


common words, low syllables 
Focused Cash


per word, two words
Butter Jelly



Collected Wealth



Makeshift Gear









Returning to FIG. 8, phrase-filtering module 802 also includes whitelist filter 830. In some instances, filtering module 802 may compare each word of a phrase to the whitelist filter to ensure that the word appears on a whitelist. If a phrase includes one or more words that do not appear on the whitelist, then filter 830 may filter out these phrases. In some instances, whitelist filter 830 includes each English language word (and/or potentially one or more other languages). As such, filter 830 checks to ensure that phrases being passed through to second corpus 808 are indeed English words (or words of the proper language, depending on context). Additionally, proper names, such as the names of users who have accounts with content provider 106, may also be added to this whitelist.
Once phrase-filtering module 802 has filtered out phrases from first corpus 806 to define second corpus 808, phrase-generation service 110 may create index 228 that maps keywords to the phrases of second corpus 808. As discussed above in regards to FIG. 2, index 228 allows phrase-suggestion module 118 to provide personalized phrases to a user by inserting into the index keywords that are associated with the user.
In some instances, service 110 may build index 228 in a manner that is reverse from how service 110 will actually use the index. That is, service 110 may create index 228 by mapping each phrase of second corpus 808 to one or more keywords. After association of the keywords to the phrases, the index is sorted by keywords such that each keyword maps to one or more phrases. Therefore, when phrase-suggestion module 118 enters one or more keywords into index 228, the index returns one or more phrases for suggestion.
To begin creation of index 228, one or more keywords are associated with a first phrase, such as “Dancing Pencil.” The first keywords that are associated with this phrase are the words of the phrase itself. Therefore, “Dancing” is associated with “Dancing Pencil,” as is “Pencil.” Next, the stemming variations of those words are related as keywords to the phrase. In the instant example, “Dance,” “Dances,” “Danced,” “Pencils,” and “Penciled” may be associated with “Dancing Pencil.”
Third, words that are related to “Dancing” and “Pencil” to a certain degree become associated with “Dancing Pencil.” These related words may include synonyms, hypernyms, and hyponyms. For instance, “pen,” “marker,” and “highlighter” may become associated with “Dancing Pencil,” as may “graphite,” “mechanical,” and “erasable.” In some instances, a compound synonym for a word may also be related to the phrase (e.g., “non-finite” is a compound synonym of “infinite”).
Next, service 110 searches for sources (e.g., books, magazines, audio content, etc.) that is related to the set of words of the phrase itself (here, “Dancing Pencil”). Once these books are found, service 110 determines words that are important to the book based on the fact that the words appear more than a threshold number of times. Service 110 then relates these words to the phrase. For example, “Dancing Pencil” could be related to a book having keywords “Ballet” and “Opera” (which appear in the book more than a threshold number of times, for instance). As such, service 110 would associate these words with “Dancing Pencil.”
At this point, each phrase within second corpus 808 is associated with one or more keywords. As such, the relationships are turned around to form index 228 such that each keyword of index 228 maps to one or more phrases. Furthermore, service 110 may determine a strength of a relationship between each keyword/phrase combination and may organize index 228 accordingly. For instance, a phrase that most strongly relates to a keyword may appear first in response to entering this keyword into index 228. In other instances, service 110 may apply a stronger weight to keyword/phrase relationship based on the ranking, such that the phrases that phrase-suggestion module 118 returns in response to the entering of one or more keywords into index 228 is more likely to include phrases having strong relationships with the entered keywords. In still other instances, certain types of phrases (e.g., SIPs) are also given artificially high scores regardless of these phrases' relationship strengths to the entered keywords.
Once service 110 has created second corpus 808 and index 228, phrase-suggestion module 118 may suggest or otherwise provide phrases to one or more users. Module 118 may suggest phrases randomly, may suggest phrases from each of multiple buckets, may suggest personalized phrases, or may suggest some combination thereof
As illustrated, phrase-suggestion module 118 may include a keyword module 846 and a pseudo-SIP module 848. Keyword module 846 functions to determine one or more keywords associated with a user and, in response, determine phrases that are related to the user. Module 118 may then suggest these personalized phrases to the user. As discussed above, keyword module 846 may determine keywords based on previously-known information about the user or based on manual or explicit input received from the user.
In either instance, keyword module 846 may insert the determined keywords into index 228 to determine related phrases. In instances where keyword module 846 inserts more than one keyword into index 228, module 846 may analyze index 228 for phrases that are associated with each of the entered words. Those phrases that are associated with each of the entered words may be deemed most-related to the entered keywords, while those phrases only related to one of the related keywords will be deemed less related. As such, phrase-suggestion module may suggest the most related phrases or may provide more of the heavily-related phrases than when compared to the tenuously- or less-related phrases.
Pseudo-SIP module 848, meanwhile, serves to determine phrases that are associated with statistically improbable phrases. In some instances, users find SIPs to be particularly interesting phrases. Therefore, Pseudo-SIP module 848 uses the interesting nature of these SIPs in order to find an even great number of interesting phrases. To do so, module 848 may insert the words of one or more SIPs into index 228 in order to determine phrases that are related to these SIPs (known as “pseudo SIPs). FIG. 10 and an accompanying discussion illustrate and describe this in detail below. Similar to SIPs, phrase-suggestion module 118 may assign a larger weight to these pseudo SIPs so that module 118 is more likely to output these types of phrases.
Once module 118 suggests or otherwise provides phrases, users such as user 102(1) may choose to select a phrase for association with the user and, potentially, with one or more aspects of a user account (such as a payment instrument of the user or the like). In response to receiving selections, phrase-association module 120 assigns the selected phrase to the user and possibly with one or more aspects of the user account.
Phrase-generation service 110 is also shown to include similar-phrase module 132. As discussed above, Similar-phrase module 132 may function to receive a source phrase and determine one or more other phrases that are similar to the source phrase. As described below, module 132 may make this determination with use of a variety of techniques, including those discussed below with reference to FIGS. 10-17.
Finally, phrase-generation service 110 is shown to include feedback engine 804. Feedback engine 804 functions to track user selections of the phrases, determine characteristics of the phrases being selected, modify the criteria that generates second corpus 808 and that suggests phrases from second corpus 808 to users. FIG. 18 and an accompanying discussion illustrate and describe feedback engine 804 in detail below.
Illustrative Flow Diagrams
Generating and Suggesting Phrases
FIG. 9 illustrates an example flow diagram of a process 900 for generating and suggesting phrases in the manners discussed above. At operation one (1), phrase-generation module 116 analyzes multiple sources to generate multiple phrases. Module 116 may mine these sources to determine phrases within the sources, or module 116 may construct phrases based on words found within these sources.
At operation two (2), phrase-generation module stores these generated phrases in first corpus of phrases 806. At operation three (3), phrase-filtering module 802 receives phrases from first corpus 806. Phrase-filtering module 802 may filter these phrases via some or all of the methods described above. For instance, module 802 may filter these phrases according to determined part-of-speech combinations, according to a blacklist and/or whitelist of words or phrases, and/or based on one or more other factors. In the latter instance, module 802 may filter out phrases based on an estimated perceived interestingness of the phrases. As such, multidimensional filter 828 may score, rank and filter out phrases based on a phrase commonality, word commonality, readability, and word length, potentially amongst other factors.
At operation four (4), phrase-filtering module 802 provides phrases that have not been filtered out to define second corpus of phrases 808. Due to the filtering, second corpus 808 typically comprises fewer phrases than first corpus of phrases. Second corpus of phrases 808 may also be more likely, in some instances, to contain “good” or “interesting” phrases. At operation five (5), phrases of second corpus of phrases 808 may be mapped to one or more keywords to create index 228.
At operation six (6), phrase-suggestion module 118 may receive one or more keywords associated with a particular user for use in suggesting personalized phrases for the user. Phrase-suggestion module 118 may receive these keywords from information previously know about the user, such as via a user account at content provider 106 or otherwise, or module 118 may receive these keywords from the user directly. For instance, the user could enter in one or more keywords into user interface 300 as discussed above in regards to FIG. 3.
Next, operation seven (7) represents that keyword module 846 of phrase-suggestion module 118 inserts these received keywords into index 228. In response, index 228 maps the inserted keywords to one or more phrases and returns these phrases to phrase-suggestion module 118 at operation eight (8). Finally, at operation nine (9), phrase-suggestion module 118 suggests some or all of the received phrases to one or more users. In response to receiving a selection of a phrase, phrase association module 120 may associate the selected phrase with the user and with one or more aspects of a user account (e.g., a payment instrument associated with the user account, a location associated with the user account (a shipping address, digital location, etc.), a delivery method associated with the user account, etc.). As discussed above, however, users may use these phrases for other reasons, such as for use as identifiers of other entities, for help in writing a book or a poem, or in any other manner. \
Determining Related or Similar Phrases with an Index
FIG. 10 illustrates an example flow diagram of a process 1000 for determining phrases that are related to statistically improbable phrases (i.e., for determining pseudo SIPs). As discussed above, SIPs comprise phrases that occur in a corpus of text a statistically-probable number of times. Furthermore, users typically rate these phrases as interesting at a higher rate than compared to phrases generated by other techniques. Process 1000, therefore, attempts to determine more phrases that have similar characteristics to SIPs.
Process 1000 includes operation 1002, which represents determining one or more SIPs that are associated with a particular user. For instance, if a user has previously purchased a biography about Beethoven, then operation 1002 may determine that the SIP “fifth concerto” is related to the user. Similarly, if content provider 106 recommends the Lord of the Rings book to the user, then operation 1004 may determine that the SIP “One Ring” is associated with the user. In another example, the user may manually input keywords, such as “Harry Potter,” in which case operation 1002 may determine that the user is now associated with the SIP “Lightning Scar.”
Next, operation 1004 determines the words that compose the determined SIPs. Operation 1006, meanwhile, inserts these determined words as keywords into index 228 to determine phrases that are associated with these words. In some instances, phrases that are associated with the greatest number of the input keywords are deemed to be the most related, and so on. If certain phrases are associated with a same number of inputted keywords, then strengths of these relationships may determine which of these phrases is more related to the entered keywords than the other.
Operation 1008 then ranks the phrases that operation 1006 has mapped to the keywords. In the illustrated example, operation 1006 inserted four keywords (W1, W2, W3 and W4) into index 228. Also as illustrated, index 228 maps only a single phrase (P7) to each of the four keywords. As such, operation 1008 ranks this phrase as the most-related phrase. The other related phrases, meanwhile, are ranked in descending order according to a number of keywords that the phrases map to and, if necessary as a tiebreaker, a strength of those relationships. Again, while this represents one illustrative ranking method, other implementations may rank the related phrases (i.e., the “pseudo SIPs”) in other manners.
Finally, operation 1010 outputs a portion or all of the determined phrases for suggestion to the user. As such, this user receives phrases that are likely to relate to information in which the user is interested. These phrases are also likely to contain SIP-like qualities, as Pseudo-SIP module 848 determined these phrases based on SIPs. Operation 1010 may output these phrases randomly, according to their rank, or otherwise.
While, FIG. 10 illustrates a process for determining phrases that are associated with statistically improbable phrases, the described techniques may similarly determine phrases that are associated with any one or more random or pre-selected phrases. For instance, the described techniques allow for determining phrases that are associated with a set of phrases that share a common characteristic.
To give an example, envision that a user is determined to be associated with fruit (e.g., via the user's purchase history or otherwise) and, as such, the techniques desire to determine phrases that are associated with fruit. In a manner similar to the discussion of FIG. 10 above, the techniques may determine a first set of phrases that are associated with fruit. Next, the words that compose each phrase of the first set of phrases may be determined. These words may then be inputted as keywords into an index (e.g., index 228) that maps keywords to phrases in order to determine a second set of phrases that are associated with the first set of phrases.
The phrases that are associated with these keywords (i.e., the phrases of the second set of phrases) may then be determined and, potentially, ranked. For instance, a phrase that is associated with each inputted keyword may be ranked more highly than a phrase that is only associated with a single inputted keyword. Finally, phrases of the second set of phrases may be output for suggestion to a user, for use an identifier or otherwise. In the instant example, the second set of phrases that may be output for suggestion to the user will likely somehow relate to fruit, which, again, is a theme associated with the user. As such, the described techniques took a first set of phrases related to fruit and outputted for suggestion a second set of phrases that are related to the first set of phrases (and, as such, are likely related to fruit as well).
Furthermore, while the first set of phrases may comprise phrases associated with a common theme (e.g., fruit), these phrases may share any other common characteristic. For instance, the first set of phrases may comprise phrases that were mined from a common source, that were generated in a common manner (e.g., templatized, constructed, mined, etc.), that have the same number of words, or that share any other common feature. To give another example, phrases that are mined from a common source, such as a particular website or webpage, may be entered into the index in order to find related phrase. For instance, the techniques could mine the website to determine phrases that are related to the phrases of the website (and, hence, potentially to the website itself).
For instance, the techniques could find statistically improbable phrases from the website and/or could determine phrases based on two or more contiguous words used on the website. The techniques could then determine the words that compose these phrases, before determining a second set of phrases that map to these words. This second set of phrase may, therefore, represent the content of the website to a certain degree. Furthermore, as the website (or other source) changes with time, the second set of phrases that relate to the website may also change.
Determining Related or Similar Phrases Using Part-of-Speech Combinations
In addition to using index 228 to locate phrases that are similar or related to in inputted source phrase, the described techniques may instead reference part-of-speech combinations of the source phrase to find similar or related phrases. The described techniques may locate these similar phrases in a variety of manners. FIGS. 11-16 describe a first manner, while FIG. 17 describes yet another manner that leverages the techniques described in FIGS. 11-14.
FIG. 11 illustrates a process 1100 for determining one or more phrases that are related or similar to a particular inputted source phrase. A computing device may implement process 1100 in whole or in part, as discussed above. In some instances, process 1100 may locate a set of phrases that each has a similar connotation to the source phrase or otherwise evokes similar emotions in a user as the source phrase. For instance and as discussed in detail below, when a user requests a set of phrases that are similar to a source phrase “Beautiful Summer Morning,” process 1100 may output phrases such as “Late Sunday Morning,” “Crisp Autumn Night,” “Early Friday Evening” and others. As the reader appreciates, each of these determined similar phrases may resonate with the user as having a similar connotation as the inputted source phrase “Beautiful Summer Morning.”
Process 1100 begins at operation 1102, at which point similar-phrase module 132 receives a source phrase and determines a part-of-speech combination of the source phrase. Similar-phrase module 132 may receive this source phrase directly from a user (e.g., user 102(1)) that desires to receive a set of phrases that are similar to the source phrase. Conversely, phrase-generation service 110 may automatically provide the source phrase to similar-phrase module 132 for determination of similar phrases in response to user 102(1) selecting the source phrase. That is, service 110 may deduce that the selected phrase was an interesting or likable phrase (given that the user selected the phrase) and, as such, service 110 may find more phrases similar to that interesting phrase. While a few non-limiting examples have been provided, similar-phrase module 132 may receive the source phrase in many other ways and for many other purposes.
As discussed immediately above, similar-phrase module 132 may also determine a part-of-speech combination of the source phrase at operation 1102. Module 132 may determine this combination itself or, in other instances, module 132 may request that a separate service determine this combination. In either instance, module 132 or the separate service may determine a number of words in the phrase, respective positions of the words in the phrase, and a part-of-speech of each word in order to determine the part-of-speech combination.
In the illustrated example, the received source phrase is “Beautiful Summer Morning”. In response to receiving this source phrase, module 132 determines that this phrase comprises three words in a respective first, second and third position. Similar-phrase module 132 (or the separate service) also determines that, within the context of this phrase, the term “Beautiful” comprises an adjective (JJ). Module 132 may also determine that the second term, “Summer,” and the final term, “Morning,” each comprises a noun (NN). As such, similar-phrase module 132 determines that the received source phrase comprises the following part-of-speech combination: “JJ-NN-NN”.
In some instances, module 132 may attempt to locate similar phrases that have a same part-of-speech combination as the received source phrase. That is, if user 102(1) requests phrases that are similar to the phrase “Beautiful Summer Morning,” similar-phrase module 132 may return, in response, phrases that are similar to this phrase and that have a part-of-speech combination of “JJ-NN-NN”.
As such, operation 11904 represents that similar-phrase module 132 attempts to find a first set of phrases that differ by at least one word from the source phrase, but maintain the same part-of-speech combination as the source phrase. In some instances, module 132 attempts to locate phrases that differ from the source phrase by exactly one word. Here, for instance, module 132 attempts to locate a first set of phrases that have the following characteristics:
<Adjective> Summer Morning;
Beautiful <Noun > Morning; and
Beautiful Summer <Noun>
To find these phrases, similar-phrase module 132 or another service may analyze one or corpuses of text. For instance, module 132 or another service may analyze sources 122 from FIGS. 1 and 2. As discussed above, sources 122 may include books, newspapers, magazines, audio content, video content and/or any other source from which a corpus of text may be determined. Books may include, for instance, fiction books, non-fiction books, encyclopedias, dictionaries, and any other type of bound or electronic book.
In the illustrated example, similar-phrase module 132 or another service may analyze the corpuses of text to locate phrases having the characteristics discussed immediately above. For instance, module 132 may attempt to locate phrases that begin with a noun and end with “Summer Morning.” Module 132 may find phrases such as “Late Summer Morning”, “Hot Summer Morning”, “Cool Summer Morning” and the like. In these instances, the words “Late”, “Hot” and “Cool” comprise substitutes for the original word in the first position, “Beautiful”.
Module 132 may also attempt to locate phrases that begin with “Beautiful”, end with “Morning” and have a substitute word comprising a single noun there between. For instances, the searched corpuses of text may include phrases such as “Beautiful Fall Morning”, “Beautiful Spring Morning”, “Beautiful Monday Morning” and the like. Here, the words “Fall”, “Spring” and “Monday” comprise substitute words for the original word at the second position, “Summer”.
Finally, module 132 (or another service, as discussed above) may attempt to locate phrases that begin with “Beautiful Summer” and end with a substitute word comprising a noun. As such, similar-phrase module 132 may find phrases such as “Beautiful Summer Day”, “Beautiful Summer Night”, “Beautiful Summer Dress”, and the like. The words “Day”, “Night” and “Dress” each comprises a substitute for the original word in the third position, “Morning”.
Next, operation 1106 generates a second set of phrases by combining substitute words determined at operation 11904. In some instances, similar-phrase module 132 combines the substitute words in a manner that maintains the positions of the substitute words. For instance, the substitute words found by searching for phrases that begin with an adjective and end with “Summer Morning” may be associated with a first position in the phrase, and may maintain that position in the generated phrases. In addition, similar-phrase module 132 may create some or every available combination of phrases based on the substitute words (while maintaining their respective positions).
To illustrate, the example above found phrase having the following substitute words at the following respective positions:
First position: Late, Hot and Cool;
Second position: Fall, Spring and Monday; and
Third position: Day, Night and Dress.
With these example substitute words, similar-phrase module 132 may create the following second set of phrases at operation 1106:
Late Fall Day
Late Fall Night
Late Fall Dress
Late Spring Day
Late Spring Night
Late Spring Dress
Late Monday Day
Late Monday Night
Late Monday Dress
Hot Fall Day
Hot Fall Night
Hot Fall Dress
Hot Spring Day
Hot Spring Night
Hot Spring Dress
Hot Monday Day
Hot Monday Night
Hot Monday Dress
Cool Fall Day
Cool Fall Night
Cool Fall Dress
Cool Spring Day
Cool Spring Night
Cool Spring Dress
Cool Monday Day
Cool Monday Night
Cool Monday Dress
After generating the second set of phrases, operation 1108 represents that similar-phrase module 132 determines a part-of-speech combination for each phrase in the set. While operation 11904 searched for phrases comprising substitute words having the appropriate part-of-speech, in some instances a pre-supposed part-of-speech combination for a phrase may be different than the actual combination of the phrase, due to the context of the phrase. Once the part-of-speech combination for each phrase is determined, similar-phrase module 132 filters out phrases whose part-of-speech combination differs from the part-of-speech combination of the original source phrase.
Next, operation 1110 represents that module 132 takes the remaining phrases and determines if these phrases are found within the corpuses of text (e.g., from sources 122). Similar-phrase module 132 then filters out those phrases that do not appear a predetermined number of times within the corpuses of text. In some instances, module 132 filters out those phrases that do not appear at all within the corpuses of text, while refraining from filtering out those phrases that appear at least once, at least twice, at least five times, at least a thousand times, or at least any other predetermined number of times. In some instances, module 132 performs operation 1110 to identify phrases that very likely make sense and are grammatically correct. That is, module 132 may assume that because a particular phrase appears within a corpus of text (a predetermined number of times), that phrase likely is grammatically correct and may be used in everyday language. For this reason, module 132 may search one or more published corpuses of text in some instances.
In addition or in the alternative, operation 1112 may determine if each word of the phrase appears within the corpuses of text at least a predetermined number of times (e.g., once, twice, five times, ten thousand times, etc.). Operation 1112 may then filter out those phrases that include one or more words that do not appear in the corpuses of text at least the predetermined number of times.
FIG. 12 continues the illustration of process 1100, and includes ranking the remaining phrases at operation 1112 before outputting the phrases at operation 1114. While FIG. 12 illustrates that process 1100 includes ranking the phrases and outputting them at least in part based on the ranking, some implementations may refrain from ranking the phrases and may instead output the phrases randomly or in any other manner.
In instances where process 1100 does rank phrases, meanwhile, this ranking may be done in a number of ways. In one implementation and as illustrated, operation 1112 ranks the phrases according to a pointwise mutual information score (or “mutual information score”) of each respective phrase. Mutual information is a measure of how much one independent variable tells us about another independent variable. Here, similar-phrase module may measure the mutual information score by determining how often the respective phrase appears in the book compared to the individual words of the phrase. In some instances, phrases with a higher mutual information score (e.g. “Yellow Banana”) may rank higher than phrases with a relatively lower mutual information score (e.g., “Yellow Camera”). In some instances, module 132 may measure mutual information (MI) scores as follows:

MI(W1,W2. . . Wk)=Log2(Pr(W1,W2. . . Wk)/(Pr(W1)*Pr(W2)* . . . *Pr(Wk)

where Wk is the kth word of a phrase
Finally and as discussed above, operation 1114 outputs at least a portion of the phrases, possibly based on the ranking of the phrases. In some instances, these phrases are output for association with a user or a user account, for use as an identifier or for any other reason as discussed throughout the document. In one example, when module 132 (and/or another entity) employs process 1100 for the source phrase “Beautiful Summer Morning”, module 132 may return the following list of phrases:
1. Late Monday Afternoon
2. Early Friday Morning
3. Hot Sunday Afternoon
4. Hot Summer Afternoon
5. Closed Sunday Evening
6. Late Sunday Morning
7. Crisp Autumn Afternoon
8. Late One Evening
9. Sunny Summer Afternoon
10. Late Sunday Night
11. Last Saturday Night
12. Bright Summer Morning
13. Early Tuesday Morning
14. Last Monday Night
15. Last Friday Night
16. Beautiful Summer Evening
17. Beautiful Spring Morning
18. Sunny Sunday Afternoon
19. Sunny Saturday Afternoon
Again, the reader will appreciate that each phrase of the outputted list may be deemed similar to the source phrase by a user who inputs the source phrase “Beautiful Summer Morning”.
FIG. 13 illustrates another example process 1300 that similar-phrase module 132 may implement in whole or in part for locating phrases that are related or similar to a particular source phrase. Again, module 132 may implement process 1300 in response to receiving a request from a user, in response to a user selecting a particular phrase for association with an account of the user or for any other similar or different reason. In some instances, module 132 implements process 1300 when an inputted source phrase consists of two words, although module 132 may similarly implement process 1300 for phrases comprising any other number of words.
Process 1300 includes operation 1302, which represents that similar-phrase module 132 receives a source phrase (e.g., “Misplaced Anger”) and, in response, identifies a number (i) of derived source phrases that each comprise a portion or the entire source phrase as well as one or more additional words (e.g., that appear before the portion of the phrase, after portion of the phrase or in both locations). In some instances, module 132 identifies the derived source phrases by analyzing the corpuses of text associated with sources 122 to identify phrases that include the source phrase or the portion of the source phrase. Alternatively, module 132 may identify a source phrase by simply appending a word (e.g., a mined word, a random word, etc.) to some or the entire original source phrase.
In the illustrated example, module 132 may analyze the corpuses of text to find phrases that include the phrase “Misplaced Anger”. These Phrases may include “Deeply Misplaced Anger”, “His Misplaced Anger”, “Misplaced Anger Today”, “Wrongly Misplaced Anger Yesterday”, and the like. In some instances, module 132 locates a number, i, of most-used phrases that end with “Misplaced Anger” and may use these phrase as derived source phrases. Module 132 may identify any number of derived source phrases, such as one, five, ten or any other number. In some instances, the “i” most-used phrases may comprise the thirty most-used phrases that end with “Misplaced Anger,” while excluding phrases beginning with a stop word (e.g., “The Misplaced Anger”). In other embodiments, these “i” derived source phrases may comprise those phrases having the highest mutual information score. In still other embodiments, these derived source phrases may comprise a portion of phrases that begin within “Misplaced Anger” and a portion of phrases that end with “Misplaced Anger.” For instance, some embodiments may use an approximately equal number of these two different types of derived source phrases.
At operation 1304, similar-phrase module 132 or another service determines a part-of-speech combination for each derived source phrase. As such, operation 1304 may be similar to operation 1102 discussed above. In the illustrated example, module 132 determines that the part-of-speech combination of the first derived source phrase (“Deeply Misplaced Anger”) is “RB-VBN-NN”. At operation 1306, module 132 or another service then attempts to find a first set of phrases that differ from the derived source phrase by one word, but that have the same part-of-speech combination of the derived source phrase. Operation 1306 may accordingly be similar to operation 11904 discussed above, although operation 1306 locates this first set of phrases for each of the “i” derived source phrases.
Next, at operation 1308, similar-phrase module 132 generates a second set of phrases for each derived source phrase from the respective set of substitute words associated with each derived source phrase. This operation may be similar to operation 1106. At this point, module 132 may have a second set of phrases for each of the “i” derived source phrases. At operation 1310, similar-phrase module 132 determines a part-of-speech combination for each phrase of the generated second sets of phrases. Again, this operation may be similar to operation 1108 discussed above.
FIG. 14 continues the illustration of process 1300, and includes module 132 or another service filtering out phrases of the second sets of phrase that do not appear in the corpuses of text a predetermined number of times (e.g., one time, ten times, one hundred thousand times, etc.), similar to operation 1110. The illustrated example represents that the generated phrases “Deeply Buried Enthusiasm” and “Broadly Suppressed Guilt” may not appear within the analyzed corpuses of text the predetermined number of times and, as such, module 132 may filter out these phrases.
At operation 1314, similar-phrase module 132 then removes the substitute word that appears in the position of the additional word of the derived source phrase for each remaining phrase of the second sets of phrases. That is, up to this point, process 1300 may have generated the phrases “Greatly Misplaced Faith”, “Deeply Suppressed Guilt” and “Massively Misplaced Enthusiasm” for the derived source phrase of “Deeply Misplaced Anger”. However, the reader will recall that the original source received at operation 1302 comprises the two-word phrase: “Misplaced Anger”. As such, operation 1314 removes the first word in the generated three-word phrases associated with respective derived source phrases. In the illustrated example, module 132 may remove the first word of each of the phrases that have been determined to be similar to the derived source phrase of “Deeply Misplaced Anger”, leaving these phrases as “Misplaced Faith”, “Suppressed Guilt”, “Misplaced Enthusiasm” and the like.
It is to be appreciated, however, that while module 132 removes one word from these phrases in the illustrated example, module 132 may remove more or fewer words in other instances. In some instances, module 132 may remove a corresponding number of words from positions that correspond to the words added to the source phrase at operation 1302. For instance, if module 132 identified one or more derived source phrases having two words in addition to the received source phrase, then module 132 may remove the first two words from each phrase of the generated second sets of phrases at operation 1314. Similarly, if module 132 added two words to the front of the source phrase and one word to the end of the source phrase at operation 130, then module may remove the first two words and the last word of the phrases at operation 1314.
Finally, similar-phrase module 132 merges the “i” sets of phrases, ranks the merged phrases and outputs at least a portion of these merged, ranked phrases at operation 1316. In some instances, module 132 may first rank the phrases within each second set of phrases before selecting a portion of these ranked phrases to merge with other phrases from the other second sets. In one particular and non-limiting example, module 132 may determine a number (Nselect) of phrases to select from each second set associated with a derived source phrase based on the following equation:

Nselect=100*Noutput/i 

Stated otherwise, module 132 may choose a number of phrases from each second set by multiplying a total desired number (Noutput) of output phrases associated with the original source phrase (e.g., “Misplaced Anger”) by one hundred and dividing by the number (i) of derived source phrases. For instance, if module 132 is ultimately to output twenty-five phrases associated with “Misplaced Anger”, and if module 132 created ten derived source phrases at operation 1302, then module 132 may select 250 phrases from each ith second set of phrases (100*25/10).
Once module 132 merges the phrases from each ith second set of phrases, module 132 may rank the combined phrases before outputting them to a user or otherwise. In some instances, the process may rank the techniques according to how often the resulting phrase appeared in the each ith second set of phrases. For instance, a phrase that appeared in three lists may be ranked higher than a phrase that only appeared in a single list. In fact, in some instances, only those phrases that appeared in at least a predetermined number of lists (e.g., two) may be ranked and outputted to a user. As mentioned above with regards to process 1100, however, some implementations may refrain from ranking the phrases and may simply output phrases in a random manner or otherwise.
In one example, when module (and/or another entity) employs process 1300 for the source phrase “Misplaced Anger”, module 132 may return the following list of phrases:
1. Misplaced Faith
2. Misplaced Confidence
3. Suppressed Sense
4. Misplaced Pride
5. Buried Desire
6. Buried Anger
7. Displaced Desire
8. Misplaced Optimism
9. Suppressed Desire
10. Suppressed Guilt
11. Misplaced Guilt
12. Suppressed Anger
13. Misplaced Desire
14. Misplaced Trust
15. Displaced Anger
16. Misplaced Sense
17. Misplaced Zeal
18. Buried Guilt
19. Misplaced Enthusiasm
Again, the reader will appreciate that each phrase of the outputted list may be deemed similar to the source phrase by a user who inputs the source phrase “Misplaced Anger”.
FIG. 15 illustrates yet another example process 1500 that similar-phrase module 132 may implement in whole or in part for locating phrases that are related or similar to a particular source phrase. Again, module 132 may implement process 1300 in response to receiving a request from a user, in response to a user selecting a particular phrase for association with an account of the user or for any other similar or different reason. In some instances, module 132 implements process 1500 after module performs processes 1100 and/or 1300 and, in response, receives too few similar phrases via one or both of these processes. However, module 132 may similarly implement process 1500 at any other time that module 132 is to find phrase related or similar to a particular source phrase.
Process 1500 includes operation 1502, at which point similar-phrase module 132 receives a source phrase and derives two or more source phrases by adding words to the beginning, the end or both sides of the entire received source phrase or some portion of the received source phrase. In some instances, operation 1502 may perform this task in a similar or same manner as discussed above with regards to operation 1302 of FIG. 13. In this illustrated example, module 132 receives the source phrase “Wild Mushroom Farmer” and, in response, derives the source phrases of “Growing Wild Mushrooms” and “Dedicated Mushroom Farmer”. As these example phrases illustrate, module 132 may add words to the beginning of a phrase while excluding some word(s) of the original source phrase at the end (e.g., “Growing Wild Mushrooms”). Conversely, module 132 may also take a cut of the original source phrase that excludes word(s) at the beginning of the source phrase and may append a word to the front of this cut (e.g., “Dedicated Mushroom Farmer”). In addition and as discussed above, module 132 may add one or multiple words to both the front and the back end of a particular cut of the original source phrase. Finally, module 132 may or may not treat a singular noun similarly or the same as a plural noun. In the illustrated example, for instance, module 132 treats “Mushroom” the same as “Mushrooms”.
To select the words to which to append to the cuts of the original source phrase, module 132 may again analyze the corpuses of text associated with sources 122. For instance, if module 132 takes the cut “Wild Mushroom”, module 132 or another service may analyze the corpuses of text to locate phrases that end (or begin) with “Wild Mushroom” (or “Wild Mushrooms”). In some instances, module 132 may select a most popular phrase or phrases in order to select the appended word. For instance, if “Growing Wild Mushrooms” most appears in the analyzed corpuses of text, then module 132 may use this phrase as the derived phrase (and, hence, may append “Growing” to “Wild Mushrooms”). Module 132 may also use the most-common ten three-word phrases that end with the phrase “Wild Mushroom”, in which case the multiple derived source phrases may be treated similar to the process 1300 described above.
Similar to the creating of the derived source phrase “Growing Wild Mushrooms”, module 132 may analyze the corpuses of text to locate one or more most-used phrases that end with the phrase “Mushroom Farmer”. As illustrated, module 132 may locate the phrase “Dedicated Mushroom Farmer” as the most-used phrase and may use this phrase as the derived source phrase. Again, module 132 could also use multiple other common phrases that end with, begin with or otherwise include the phrase “Mushroom Farmer”.
At operation 1504, similar-phrase module 132 may determine a part-of-speech combination for each of the multiple derived source phrases. Here, for instance, module 132 may determine that the first derived source phrase (“Growing Wild Mushrooms”) has a part-of-speech combination of “VGB-JJ-NN”. Module 132 may similarly determine that the second derived source phrase (“Dedicated Mushroom Farmer”) has a part-of-speech combination of “JJ-JJ-NN”.
At operation 1506, similar-phrase module 132 may attempt to locate, for each derived source phrase, a first set of phrases that differ from the respective source phrase by one word but have the same part-of-speech combination. At operation 1508, similar-phrase module 132 generates a second set of phrases from the substitute words from the determined first set of phrases. Next, module 132 removes the word(s) from each phrase of the second sets of phrases corresponding to the position of the originally appended word(s). For instance, module 132 may remove the first word in each phrase associated with the derived source phrase “Growing Wild Mushrooms”, as well as the first word of each phrase associated with derived source phrase “Dedicated Mushroom Farmer”.
FIG. 16 continues the illustration of process 1500, and includes operation 1512. Here, similar-phrase module 132 locates a matching word at a position that connects two phrases from respective derived source phrases and merges any such phrases. For instance, module 132 may determine that the derived source phrase “Wild Mushrooms” is associated with a phrase “Crazy Fungus” (after the striking of the first word of the phrase “Growing Crazy Fungus”). Module 132 may also determine that the derived source phrase “Dedicated Mushroom Farmer” is associated with a phrase “Fungus Farmer” (after the striking of the first word of the phrase “Pennsylvania Fungus Farmer”). Here, the word “Fungus” in each of the associated phrases is associated with the word “Mushroom” in the respective source phrases. As such, the use of the term “Fungus” in this location by these two phrases essentially acts as a bridge between the two phrases that are otherwise associated with different derived source phrases. With this is mind, module 132 merges the phrase “Crazy Fungus” with the phrase “Fungus Farmer” to create a phrase “Crazy Fungus Farmer”. Similar-phrase module 132 then marks this phrase (“Crazy Fungus Farmer”) as related or similar to the original source phrase (“Wild Mushroom Farmer”). Module 132 repeats this merging to create a list of phrases that are similarly associated with the original source phrase.
At operation 1514, similar-phrase module 132 then determines a part-of speech combination for each phrase resulting from operation 1512 and filters out those phrases having part-of-speech combinations that do not match the part-of-speech combination of the original source phrase (“Wild Mushroom Farmer”). Next, operation 1516 filters out the remaining phrases that do not appear within the corpuses of text at least a predetermined number of times. Finally, operation 1518 ranks and outputs a least a portion of the remaining phrases, similar to operation 1316 of process 1300 discussed above.
FIG. 17 and the figures that follow illustrate yet another process 1700 in which the described techniques may locate phrases that are related or similar to an inputted source phrase. For instance, process 1700 may include receiving a request to locate and output phrase that are related or similar to a phrase “Beautiful Summer Morning”. Here, the techniques leverage the techniques described above with regards to FIGS. 11-14.
Process 1700 begins by breaking a received source phrase into fragments of the source phrase at operation 1702. A source phrase comprising “n” words (or an n-gram source phrase) may be broken into a number of fragments, Nf, according to the following equation:

Nf=[n*(n+1)]/2

For instance, continuing the example from above, operation 1702 may break the source phrase “Beautiful Summer Morning” into the following six fragments:
Beautiful
Summer
Morning
Beautiful Summer
Summer Morning
Beautiful Summer Morning
Next, operation 1704 attempts to find similar fragments of a same length for each of these fragments. In the instant example, operation 1704 creates six different buckets, each containing one or more fragments that are similar to the respective fragment of the source phrase.
The techniques for finding similar fragments may vary based on a length of a fragment. For instance, for a 1-word (or “1-gram”) fragment, operation 1704 may attempt to find similar 1-gram fragments in multiple ways. In one instance, operation 1704 may use a thesaurus or other reference tool to determine synonyms associated with the fragment, such as “Morning.”
In other instances, however, operation 1704 may attempt to find similar fragments (or, in this case, similar words) by analyzing the corpuses of text to find words with the same part of speech as the original fragment and separated by a conjunction or a delimiter. For instance, operation 1704 may first determine the part-of-speech combination of the original source phrase in order to determine the part of speech of each respective word or 1-gram. In the instant example and as discussed above, the operation may determine that the original source phrase “Beautiful Summer Morning” has a part-of-speech combination of “JJ-NN-NN”. As such, operation 1704 may search for similar 1-gram fragments for each of the words “Beautiful”, “Summer” and “Morning” using the determined parts of speech of the source phrase. Stated otherwise, individual fragments maintain their parts of speech (or part-of-speech combinations) based on the part-of-speech combination of the original source phrase.
To use the word “Morning” as an example, operation 1704 may analyze the corpuses of text to find words (excluding stop words) that that are nouns and that are separated from the word “morning” by a conjunction or a delimiter (i.e., “[NN]+Morning” or “Morning+[NN]”). When doing so, operation 1704 may locate the following phrases possibly amongst many additional phrases:
“Morning or Night”
“Night and Morning”
“Dawn to Morning”
“Morning to Dusk”
In these instances, operation 1704 may determine that the following words are similar fragments for the fragment “Morning”: “Night” (×2), “Dawn” and “Dusk”. In addition, operation 1704 may determine similar fragments in the same manner for the remaining two 1-gram fragments “Beautiful” and “Summer”.
Once operation 1704 determines these buckets of similar fragments for respective 1-gram fragments of the source phrase, this operation may proceed to sort or rank these similar 1-gram fragments in any number of ways. In some instances, operation 1704 may analyze a mutual information score of the phrase, such as “Morning or Night”. As described above, the calculated mutual information score may indicate how probable the phrase “Morning or Night” is relative to the probability of the individual words “Morning” and “Night”.
In addition or in the alternative, operation 1704 may count the total number of occurrences in which the similar fragment appears in a respective bucket. If, for instance, “Night” appears in the bucket associated with “Morning” more times than does “Dawn”, then operation 1704 may determine that “Night” is more similar, at least based on that metric.
In addition, operation 1704 may count a number of unique occurrences of the similar fragment. For instance, if operation 1704 locates “Dusk” in three different phrases in the corpuses of text (e.g., “Morning to Dusk”, “Dusk and Morning” and “Dusk but Morning”) while locating the term “Night” in six different phrases (e.g., “Morning and Night”, “Morning or Night”, “Morning to Night”, etc.), then operation 1704 may determine that “Night” has a higher unique count for “Morning” than does “Dusk”.
In some instances, operation 1704 may sort each 1-gram bucket of phrases according to one or any combination of these metrics. In one particular instance, operation 1704 sorts each bucket twice, once according to a total number of occurrences and once according to mutual information scores. For each fragment, operation 1704 may then use the minimum rank of the fragment on these lists as the primary sort key. For instance, if the term “Night” is first on the list sorted by total number of occurrences and fourth on the list sorted by mutual information scores, then “Night” has a primary sort key of four. If, meanwhile, the 1-gram fragment “Dusk” appears tenth and twelfth on the respective lists, then “Dusk” has a primary sort key of twelve. As such, operation 1704 may deem “Night” as more similar to “Morning” than is “Dusk”.
In addition, if two 1-gram fragments have the same primary sort key, then operation 1704 may determine a secondary sort key as a tie breaker. In some instances, the second sort key comprises the maximum of the two sorted lists. To illustrate, envision that one word was fifth and seventeenth on the lists (thus, having a primary sort key of seventeen and a secondary sort key of five) while another word was eleventh and seventeenth (thus, having a primary sort key of seventeen and a secondary sort key of eleven). Here, the first word would be deemed more similar based on the secondary sort key. Finally, in rare instances where two 1-gram fragments have the same primary and secondary sort keys, operation 1704 may look to each word's “unique count” to break the tie. Of course, while one example sorting method has been discussed, other implementations may sort each buck of 1-gram fragments in multiple other similar or different ways.
After or during the sorting each bucket of 1-gram fragments, operation 1704 may also calculate and assign a normalized similarity score to each 1-gram fragment. That is, operation 1704 may calculate how similar each 1-gram fragment is to the corresponding 1-gram fragment of the source phrase (e.g., “Morning”). By providing a normalized score, process 1700 enables for comparison of similarities between dissimilar fragments, such as between 1-gram, 2-gram and 3-gram fragments (or n-gram fragments in implementations for source phrases consisting of “n” words).
In addition to locating similar 1-gram fragments, operation 1704 may attempt to locate similar 2-gram fragments for the 2-grams present in the original source phrase. In the instant example, operation 1704 may attempt to find 2-gram fragments that are similar to the fragments “Beautiful Summer” and “Summer Morning”. While operation 1704 may achieve this in any number of ways, in some instances this operation may employ some or all of process 1300 described above with reference to FIGS. 13-14. In the instant example, operation 1704 may use operation 1300 to determine that the following fragments (possibly in addition to many more fragments) are similar to “Beautiful Summer”:
“Gorgeous Fall”
“Beautiful Winter”
“Late Morning”
In addition, operation 1704 may determine that the following fragments (possibly in addition to many more fragments) are similar to the 2-gram fragment “Summer Morning”:
“Fall Night”
“Winter Evening”
“Sunday Afternoon”
After determining these similar fragments, operation 1704 may sort these fragments according to a similarity between the respective fragments and the original 2-gram fragment of the source phrase. In this instance, because the original source phrase comprises two 2-gram fragments, operation 1704 may sort these two different buckets of fragments according to similarity.
While operation 1704 may sort these fragments in any number of ways, in one instance this operation first counts the number of input 3-grams that produced the 2-gram at least once. The operation then throws out any 2-gram fragments having a final count of one. Then, the operation ranks the remaining 2-gram fragments from a largest count to a smallest count. When multiple 2-gram fragments have the same count, the operation uses the mutual information score as the secondary sort key. Finally, operation 1704 may add the input 2-gram to the head of the list by convention. In addition and similar to the discussion above in regards to 1-gram fragments, operation 1704 may calculate and assign a normalized score to each similar 2-gram fragment.
Finally, in the instant example operation 1704 may determine 3-gram fragments that are similar to the 3-gram fragment of the source phrase, which here comprises the 3-gram source phrase itself. To determine phrases that are similar to “Beautiful Summer Morning”, operation 1704 may implement process 1100 described above with reference to FIGS. 11-12. As discussed above, operation 1704 may determine that the following 3-gram fragments (possibly in addition to many more fragments) are similar to the 3-gram fragment “Beautiful Summer Morning”:
“Late Sunday Morning”
“Crisp Autumn Night”
“Early Friday Evening”
Again, operation 1704 may then sort or rank these similar fragments, possibly in the manner discussed above in regards to process 1100 or in any other way. In addition and similar to the discussion above, operation 1704 may also calculate a normalized score for each of these similar 3-gram fragments.
Process 1700 then proceeds to operation 1706, which functions to combine the similar fragments into similar phrases having the same length as the source phrase. For instance, in the instant example, operation 1706 may combine 1-gram fragments with one another, 1-gram and 2-gram fragments with one another, and so forth. More specifically, operation 1706 may combine fragments in the following manner to create the following four buckets of phrases that are similar to the inputted source phrase:

    
    
        1. Fragments similar to “Beautiful”+fragments similar to “Summer”+fragments similar to “Morning” (e.g., “[Sweet] [Winter] [Night]”)
        2. Fragments similar to “Beautiful”+fragments similar to “Summer Morning” (e.g., “[Wonderful] [Fall Night]”)
        3. Fragments similar to “Beautiful Summer”+fragments similar to “Morning” (e.g., “[Gorgeous Fall] [Night]”)
        4. Fragments similar to “Beautiful Summer Morning” (e.g., “[Late Sunday Morning]”)
    
    


While the above example describes combining fragments that are associated with a 3-gram source phrase, the techniques may similarly combine fragments that are associated with a source phrase of any number of “n” words.
Once operation 1706 combines the fragments to form phrases that are similar to the original source phrase, operation 1708 may assign a similarity score for use in outputting phrases (e.g., to a user) at operation 1710. Operation 1708 may assign these scores in any variety of ways.
In one implementation, operation 1708 scores phrases within each bucket. Operation 1710 then outputs phrases starting with a first bucket and only proceeds to a second bucket when the first bucket is exhausted. For instance, operation 1710 may output each phrase within the fourth bucket above (comprising 3-gram fragments that are similar to the original source phrase) before proceeding to either the second or the third bucket above. In this instance, operation 1710 outputs the phrases from the fourth bucket in descending order of similarity scores, beginning with the most similar phrase.
After operation 1710 exhausts the fourth bucket, then operation 1710 may proceed to output phrases from the second and/or third buckets (both comprising phrases that are combinations of 1-gram and 2-gram fragments). In order to determine whether to begin with the second bucket or third bucket, operation 1710 may determine a mutual information score of both 2-gram fragments of the original source phrase (“Beautiful Summer” and “Summer Morning”) and may begin with the bucket associated with the fragment having the higher mutual information score. After exhaustion of that bucket, operation 1710 may proceed to the bucket that was not selected via the mutual information score comparison. Finally, operation 1710 may transition to the first bucket above comprising the combination of three 1-gram fragments. Of course, in each of these instances, operation 1710 may cease outputting phrases once the operation has outputted the desired number, k, of phrases.
In another implementation, operation 1708 assumes that all of the similar phrases within each of the buckets (in this example, within each of the four buckets) have an equal potential be a good phrase. As such, operation 1706 will simply calculate a mutual information score for each phrase of all of the buckets and will output the desired number of phrases based on the calculated scored, regardless of a bucket origin of the phrases.
In still another implementation, operation 1708 again assumes that all of the similar phrases within each of the buckets have an equal potential be a good phrase. As such, operation 1708 calculates or determines a normalized score for each of the phrases, while operation 1710 outputs phrases at least in part in accordance with the normalized scores.
In each of these scoring examples, operation 1708 and/or operation 1710 may apply one or more heuristics to tweak the final output of phrases at operation 1710. For instance, operations 1708 and/or 1710 may apply a “preimage heuristic” that comprises a count of a number of fragment patterns that output a particular phrase. For instance, envision that the phrase “Late Sunday Morning” was created in the first bucket above (i.e., “[Late] [Sunday] [Morning]”) as well as in the fourth bucket (i.e., “[Late Sunday Morning]”). Meanwhile, envision that the phrase “Gorgeous Fall Night” was only created in the third bucket (i.e., “[Gorgeous Fall] [Night]”).
In this example, the first phrase “Late Sunday Morning” may have a preimage count of two, while the phrase “Gorgeous Fall Night” may have a preimage count of one. In some instances, operations 1708 and/or 1710 may assume that “Late Sunday Morning” is a better candidate for outputting to a user and, as such, may somehow take this preimage count into consideration when outputting the phrases.
In some instances, operation 1708 takes the preimage count into consideration before actually scoring the phrases. Here, operation 1708 separates out those phrases having the highest preimage count before applying one or more of the scoring techniques discussed above to the phrases. Then, operation 1710 outputs those all of the phrases having the highest preimage count (or as many as are needed) in the ranked order before outputting phrases of a lesser preimage count (if needed).
For instance, in the above example, operation 1708 may first score those phrases have a highest preimage count of four (that is, phrases that appear in all four buckets for the 3-gram source phrase “Beautiful Summer Morning”). Operation 1710 may then output these phrases having a preimage count of four in the ranked order before outputting any phrases having a preimage count of three or less. If operation 1710 outputs each phrase having a preimage count of four, and operation 1710 still needs to output additional phrases, then operation 1710 may output phrases having a preimage count of three in the ranked order and so on.
In other instances, operations 1708 and/or operation 1710 may take the preimage counts into consideration after operation 1708 has scored the phrases. For instance, after operation 1708 has sorted or ranked the “K” requested phrases, this sorted list may be re-sorted according to a preimage count of the phrases. That is, those phrases with a highest preimage count (here, four) will move to the top of the outputted while those with fewer preimage counts will move towards the bottom of the list. In instances where multiple phrases have a same preimage count, operation 1710 may maintain the original ranking calculated by operation 1708.
Implementing a Feedback Loop
FIG. 18 illustrates an example feedback loop 1800 for learning characteristics about the phrases selected by users and, in response to the learning, altering the characteristics of phrases suggested to users. As illustrated, at operation one (1) phrases of first corpus of phrases 806 are inserted into phrase-filtering module 802. As discussed above, module 802 filters out a portion of these phrases according to multiple factors, with the phrases that have not been filtered-out defining second corpus of phrases 808 at operation two (2).
Operation three (3) then suggests some portion of these phrases to each of multiple users 102(1)-(N). These suggested phrases may comprise buckets of phrases that have been generated by each generation technique discussed above. For instance, the phrases output to users 102(1)-(N) may include mined phrases (both contiguous words and SIPs), constructed phrases (based on both POS templates, POS/class templates and personalized templates), pseudo SIPs, and potentially any other form of generated phrase. Some or all of users 102(1)-(N) then select one or more phrases of these provided phrases.
Next, operation four (4) represents that feedback engine 804 may receive these user selections of phrases. In response to receiving these selections, feedback engine may analyze characteristics associated with the selected phrases. For instance, feedback engine 804 may determine which types of phrases users are selecting as well as the details about these selected phrases. For instance, feedback engine 804 may determine if users are selecting more mined phrases or more constructed phrases (or more pseudo SIPs). Within these categories, engine 804 may determine which subcategories are popular, and which are not. For instance, engine 804 may determine which POS combinations or POS/class combinations users are selecting, as well as the characteristics of selected SIPs, pseudo-SIPs, and contiguous-word mined phrases.
With this information, feedback engine 804 may determine how the filters of phrase-filtering module 802 could be tweaked so as to output to second corpus 808 phrases that more closely resembles what users 102(1)-(N) are actually selecting. Furthermore, feedback engine 804 may determine which category and sub-category users are selecting and, in response, can cause alteration of the percentage-size of each of the suggested buckets.
At operation five (5), feedback engine 804 makes a determination that the filters of module 802 should be tuned to better reflect the phrase selections of users 102(1)-(N). In response, module 802 tweaks the corresponding filter and outputs, at operation six (6), a new set of phrases that now defines a newly-sculpted second corpus of phrases 808. Furthermore, at operation seven (7), second corpus 808 may output a new set of suggested phrases. In some instances, the makeup of these suggested phrases varies from the previous set of suggested phrases. For instance, these suggested phrases may contain a greater percentage of phrases generated via a popular technique (e.g., two-word SIPs) and a lesser percentage of phrases generated via a less popular technique (e.g., [NN]-[NN] constructed phrases that comprise a common phrase). With use of feedback loop 1700, phrase-generation service 110 is able to quickly and aptly respond to tune the phrases being suggested or otherwise provided to users in order to better suit users' desires.
Furthermore and as discussed above in regards to FIG. 3, users 102(1)-(N) may choose to select non-suggested phrases. That is, users may choose to create their own phrase. In these instances, feedback engine 804 will again receive this information at operation four (4). Armed with this information, feedback engine 804 may learn that users are creating certain types of phrases that second corpus of phrases 808 is not providing. Feedback engine 804 may accordingly attempt to determine how phrase-filtering module 802 could be tweaked to create these types of phrases.
For instance, engine 804 may determine that a particular POS combination that module 802 has been filtering out is actually quite popular amongst users 102(1)-(N). As such, engine 804 may instruct phrase-filtering module 802 to remove this POS combination from a POS-exclusion list of the POS filter (or engine 804 may instruct module 802 to add this POS combination to a POS inclusion list). In either instance, phrases of this particular POS combination may form a portion of second corpus 808 and, as such, may be suggested to users 102(1)-(N) for selection.
Illustrative Processes
FIGS. 19-26 illustrate example processes 2000, 2100, . . . , 2600 for implementing the techniques described above. While these processes are described with reference to the architectures discussed above, the described techniques may be equally applicable in many other architectures and environments. Furthermore, the described processes (as well as each process described throughout this document) can be implemented in hardware, software, or a combination thereof. In the context of software, the illustrated operations represent computer-executable instructions that, when executed by one or more processors, perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, and the like that perform particular functions or implement particular abstract data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described blocks can be combined in any order and/or in parallel to implement the process.
Process 1900 includes operation 1902, which analyzes multiple corpuses of text to generate a first corpus of phrases. As discussed above, these corpuses of text may come from any source, including books, magazines, newspapers, online content, audio content, video content or any other source. Furthermore, this operation may generate these phrases in any of the ways discussed above or otherwise. For instance, this operation may mine phrases, construct phrases, or determine phrases in any way. Operation 1904, meanwhile, represents filtering the first corpus of phrases to define a second corpus of phrases. This filtering may include filtering phrases by part-of-speech combinations, according to a blacklist or a whitelist, according to one or more of the multiple factors discussed above (e.g., commonality, readability, length, etc.), or otherwise.
Next, operation 1906 suggests phrases of the second corpus of phrases to a user. This operation may suggest these phrases visually, orally, or in any other suitable manner. Operation 1908 receives a selection of a phrase, either from the suggested phrases or otherwise. In response, operation 1910 associates the selected phrase with the user and, potentially, with an aspect of a user account of the user. For instance, operation 1908 may associate the selected phrase with a payment instrument associated with the user account, a location (e.g., shipping address, digital location, etc.) associated with the user account, a delivery method associated with the user account, or the like. In these instances, the user may be able to conduct transactions with use of the phrase (and with, for instance, the associated payment instrument, location, or delivery method).
Process 1900 continues to operation 1912, which associates rules to the selected phrase. In some instances, a user to whom the phrase is associated selects one or more of these rules. Next, operation 1914 receives a request to conduct a transaction with use of the selected phrase. For instance, content provider 106 or another content provider may receive a request from the user to purchase, rent, or otherwise consume an item. At operation 1916, the content provider approves the requested transaction if the associated rules allow for the transaction. Operation 1918, however, declines the transaction if the associated rules do not allow for the transaction.
FIG. 20 illustrates an example process 2000 for suggesting phrases, such as the phrases generated by the techniques described above. Process 2000 includes operation 2002, which represents analyzing multiple corpuses of text to generate a first corpus of phrases. Next, operation 2004 filters the first corpus of phrases to define a second corpus. Operation 2006 then determines keywords associated with a particular user. In some instances, this operation bases this keyword on previously-known information about the user (e.g., name, address, purchase history, etc.), while in other instances this keyword is based on an input from the user.
Process 2000 further includes operation 2008, which represents determining phrases that are associated with the determined keywords. This operation may include, for instance, analyzing an index (e.g., index 228) that maps keywords to phrases. Operation 2010 then suggests, to the user, some or all of the phrases that operation 2008 determines to be associated with the keyword(s). These suggestions may be in a ranked order, beginning with the most-related or may be suggested in any other manner (e.g., weighted by score, randomly, etc.).
Operation 2012, meanwhile, receives a selection of a phrase from the user. This selection may be from the suggested phrases or this selection may be a user-inputted phrase. Finally, operation 2014 associates the selected phrase with the user and, in some instances, with a payment instrument of the user.
FIG. 21 illustrates an example process 2100 for generating phrases by mining multiple sources. Operation 2102 analyzes multiple corpuses of text to determine statistically improbable phrases. As discussed above, statistically improbable phrase include phrases that appear in a corpus of text more than a predetermined threshold number of times. Next, operation 21904 stores the determined statistically improbable phrases in a first corpus of phrases.
Operation 2106, meanwhile, parses the multiple corpuses of text to determine phrases comprising two or more contiguous words. In some instances, these words are free from separation by punctuation (e.g., a comma, a period, etc.). Furthermore, in some instances, these determined phrases comprise between two and seven words. Next, operation 2108 stores the determined contiguous words in the first corpus of phrases. Operation 2110 then filters the first corpus of phrases to define second corpus of phrases, while operation 2112 suggests phrases of the second corpus to a user. Operation 2114 receives a selection and, in response, operation 2116 associates the selected phrase with the user and with an aspect of a user account of the user (e.g., a payment instrument, a location, a delivery method, or the like).
FIG. 22 illustrates an example process 2200 for generating phrases via the construction techniques discussed above. First, operation 2202 represents creating a template comprising at least first and second placeholders. The first placeholder specifies a first part of speech and a first describable class, while the second placeholder specifies a second part of speech and a second describable class. The describable classes may specify any grouping of words that, when associated with a part of speech, specify a subset of words having the specified part of speech. For instance, a class may be “words that relate to movement.” When coupled with a verb part of speech, the specified subset of words may include “run,” “dance,” “walk,” and the like. Furthermore, while the first and second parts of speech and/or the first and second classes may be different in some instances, in other instances they may be the same. For illustrative purposes, an example created template may comprise the following placeholders: [a verb in a gerund form that relates to movement] and [a noun that describes a fruit].
Operation 2204 then analyzes at least one corpus of text to determine multiple words. For instance, operation 2204 may mine a dictionary or any other source. Next, operation 2206 categorizes the determined words, thereby determining a first word having the specified first part of speech and first class and a second word having the second part of speech and the second class. In the example template described immediately above, for instance, operation 2206 may determine a first word “dancing” and a second word “bananas.” Operation 2208 then applies the determined first and second words to the created template to create a phrase. In the instant example, operation 2208 may create the phrase “dancing bananas.” Finally, operation 2210 associates this phrase with a user and with an aspect of a user account of the user (e.g., a payment instrument, a location, a delivery method, or the like) in response to receiving a selection of this phrase.
FIG. 23 illustrates an example process 2300 for filtering phrases. Similar to the processes described above, operation 2302 represents analyzing multiple corpuses of text to generate a first corpus of phrases. Next, operation 2304 determines, for each phrase, a commonality of the phrase, a commonality of a word of the phrase, a number of syllables in the phrase, and a number of words in the phrase. Operation 2306 then scores each of the phrases based at least in part on the determined values of operation 2304.
Next, operation 2308 then filters the first corpus of phrases to define a second corpus of phrases. As illustrated, operation 2308 represents sub operations 2308(1), (2), and (3). Sub-operation 2308(1) removes phrases based on the scoring of the phrases from operation 2306. Next, operation 2308(2) removes phrases that include words appearing on a blacklist of words. Finally, operation 2308(3) removes phrases that comprise specified part-of-speech combinations.
Operation 2310, meanwhile, provides phrases of the second corpus of phrases to a user for selection. In response to receiving a selection of a phrase, operation 2312 associates the selected phrase with the user.
FIG. 24 illustrates an example process 2400 for determining phrases that are related to one or more other phrases. These one or more other phrases may, in some instances, share one or more common characteristics. For instance, these one or more phrases may each comprise a statistically improbable phrase (within the same source or different sources). As discussed above, phrases that are determined to be related to statistically improbable phrases tend to themselves share characteristics of statistically improbable phrases and, as such, are often perceived as interesting to users.
Process 2400 includes operation 2404, which represents creating an index that maps keywords to phrases. For instance, operation 2404 may represent creating index 228 illustrated in FIG. 2. Next, operation 2404 determines one or more phrases that are associated with a user. In instances where these phrases comprise statistically improbable phrases, operation 2404 may include determining books that are associated with the user (e.g., via purchase history 218, item recommendations 220, etc.) and determining statistically improbable phrases that appear in these books. Operation 2406 then determines the words that compose the phrases that are associated with the user.
With use of the words as keywords, operation 2408 analyzes the created index to determine phrases that are associated with the keywords. This may include, determining phrases that are associated with each of the keyword, all but one of the keywords, and so forth down to the phrases that are only associated with a single inputted keyword. Finally, operation 2410 outputs at least a portion of the determined phrases for suggestion to a user. In some instances, operation 2410 outputs the most-related phrases to the user, while in other instances operation 2410 outputs these phrases randomly or in another manner.
FIG. 25 illustrates an example process 2500 that feedback engine 804 may implement. Operation 2502 first represents generating a corpus of phrases based on a first set of criteria. For instance, this first set of criteria may comprise specified filter parameters, a specified weighting of phrases by generation technique, or any other criteria that may be used in generating the corpus. Next, operation 2504 provides a portion of the corpus for selection by users. For instance, this operation may suggest these phrases via user interface 300, orally, or otherwise.
Operation 2506, meanwhile, receives selections of phrases by users, while operation 2508 analyzes the selected phrases to determine a second set of criteria. For instance, operation 2508 may determine that users tend to select mined phrases at a higher rate than constructed phrases. Additionally or alternatively, operation 2508 may determine that users tend to select phrases that have a particular score that is based on a phrase commonality, word commonality, readability and word length.
In response, operation 2510 re-generates the corpus of phrases based at least in part on the second set of criteria. For instance, operation 2510 may tweak the filters based on the second criteria and/or may alter the distribution of provided phrases based on generation technique. After this re-generation, operation 2512 represents providing a portion of the re-generated corpus for selection by users. As discussed above, these phrases may be associated with users in response to received user selections.
FIG. 26, meanwhile, illustrates a process 2600 for determining a set of phrase that are each related or similar to an inputted source phrase. In some instances, similar-phrase module 132 may employ some or all of process 2600. This process includes, at operation 2602, identifying a source phrase comprising: (i) a first word having a first part-of-speech and a first position within the source phrase; (ii) a second word having a second part-of-speech and a second position within the source phrase, and (iii) a third word having a third part-of-speech and a third position within the source phrase. This source phrase may comprise, for instance, “Beautiful Summer Morning” or it may comprise any other source phrase of three or more words.
Next, operation 2604 identifies substitute words for the first word (e.g., “Beautiful”) by searching a corpus of text for phrases each comprising: (i) a word having the first part-of-speech and the first position; (ii) the second word at the second position, and (iii) the third word at the third position. As discussed in the example above, these phrases may be, for instance, “Late Summer Morning”, “Hot Summer Morning”, “Cool Summer Morning” or the like. As such, the substitute words may comprise “Late”, “Hot” and “Cool”, amongst other words.
Next, operation 2606 identifies substitute words for the second word by searching a corpus of text for phrases that comprising: (i) the first word at the first position; (ii) a word having the second part-of-speech and the second position, and (iii) the third word at the third position. Similarly, operation 2608 identifies substitute words for the third word by searching a corpus of text for phrases that comprising: (i) the first word at the first position; (ii) the second word at the second position, (iii) and a word having the third part-of-speech and the third position.
After identifying substitute words for each of the words of the source phrase, operation 2610 generates phrases by combining different combinations of the substitute words. For instance, these phrases may comprise “Late Sunday Evening”, “Cool Autumn Night” or the like, as discussed above. Finally, operation 2612 outputs at least a portion of the generated phrases, possibly to a user for association with a user account or for any other reason as discussed throughout the document.
CONCLUSION
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as exemplary forms of implementing the claims.

## Claims

1. One or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause one or more processors to perform acts comprising:
identifying a first set of phrases that exist within a corpus of text, individual ones of the first set of phrases including a substitute word that replaces one word of a source phrase comprising multiple words;
generating a second set of phrases by combining substitute words from the first set of phrases with one another such that individual ones of the second set of phrases include a first substitute word from a first phrase of the first set of phrases and a second substitute word from a second phrase of the first set of phrases;
generating a personalized user interface including at least a portion of the second set of phrases and at least one rule selector associated with a corresponding rule for using the at least the portion of the second set of phrases; and
receiving a user selection of the at least the portion of the second set of phrases and the at least one rule selector associated with the corresponding rule for using the at least the portion of the second set of phrases.

2. One or more non-transitory computer-readable media as recited in claim 1, wherein the identifying of the first set of phrases comprises:
determining a part-of-speech combination of the source phrase; and
for individual words of the source phrase, identifying phrases that differ only by that word but that comprise the part-of-speech combination of the source phrase.

3. One or more non-transitory computer-readable media as recited in claim 1, further storing computer-executable instructions that, when executed, cause the one or more processors to perform acts comprising:
determining a part-of-speech combination of the source phrase; and
filtering out phrases of the second set of phrases that do not comprise the part-of-speech combination of the source phrase.

4. One or more non-transitory computer-readable media as recited in claim 1, further storing computer-executable instructions that, when executed, cause the one or more processors to perform acts comprising:
determining, for individual phrases of the second set of phrases, that a phrase appears in a published corpus of text at least a predetermined number of times; and
filtering out phrases of the second set of phrases that do not appear in the published corpus of text at least the predetermined number of times.

5. One or more non-transitory computer-readable media as recited in claim 1, further storing computer-executable instructions that, when executed, cause the one or more processors to perform an act comprising ranking the second set of phrases according to a mutual information score for individual phrases of the second set of phrases, and wherein the generating the personalized user interface including the at least the portion of the second set of phrases comprises selecting individual phrases of the second set of phrases to be presented at least in part based on the ranking.

6. One or more non-transitory computer-readable media as recited in claim 1, further storing computer-executable instructions that, when executed, cause the one or more processors to perform acts comprising associating the user selection of the at least the portion of the second set of phrases and the at least one rule selector associated with the corresponding rule for using the at least the portion of the second set of phrases with an aspect of a user account of a user.

7. One or more non-transitory computer-readable media as recited in claim 6, wherein the aspect of the user account comprises a payment instrument associated with the user account, a physical or digital location associated with the user account, or a delivery method associated with the user account.

8. One or more non-transitory computer-readable media as recited in claim 1, wherein individual phrases of the portion of the second set of phrases are grammatically correct and comprise two or more words.

9. A computer-implemented method comprising:
under control of one or more computer systems configured with executable instructions:
identifying a source phrase comprising multiple words, individual ones of the multiple words comprising a respective part-of-speech and a respective position within the source phrase;
determining a first set of phrases that: (i) exist within a corpus of text; (ii) individually comprise multiple words having a same respective part-of-speech in a same position as the source phrase, and (iii) individually differ from the source phrase by one word;
generating a second set of phrases that individually include multiple words from the first set of phrases that differ from the source phrase;
generating a personalized user interface including at least a portion of the second set of phrases and at least one rule selector associated with a corresponding rule for using the at least the portion of the second set of phrases; and
receiving a user selection of the at least the portion of the second set of phrases and the at least one rule selector associated with the corresponding rule for using the at least the portion of the second set of phrases in an electronic commerce transaction.

10. A computer-implemented method as recited in claim 9, wherein the generating of the second set of phrases comprises combining the words of the first set of phrases that differ from the source phrase together in their respective positions.

11. A computer-implemented method as recited in claim 9, wherein the generating the personalized user interface comprises presenting phrases of the second set of phrases that comprise a same part-of-speech combination as the source phrase and refraining from presenting phrases of the second set of phrases that comprise a different part-of-speech combination than the source phrase.

12. A computer-implemented method as recited in claim 9, further comprising:
before the determining of the first set of phrases, adding an additional word having a corresponding part-of-speech to a particular position of the source phrase to create a derived source phrase; and
wherein individual phrases of the first set of phrases further comprise a word having a same part-of-speech in a same respective position as the additional word.

13. A computer-implemented method as recited in claim 9, wherein the determining of the first set of phrases comprises analyzing the corpus of text to locate the first set of phrases.

14. One or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause one or more processors to perform acts comprising:
receiving a grammatically correct source phrase as a received source phrase comprising multiple words;
identifying a derived source phrase that comprises at least a portion of the received source phrase and at least one additional word;
identifying a first set of phrases that exist within a corpus of text, individual ones of the first set of phrases differing from the derived source phrase by one word;
generating a second set of phrases from the first set of phrases, the second set of phrases individually including multiple words from the first set of phrases that differ from the derived source phrase;
removing at least one word from a portion of phrases of the second set of phrases;
generating a personalized user interface including at least the portion of the phrases and at least one rule selector associated with a corresponding rule for using the at least the portion of the phrases; and
receiving a user selection of the at least the portion of the phrases and the at least one rule selector associated with the corresponding rule for using the at least the portion of the phrases.

15. One or more non-transitory computer-readable media as recited in claim 14, wherein the identifying of the derived source phrase comprises:
analyzing the corpus of text to determine how often the derived source phrase appears in the corpus of text relative to other phrases that also comprise at least a portion of the received source phrase and a different at least one additional word; and
selecting the derived source phrase based at least in part on the determination.

16. One or more non-transitory computer-readable media as recited in claim 14, wherein the at least one additional word resides within the derived source phrase at a particular position, and wherein the removing of the at least one word from the portion of the phrases comprises removing a word at the particular position from individual phrases of the portion of the phrases.

17. One or more non-transitory computer-readable media as recited in claim 14, wherein the derived source phrase comprises a first derived source phrase, and the one or more non-transitory computer-readable media further storing computer-executable instructions that, when executed, cause the one or more processors to perform acts comprising:
identifying a second derived source phrase that comprises at least a portion of the received source phrase and a different at least one additional word;
identifying another first set of phrases individually found within the corpus of text and individually differing from the second derived source phrase by one word;
generating another second set of phrases from the another first set of phrases by combining the words from the another first set of phrases that differ from the second derived source phrase; and
merging the second set of phrases associated with the first derived source phrase with the another second set of phrases associated with the second derived source phrase before the generating the personalized user interface including the at least the portion of the phrases.

18. One or more non-transitory computer-readable media as recited in claim 17, wherein the merging comprises merging phrases based at least in part on a number of derived source phrases that produced individual respective phrases.

19. One or more non-transitory computer-readable media as recited in claim 14, wherein the derived source phrase excludes one or more words of the received source phrase.

20. One or more non-transitory computer-readable media as recited in claim 14, wherein at least a portion of phrases of the first set of phrases individually differ from the derived source phrase by two words.

21. One or more computing devices comprising:
one or more processors; and
the one or more non-transitory computer-readable media as recited in claim 14.

22. One or more computing devices comprising:
one or more processors; and
one or more computer-readable media storing computer-executable instructions that, when executed, cause the one or more processors to perform acts comprising:
identifying a first set of phrases that exist within a corpus of text, individual ones of the first set of phrases including a substitute word that replaces one word of a source phrase comprising multiple words;
generating a second set of phrases by combining substitute words from the first set of phrases with one another, the second set of phrases individually including multiple substitute words from the first set of phrases;
generating a personalized user interface including at least a portion of the second set of phrases and at least one rule selector associated with a corresponding rule for using the at least the portion of the second set of phrases; and
receiving a user selection of the at least the portion of the second set of phrases and the at least one rule selector associated with the corresponding rule for using the at least the portion of the second set of phrases.

23. One or more computing devices as recited in claim 22, wherein the identifying of the first set of phrases comprises:
determining a part-of-speech combination of the source phrase; and
for individual words of the source phrase, identifying phrases that differ only by that word but that comprise the part-of-speech combination of the source phrase.

24. One or more computing devices as recited in claim 22, the one or more computer-readable media further storing computer-executable instructions that, when executed, cause the one or more processors to perform acts comprising:
determining a part-of-speech combination of the source phrase; and
filtering out phrases of the second set of phrases that do not comprise the part-of-speech combination of the source phrase.

25. One or more computing devices as recited in claim 22, the one or more computer-readable media further storing computer-executable instructions that, when executed, cause the one or more processors to perform acts comprising:
determining, for individual phrases of the second set of phrases, if a phrase appears in a published corpus of text at least a predetermined number of times; and
filtering out phrases of the second set of phrases that do not appear in the published corpus of text at least the predetermined number of times.

26. One or more computing devices as recited in claim 22, the one or more computer-readable media further storing computer-executable instructions that, when executed, cause the one or more processors to perform acts comprising ranking the second set of phrases according to a mutual information score for individual phrases of the second set of phrases, and wherein the generating the personalized user interface including the at least the portion of the second set of phrases comprises selecting individual phrases of the second set of phrases to be presented at least in part based on the ranking.

27. One or more computing devices as recited in claim 22, the one or more computer-readable media further storing computer-executable instructions that, when executed, cause the one or more processors to perform acts comprising associating the user selection of the at least the portion of the second set of phrases and the at least one rule selector associated with the corresponding rule for using the at least the portion of the second set of phrases with an aspect of a user account of a user.

28. One or more computing devices as recited in claim 27, wherein the aspect of the user account comprises a payment instrument associated with the user account, a physical or digital location associated with the user account, or a delivery method associated with the user account.

29. One or more computing devices as recited in claim 22, wherein individual phrases of the portion of the second set of phrases are grammatically correct and comprise two or more words.

30. One or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause one or more processors to perform a method comprising:
identifying a source phrase comprising multiple words, individual ones of the multiple words comprising a respective part-of-speech and a respective position within the source phrase;
determining a first set of phrases that: (i) exist within a corpus of text; (ii) individually comprise multiple words having a same respective part-of-speech in a same position as the source phrase, and (iii) individually differ from the source phrase by one word;
generating a second set of phrases that individually include multiple words from the first set of phrases that differ from the source phrase; and
generating a personalized user interface including at least a portion of the second set of phrases and at least one rule selector associated with a corresponding rule for using the at least the portion of the second set of phrases; and
receiving a user selection of the at least the portion of the second set of phrases and the at least one rule selector associated with the corresponding rule for using the at least the portion of the second set of phrases in an electronic commerce transaction.

31. One or more non-transitory computer-readable media as recited in claim 30, wherein the generating of the second set of phrases comprises combining the words of the first set of phrases that differ from the source phrase together in their respective positions.

32. One or more non-transitory computer-readable media as recited in claim 30, wherein individual phrases of the at least the portion of the second set of phrases comprise a same part-of-speech combination as the source phrase.

33. One or more non-transitory computer-readable media as recited in claim 30, further storing computer-executable instructions that, when executed, cause the one or more processors to perform acts comprising:
before the determining of the first set of phrases, adding an additional word having a corresponding part-of-speech to a particular position of the source phrase to create a derived source phrase; and
wherein individual phrases of the first set of phrases further comprise a word having a same part-of-speech in a same position as the additional word.

34. One or more non-transitory computer-readable media as recited in claim 30, wherein the determining of the first set of phrases comprises analyzing the corpus of text to locate the first set of phrases.

35. One or more non-transitory computer-readable media as recited in claim 30, wherein the at least one rule selector associated with the corresponding rule for using the at least the portion of the second set of phrases comprises at least an indication of how another user may use the at least the portion of the second set of phrases.