prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: How does the scoring engine generate a stream of content for the channel?

Read the following text and answer briefly.

Context 1: A system and method for generating a stream of content for a channel  The channel application includes a content categorizer, a scoring engine and a channel engine  The content categorizer categorizes new content items received from heterogeneous data sources  The channel engine identifies a channel category for a user based at least in part on at least one of a historical trend and a user activity  The scoring engine queries the new content items based on the channel category and at least one other channel attribute  The scoring engine retrieves candidate content items that include the channel category and the other channel attribute  The scoring engine then generates a stream of content from the candidate content items for the channel 
This application claims priority under 35 USC §120 to U S  application Ser  No  13/225,209, entitled, “Generating a Stream of Content for a Channel,” filed on Sep  2, 2011, and claims priority under 35 USC §119(e) to U S  Application No  61/424,636, entitled “Scoring Stream Items with Models Based on User Interests” filed Dec  18, 2010, the entireties of which are herein incorporated by reference 
The specification relates to a system and method for generating a stream of content for a channel  In particular, the specification relates to generating a stream of content for a channel based on user interests and historical trends 
Many consumers of digital media have two somewhat contradictory goals: keep apprised of information in the areas they already find interesting and discover new content that is also enjoyable  Keeping apprised of information can become burdensome in the digital age because there is so much information  Hence, there is a need to present the best and most relevant information, without overwhelming the consumer  Furthermore, consumers have varied interests depending on the time of a year or a day  As a result, there is also a need to cater to the time dependent changes in the consumer's interests while presenting information  Similarly, discovering new content is difficult when the consumer is overburdened with existing content 
Prior attempts to solve these problems allow consumers to create personalized sections in feed aggregation websites that are defined by keywords  Often, these personalized sections present any item that includes the keywords even though the item is not of interest to the consumer, per se  In another method, consumers are allowed to manually subscribe to Really Simple Syndication (RSS) feeds from multiple websites  This method often leads to the consumer viewing multiple items which contain redundant information 
In some examples, the specification describes a system and method for generating a stream of content for a channel using a channel application  The channel application includes a processing unit, a model generation engine, a scoring engine, a collaborative filtering engine, a content categorizer, a channel engine, and a user interface engine  The model generation engine generates a model that is used to determine suggestions for channels  The content categorizer categorizes new content items received from heterogeneous data sources  The channel engine identifies a channel category for a user based on at least one of a historical trend and a user activity  The historical trend is at least one of an increase in a number of new content items for a content category, an increase in a number of times one of the new content items is accessed and an event  A scoring engine queries the new content items based on the channel category and at least one other channel attribute  The scoring engine receives candidate content items that include the channel category and the at least one other channel attribute  The scoring engine then generates a stream of content from the candidate content items for the channel  The scoring engine transmits the stream of content to the channel engine, which generates a channel 
In one embodiment, the user interface engine generates a user interface for the user to define the channel category and the channel attribute  The scoring engine queries the new content items based on the user defined channel category and channel attribute and then generates the stream of content  In another embodiment, the channel engine enables the user to subscribe to an existing channel 
In one embodiment, the channel engine enables the user to share the channel with at least one of a friend of the user, a community, a group, and an internet user 
The specification is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which like reference numerals are used to refer to similar elements 
FIG  1A is a high-level block diagram illustrating one embodiment of a system for generating a stream of content for a channel 
FIG  1B is a block diagram illustrating one embodiment of a channel application 
FIG  2 is a high-level block diagram illustrating another embodiment of a system for generating a stream of content for a channel 
FIG  3A is a block diagram of one embodiment of the channel engine in more detail 
FIG  3B is a block diagram of one embodiment of the scoring engine in more detail 
FIG  4 is a graphic representation of a user interface that displays the stream of content of a channel 
FIG  5 is a graphic representation of a user interface that allows a user to define or customize a channel 
FIG  6 is a flow diagram of one embodiment of a method for generating a stream of content for a channel 
FIG  7 is a flow diagram of another embodiment of a method for generating a stream of content for a channel 
A system and method for generating a stream of content for a channel is described below  In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the specification  It will be apparent, however, to one skilled in the art that the embodiments can be practiced without these specific details  In other instances, structures and devices are shown in block diagram form in order to avoid obscuring the specification  For example, the specification is described in one embodiment below with reference to user interfaces and particular hardware  However, the description applies to any type of computing device that can receive data and commands, and any peripheral devices providing services 
Some portions of the detailed descriptions that follow are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory  These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art  An algorithm is here, and generally, conceived to be a self consistent sequence of steps leading to a desired result  The steps are those requiring physical manipulations of physical quantities  Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated  It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like 
It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities  Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining,” “displaying,” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices 
The specification also relates to an apparatus for performing the operations herein  This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer  Such a computer program may be stored in a computer readable storage medium, such as, but not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, flash memories including USB keys with non-volatile memory, or any type of media suitable for storing electronic instructions, each coupled to a computer system bus 
An embodiment can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements  A preferred embodiment is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc 
Furthermore, an embodiment can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system  For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device 
FIG  1A illustrates a block diagram of a system 100 for generating a stream of content for a channel according to one embodiment  The system 100 includes user devices 115 a, 115 n that are accessed by users 125 a, 125 n, a social network server 101, a third party server 107, a ratings server 139, an email server 141, an entertainment server 137, and a search server 135  The ratings server 139 includes websites for rating places, people or objects (e g  Google Hotpot)  The entertainment server 137 includes websites with entertaining information, such as news articles  In FIG  1A and the remaining figures, a letter after a reference number, such as “115 a” is a reference to the element having that particular reference number  A reference number in the text without a following letter, such as “115,” is a general reference to any or all instances of the element bearing that reference number  In the illustrated embodiment, these entities are communicatively coupled via a network 105 
In one embodiment, the channel application 103 a is operable on the social network server 101, which is coupled to the network via signal line 104  The social network server 101 also contains a social network application 109 and a social graph 179  Although only one social network server 101 is shown, persons of ordinary skill in the art will recognize that multiple social network servers 101 may be present  A social network is any type of social structure where the users are connected by a common feature, for example, Google+  The common feature includes friendship, family, work, an interest, etc  The common features are provided by one or more social networking systems, such as those included in the system 100, including explicitly-defined relationships and relationships implied by social connections with other online users, where the relationships form a social graph 179  In some examples, the social graph 179 reflects a mapping of these users and how they are related 
In another embodiment, the channel application 103 b is stored on a third-party server 107, which is connected to the network via signal line 106  The third-party server 107 includes software for generating a website (not shown)  In one embodiment, the notifying application generates a user interface that is incorporated into the website  Although only one third-party server 107 is shown, persons of ordinary skill in the art will recognize that multiple third-party servers 107 may be present 
In yet another embodiment, the channel application 103 c is stored on a user device 115 a, which is connected to the network via signal line 108  The user device 115 a is any computing device that includes a memory and a processor, such as a personal computer, a laptop, a smartphone, a cellular phone, a personal digital assistant (PDA), etc  The user 125 a interacts with the user device 115 a via signal line 110  Although only two user devices 115 a, 115 n are illustrated, persons of ordinary skill in the art will recognize that any number of user devices 115 n are available to any number of users 125 n 
The network 105 is a conventional type, wired or wireless, and may have any number of configurations such as a star configuration, token ring configuration or other configurations known to those skilled in the art  Furthermore, the network 105 may comprise a local area network (LAN), a wide area network (WAN) (e g , the Internet), and/or any other interconnected data path across which multiple devices may communicate  In yet another embodiment, the network 105 may be a peer-to-peer network  The network 105 may also be coupled to or includes portions of a telecommunications network for sending data in a variety of different communication protocols  In yet another embodiment, the network 105 includes Bluetooth communication networks or a cellular communications network for sending and receiving data such as via short messaging service (SMS), multimedia messaging service (MMS), hypertext transfer protocol (HTTP), direct data connection, WAP, email, etc  While only one network 105 is coupled to the user devices 115 a, 115 n, the social network server 101, and the third party server 107, in practice any number of networks 105 can be connected to the entities 
The channel application 103 receives data for generating a stream of content for a channel from heterogeneous data sources  In one embodiment, the channel application 103 receives data from a third-party server 107, a social network server 101, user devices 115 a, 115 n, a search server 135 that is coupled to the network 105 via signal line 136, an entertainment server 137 that is coupled to the network 105 via signal line 138, a ratings server 139 that is coupled to the network 105 via signal line 140 and an email server 141 that is coupled to the network 105 via signal line 142  In one embodiment, the search server 135 includes a search engine 143 for retrieving results that match search terms from the Internet  In one embodiment, the search engine 143 is powered by Google®  In one embodiment, the channel application 103 generates a model based on the data from the heterogeneous data sources, identifies a channel category based on a user's activities and historical trends, receives candidate content items that include the channel category from heterogeneous data sources, scores the candidate content items by comparing them to the model, and generates a stream of content for the channel 
Referring now to FIG  1B, the channel application 103 is shown in detail  FIG  1B is a block diagram of a computing device 200 that includes the channel application 103, a memory 237 and a processor 235  In one embodiment, the computing 200 device is a social network server 101  In another embodiment, the computing device 200 is a third party server 107  In yet another embodiment, the computing device 200 is a user device 115 a 
The processor 235 comprises an arithmetic logic unit, a microprocessor, a general purpose controller, or some other processor array to perform computations and provide electronic display signals to a display device  The processor 235 is coupled to the bus 220 for communication with the other components via signal line 236  Processor 235 processes data signals and may comprise various computing architectures including a complex instruction set computer (CISC) architecture, a reduced instruction set computer (RISC) architecture, or an architecture implementing a combination of instruction sets  Although only a single processor is shown in FIG  1B, multiple processors may be included  The processing capability may be limited to supporting the display of images and the capture and transmission of images  The processing capability might be enough to perform more complex tasks, including various types of feature extraction and sampling  It will be obvious to one skilled in the art that other processors, operating systems, sensors, displays, and physical configurations are possible 
The memory 237 stores instructions and/or data that may be executed by processor 235  The memory 237 is coupled to the bus 220 for communication with the other components via signal line 238  The instructions and/or data may comprise code for performing any and/or all of the techniques described herein  The memory 237 may be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, flash memory, or some other memory device known in the art  In one embodiment, the memory 237 also includes a non-volatile memory or similar permanent storage device and media such as a hard disk drive, a floppy disk drive, a CD-ROM device, a DVD-ROM device, a DVD-RAM device, a DVD-RW device, a flash memory device, or some other mass storage device known in the art for storing information on a more permanent basis 
In one embodiment, the channel application 103 comprises a processing unit 202, a model generation engine 207, a scoring engine 211, a collaborative filtering engine 217, a content categorizer 250, a channel engine 240, and a user interface engine 260 that are coupled to a bus 220 
The processing unit 202 is software including routines for receiving information about a user's interests, activities and social connections and for storing the information in the memory 237  In one embodiment, the processing unit 202 is a set of instructions executable by the processor 235 to provide the functionality described below for processing the information  In another embodiment, the processing unit 202 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the processing unit 202 is adapted for cooperation and communication with the processor 235, the model generation engine 207, and other components of the computing device 200 via signal line 222 
The processing unit 202 obtains information about users from user input and/or prior actions of a user across a range of heterogeneous data sources including search (such as web, video, news, maps, alerts), entertainment (such as news, video, a personalized homepage, blogs, a reader, gadget subscriptions), social activity (such as interactions through email, profile information, text messaging such as short message service (SMS), microblogs, geographical locations, comments on photos, a social graph and other social networking information), and activity on third-party sites (such as websites that provide ratings, reviews and social networks where users indicate that they approve of content)  This information is obtained, for example, from a user's search history, browsing history and other interactions with the Internet  The processing unit 202 stores the information with a designation of the source of the information 
In one embodiment, there are multiple processing units 202 that each receive data from a different heterogeneous data source  In another embodiment, the user information is received by the same processing unit 202  The processing unit 202 transmits the user information to memory 237 for storage  In one embodiment, the memory 237 partitions the user information from each heterogeneous data source in a separate data storage location  In another embodiment, the user information from heterogeneous data sources is stored in the same location in the memory 237  In yet another embodiment, the memory 237 partitions the model and the stream of content into separate storage locations as well 
The model generation engine 207 is software including routines for retrieving the user information from the memory 237 and generating a model based on the user information  In one embodiment, the model generation engine 207 is a set of instructions executable by the processor 235 to provide the functionality described below for generating the model  In another embodiment, the model generation engine 207 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the model generation engine 207 is adapted for cooperation and communication with the processor 235, the processing unit 202, the scoring engine 211, the channel engine 240 and other components of the computing device 200 via signal line 224 
The model generation engine 207 receives user information from a variety of sources including, for example, queries, clicks, news clicks, gadgets, email interactions, etc , extracts features from the information and generates a model based on the extracted features  The model determines the relevance of items to users, along with floating point values to indicate the extent to which the relevance holds  Examples include liking a source, a primary location and a list of interests  The interests are generated from explicit information and inferred information  Explicit information is derived, for example, from a user's list of interests on a social network or indicating that they liked a particular content item  Inferred information takes into account a user's activities 
The model generation engine 207 will infer that a user is interested in a particular subject, for example, if the subject matter appears in search terms  For example, the model generation engine 207 infers that a user who searches for information about different types of butterflies is interested in butterflies  The model generation engine 207 can even infer information based on the user's friends' activities  For example, content items that interest the user's friends might also interest the user  As a result, in one embodiment, the model includes the user's friends' interests 
In one embodiment, the model generation engine 207 also generates a model that contains several pieces of global meta-information about the user's consumption patterns including how frequently the user consumes the stream of content of a channel and global statistics on how likely the user is to reshare various types of items  Lastly, the model includes a sequence of weights and multipliers that are used to make predictions about the user's likelihood of clicking on, sharing or otherwise engaging with stream items 
The model generation engine 207 generates the model from the user information across the heterogeneous data sources  In one embodiment, the model generation engine 207 builds extensions to the model that employ the patterns of behavior of other users  For example, the model predicts the user's behavior based on the reaction of similar users  All the data that is derived from other users is anonymized before it is incorporated into the model 
In one embodiment, the model generation engine 207 generates a model based on user information, for example, based on the user's search history or third-party accounts  Alternatively, the model generation engine 207 receives periodic updates (one hour, one day, one week, etc ) from the heterogeneous data sources and in turn updates the model 
In yet another embodiment, the model generation engine 207 generates a model each time it receives a request for generating a stream of content for a channel  The advantage of this method is that the newest updates are included and the model is current  The disadvantage is that generating the model and then comparing the candidate content items to the model to generate the stream of content takes more time than comparing the candidate content items to a pre-existing model  The model generation engine 207 transmits the model to memory 237 for storage 
The content categorizer 250 is software including routines for receiving and categorizing new content items from heterogeneous sources according to at least one category and other features  In one embodiment, the content categorizer 250 is a set of instructions executable by the processor 235 to provide the functionality described below for receiving and categorizing new content items  In another embodiment, the content categorizer 250 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the content categorizer 250 is adapted for cooperation and communication with the processor 235, the scoring engine 211 and other components of the computing device 200 via signal line 227 
The content categorizer 250 receives new content items from heterogeneous data sources and annotates them with specific tags, such as features, global scores, etc  In this embodiment, the heterogeneous data sources include a search engine 143, an entertainment server 137, an email server 141, a ratings server 139, a social network server 101, and a third-party server 107  Once the items are annotated, the content categorizer 250 indexes each new content item based on the features and stores the content items in the memory 237  The new content items, in one embodiment, are indexed according to an identification format (MediaType#UniqueItemID, for example, “YOUTUBE#video_id” and “NEWS#doc_id”), an item static feature column that holds an item's static features (title, content, content classification, context, etc ), an item dynamic feature column that holds an item's dynamic features (global_score, number of clicks, number of following, etc ), a source (src) static feature column where the source is a publisher of an item (magazine in news, video uploading in YouTube, etc ) and a src dynamic feature column that holds the source's dynamic features  The content categorizer 250 categorizes the new content items to make their retrieval more efficient and fast 
The channel engine 240 is software including routines for generating a channel for a user  In one embodiment, the channel engine 240 is a set of instructions executable by the processor 235 to provide the functionality described below for generating a channel for a user  In another embodiment, the channel engine 240 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the channel engine 240 is adapted for cooperation and communication with the processor 235, the scoring engine 211, the model generation engine 207, the user interface engine 240, and other components of the computing device 200 via signal line 230 
In one embodiment, the channel engine 240 identifies a channel category for a user based on historical trends and the user's activities, interests and social connections  The channel engine 240 submits a request for a stream of content that includes the channel category and channel attributes to the scoring engine 211  The channel engine 240 then receives a stream of content from the scoring engine 211 and generates the channel  The generated channel is either public or private depending on the user's settings  The channel engine 240 is explained in greater detail below with regard to FIG  3A 
The scoring engine 211 is software including routines for generating a stream of content for a channel  In one embodiment, the scoring engine 211 is a set of instructions executable by the processor 235 to provide the functionality described below for globally scoring content items and for generating a stream of content for a channel  In another embodiment, the scoring engine 211 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the scoring engine 211 is adapted for cooperation and communication with the processor 235, the processing unit 202, the collaborative filtering engine 217, the model generation engine 207, the channel engine 240 and other components of the computing device 200 via signal line 228 
In one embodiment, the scoring engine 211 receives the request from the channel engine 240 and queries the new content items stored in memory 237  In another embodiment, the scoring engine 211 directly queries the heterogeneous data sources  The scoring engine 211 receives candidate content items that include the channel category and the channel attributes  The scoring engine 211 then compares the candidate content items to the model to determine whether the user would find the candidate content items interesting 
In one embodiment, the scoring engine 211 first performs the query and then compares the results to the model to determine whether the user would find them interesting  In another embodiment, these steps are performed simultaneously  In yet another embodiment, the scoring engine 211 compares candidate content items to the model and then filters the results according to the subject matter of the queries  The scoring engine 211 is explained in greater detail below with regard to FIG  3B 
The collaborative filtering engine 217 is software including routines for generating additional candidate content items for the channel through collaborative filtering and transmitting the additional candidate content items to the scoring engine 211 that were derived from collaborative filtering  In one embodiment, the collaborative filtering engine 217 is a set of instructions executable by the processor 235 to provide the functionality described below for generating additional candidate content items for the channel  In another embodiment, the collaborative filtering engine 217 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the collaborative filtering engine 217 is adapted for cooperation and communication with the processor 235, the scoring engine 211 and other components of the computing device via signal line 226 
The collaborative filtering engine 217 obtains additional candidate content items that are socially relevant from a stream of content derived from people with whom the user has a relationship and transmits them to the scoring engine 211  For example, the stream of content is derived from friends in a social network such as the social network application 109 or people that the user frequently emails  The more important that the person appears to be to the user, the more likely that the user will be interested in the candidate content item  Thus, in one embodiment, the collaborative filtering engine 217 applies a weight to candidate content items based on the social relationship of the user to the friend  For example, users that are friends receive higher weights than candidate content items from second generation friends of the user (i e , a friend of a friend)  In one embodiment, the collaborative filtering engine 217 receives information about relationships between users from the social graph 179 
The collaborative filtering engine 217 increases the weights applied to candidate content items from friends when the user positively responds to the items  For example, if the user comments on the item or indicates that the user found the item interesting, the collaborative filtering engine 217 increase the weight so that more candidate content items from the friend become part of the stream of content 
The user interface engine 260 is software including routines for generating a user interface that, when rendered on a browser, displays a channel generated for a user and enables the user to customize the channel  In one embodiment, the user interface engine 260 is a set of instructions executable by the processor 235 to provide the functionality described below for generating a user interface  In another embodiment, the user interface engine 260 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the user interface engine 260 is adapted for cooperation and communication with the processor 235, the channel engine 240 and other components of the computing device 200 via signal line 232 
The user interface engine 260 receives instructions from the channel engine 240 for generating a display  The user interface includes options for viewing a channel, requesting a new channel, modifying the user interests, and following suggested channels 
FIG  2 is a high-level block diagram illustrating another embodiment of a system for generating a stream of content for a channel  In this embodiment, the components of the channel application 103 are divided among various servers so that the information is efficiently processed  The system includes a search server 135, an entertainment server 137, a ratings server 139, an email server 141, a content categorizer 250, a data storage server 265, a model server 255, a scoring server 262, a social network server 101, a user device 115, and a channel application 103 
A content categorizer 250 crawls the heterogeneous data sources (search server 135, entertainment server 137, ratings server 139, and email server 141) are crawled for new content items by the content categorizer 250 or the new content items are directly transmitted to the content categorizer 250 
The content categorizer 250 categorizes the new content items as mentioned above with regards to FIG  1B and stores them in the database 267 of the data storage server 265  The content categorizer 240 also includes a processing unit 202 for processing user information (activities, interests and social connections)  In one embodiment, the processing unit 202 stores the database 267 
In one embodiment, the data storage server 265 dynamically phases out the old content items  For example, news items expire after 24 hours, videos expire after 48 hours and feeds are kept for 24 hours or only the 10 most recent items, whichever is larger, etc 
The content categorizer 250 also transmits the new content items to the scoring server 262 for a global user ranking  The global scores are transmitted from the scoring server 262 to the data storage server 265, which stores the global scores in association with the new content items  The global scores are helpful for organizing the new content items in the data storage server 265 according to the more popular items 
Turning now to the model server 255, the model server 255 receives the user's activity, interests and social connections from the processing unit 202 or the data storage server 265  The model generation engine 207 generates a model based on user input and/or prior actions  The model server 255 transmits a model to the scoring server 262 and the channel application 103 periodically or upon request 
The channel application 103 includes a channel engine 240 and a user interface engine 260  In one embodiment, the channel engine 240 requests the model from the model server 255 and identifies a channel category that a user would find interesting  The channel engine 240 then transmits a request for a stream of content to the scoring server 262  The channel engine 240 receives the stream of content from the scoring server 262 and generates the channel  The user interface engine 260 generates a user interface for displaying a user interface that includes the channel and transmits it to the user device 115  In addition, the user interface engine 260 generates a user interface to allow the user to customize the channel or define a new channel  These user interfaces are explained in greater detail below with regard to FIGS  4-5 
In one embodiment, the channel engine 240 transmits a query based on the channel category to the scoring server 262  The scoring server 262 queries and receives candidate content items from the data storage server 265  The scoring server 262 also queries and receives candidate content items from the social network server 101  The candidate content items from the social network server 101 are pre-scored by the collaborative filtering engine 217 and, in one embodiment, the unread candidate content items are saved to a cache on the social network server 101  These items are saved to a cache because the quantity of social updates can be large enough that performing the scoring during write time enables faster reads 
In one embodiment, the scoring engine 211 requests the model from the model server 255  The scoring server 262 then compares the candidate content items to the model and scores the candidate content items  The scoring engine 211 compares the candidate content items received from the social network server 101 to the model and rescores them according to the model  In another embodiment, the scoring engine 211 scores the candidate content items according to the category and any keywords associated with a channel  In either embodiment, the scoring engine 211 generates a stream of content based on the scored candidate content items and transmits the stream of content to the channel application 103 
Referring now to FIG  3A, one embodiment of a channel engine 240 is shown in more detail  The channel engine 240 includes a historical analyzer 372, a category identifier 374, a subscription module 376 and a channel generator 378 that are each coupled to signal line 230 
The historical analyzer 372 is used to identify when a user will be interested in a particular category  The historical analyzer 372 identifies, for example, a time of the day or a year that a user will be interested in a category by analyzing historical trends associated with the category  In one embodiment, the historical analyzer 372 performs such analyses by measuring the increase or decrease in the number of new content items that are categorized under a content category or by measuring an increase or decrease in the number of times a new content item is accessed  For example, the number of times a tutorial on filing taxes is accessed would be very high during February-April  In another embodiment, the historical analyzer 372 also keeps track of events such as holidays, festivals, etc  Tracking such events is advantageous as, for example, many users might be interested in costume rentals during Halloween or camping during the Memorial Day and July 4th weekends 
The category identifier 374 identifies a channel category for a user based on the user's interests, activities and social connections  In one embodiment, the category identifier 374 requests the model generated by the model generation engine 207 to identify the channel category  For example, the category identifier 374 identifies sports cars as a channel category because it is an explicit interest of the user  The category identifier 374 suggests channels including a source, a category, keywords, a media type, a size of a content item, and a location for a channel  For example, for a user that is interested in foreign politics, especially relations between the United States and China, the category identifier 374 suggests the category of U S  and Chinese relations (e g , entity=“us_china_relations”), keywords such as trade and deficit because the user is particularly interested in the economic aspect of the relationship between China and the United States, a source such as The Economist (source=“economist com”) because the user prefers The Economist over U S  media outlets and the media being news articles because the user does not enjoy viewing videos 
In one embodiment, the category identifier 374 uses the analyses of the historical analyzer 374 for identifying a channel category for the user  This is advantageous as a user who has searched for US taxes might not be interested in knowing about it throughout the year, but it is beneficial for the user to have a separate channel for US taxes during the tax filing season  In yet another embodiment, the category identifier 374 uses contextual cues of the user for identifying channel categories  For example, the category identifier 374 identifies skiing in Switzerland as a channel category because winter sports is listed as an interest of the user and the user's current IP address is in Switzerland 
The subscription module 376 enables a user to subscribe to existing channels that are public  In one embodiment, the subscription module 376 enables a user to subscribe to a pre-defined channel (such as breaking news, most popular videos, updates from a social group, etc )  The channel application 103 generates the stream of content for pre-defined channels based on global scores of the new content items  Subscribing to pre-defined channels such as breaking news is advantageous as it helps the user to keep apprised of current information and discover new interests  Furthermore, because in one embodiment the breaking news channel is personalized since the content items are compared to a model for the user, the breaking news channel is more relevant than simply a list of popular or recent news items 
In another embodiment, the subscription module 376 enables a user to subscribe to another user's channel (a friend, a famous person, etc ) that is public  Subscribing to another user's channel is advantageous because, for example, a user who is interested in the stock market will benefit by viewing the stream of content that is viewed by a famous stock market analyst  In yet another embodiment, the subscription module 376 enables the user to search for channels that are public using the search engine 143  The subscription module 376, suggests such channels that are viewed by other users based on the interests of the user  In another embodiment, the subscription module 376 communicates with the collaborative filtering engine 217 to suggest channels viewed by other users with whom the user has a relationship 
The channel generator 378 submits a request for a stream of content for a channel to the scoring engine 211  The request includes the channel category identified by the category identifier 374 and channel attributes  The channel attributes include any attribute known to a person with ordinary skill in the art such as a source, presence of keywords, absence of keywords, a media type, a location, a time, a size of a content item, a date, etc  In one embodiment, the channel category and the channel attributes are defined by the user  In another embodiment, channel generator 378 defines the channel attributes for the channel category based on the user's preferences and activities  For example, if a user always reads news articles and seldom watches news videos, the channel generator 378 would define the media type for the channel as text based articles  At any point in time, the user can customize both the channel category and the channel attributes  The channel generator 378 then resubmits the request based on the changes made by the user 
In response to the request, the channel generator 378 receives a stream of content from the scoring engine 211 and generates the channel for the user  The generated channel is either public or private depending upon the user's preferences  In one embodiment, the user shares the channel to a community, a group of people or any internet user  The channel is then displayed to the user with an interface generated by the user interface engine 260 
Referring now to FIG  3B, one embodiment of a scoring engine 211 is shown in more detail  The scoring engine 211 includes a query generator 301, a global scorer 302 and a content stream generator 304 that are each coupled to signal line 228 
The global scorer 302 is used to rank new content items that are stored in the data storage server 265 or memory 237 (depending upon the embodiment)  The global scorer 302 uses signals from the different verticals to compute a global user-independent score for each item to approximate its popularity or importance within the stream that produced it  The global scorer 302 normalizes the score across streams so that items from various streams are comparable to aid in generating a quick yet reasonable ranking of items  The global score is a combination of its quality specific to the source stream (depending on the rank of the source, number of known followers of a source, etc ) and its global popularity (trigger rate on universal search, relevance to trending queries, number of clicks, long clicks received, etc ) 
The global scorer 302 transmits the global score to storage where it is associated with the item  The global score helps rank the items for faster retrieval  For example, if the query generated by the query generator 301 includes a request for the top ten items about skiing, those items are already organized in the data storage server 265 or memory 237 according to the global score 
The query generator 301 receives a request for a stream of content for a channel from the channel engine 240  The query generator 301 generates a query based on the channel attributes that are included in the request  The query generator 301 queries the data storage server 265 or memory 237 depending upon the embodiment  The following is an example query generated by the query generator 301: ((Category: Politics) AND (global_score>80) AND (source: NewsWebsite) AND (media type: Text)) 
The content stream generator 304 receives candidate content items that include the channel attributes  The content stream generator 304, for the above mentioned query, receives text based articles that include the channel category politics and have a global score greater than 80  Additionally, the text based articles are from the source NewsWebsite  In one embodiment, the content stream generator 304 generates the stream by ordering the content items in order of their scores  In another embodiment, the content stream generator 304 determines an interestingness of each candidate content item to the user  The content stream generator 304 determines the interestingness by comparing the candidate content items with a model generated for the user by the model generation engine 207 and scoring them 
where p is a property, that is, a setting A=a of the attributes  The latter quantity, Pr(p|user) is approximated from the user's history of interactions with content items as well as the user's search history and other opt-in data  Similarly, the former quantity, Pr(item|p) is approximated by the (suitably weighted) reciprocal of the number of items with property p (e g , if it is expected that p=((Politics) AND (global_score>80) AND (source: NewsWebsite) AND (media type: Text)) to generate 300 items, take Pr(item|p) to be 1/300) 
where the properties p are summed over single-attribute properties (as opposed to all possible settings of an entire collection of attributes), and G is an exponential function of the form G(x)=2(100 x), so that when applied in this form, if there are several values of p for which Pr(item|p) Pr(p|user) is large, the sum of their G-values slowly increases 
Once the scores are calculated, the content stream generator 304 generates a stream of content for the channel that is ordered according to the candidate content item scores  In one embodiment, only the candidate content items that exceed a certain threshold are included in the stream of content for the channel 
Turning now to the user interface engine 260, FIG  4 is a graphic representation 400 of a user interface generated by the user interface engine 260 for displaying the stream of content of a channel  In this example, the user interface 400 also includes channels 405 that are pre-defined, channels 410 that are suggested for the user and channels 415 that are subscribed to by the user  The user can also define new channels and attributes by clicking the link 420 
The example includes the stream of content for the user's soccer channel 425  The stream of content includes news items 445, videos 450 and social network news feeds 455 from the content sources 440 defined by the user  The candidate content items are listed in decreasing order of their scores  The user interface engine 260 lists five candidate content items with the highest scores in the hot items section 430  The remaining candidate content items are listed in the other items section 435  In another embodiment, the entire stream of content is listed in a single section 
FIG  5 is a graphic representation 500 of a user interface that is generated by the user interface engine 260 for a user to define a new channel or customize an existing channel  In this example, the user interface includes all the channel categories 505 that have been either pre-defined, suggested to the user, or subscribed by the user, and the content sources 510 for each channel category  The user customizes a channel by adding or removing content sources for the channel  In one embodiment, the user edits more advanced channel attributes such as media type, size of the content items, etc  by clicking on the link 515  The user makes the channel public, private or restricts it to a group of people by clicking on link 520  Additionally, the user can also define a new channel by adding a new channel category 
Referring now to FIGS  6-7, various embodiments of the method of the specification will be described  FIG  6 is a flow diagram 600 of one embodiment of a method for generating a stream of content for a channel  The channel engine 240 defines 602 a channel category and submits a request for a stream of content  The request includes channel attributes including any of a category, a source, keywords, a media type, a location, a size of a content item, and a date  The channel category is defined based on a model for a user that is generated by the model generation engine 207 or the channel is defined by a user  The scoring engine 211 receives 604 the request including the channel category and generates 606 a stream of content based on the channel category  The channel engine 240 generates 608 a channel with the stream of content and transmits it to the user 
FIG  7 is a flow diagram 700 of another embodiment of a method for generating a stream of content for a channel  The content categorizer 250 categorizes 702 new content items that are received from heterogeneous data sources  The new content items that are received from heterogeneous data sources include, for example, news articles, microblogs, blogs, videos, photos, etc  The content categorizer 250 categorizes the content according to a category and other features  The content categorizer 250 also stores 704 the new content items in a data storage server 265 or a memory 237, depending upon the embodiment  The global scorer 302 generates 706 a global score for each new content item  The category identifier 374 identifies 708 a channel category for a user based on the user's activities and a historical trend identified by the historical analyzer 372  The user's activity includes a search (such as web, video, news, maps, alerts), entertainment (such as news, video, a personalized homepage, blogs, a reader, gadget subscriptions), social activity (such as interactions through email, profile information, text messaging such as short message service (SMS), microblog, comments on photos, a social graph, and other social networking information), and activity on third-party sites (such as websites that provide ratings, reviews and social networks where users indicate that they approve of content) In one embodiment, the category identifier 374 also uses contextual information of the user to identify the channel category 
The query generator 301 generates a query based on the channel category and the channel attributes and queries 710 the new content items stored on the data storage server 265  The content stream generator 304 receives 712 candidate content items that include the channel category and channel attributes  In one embodiment, the content stream generator 304 receives additional candidate content items from the collaborative filtering engine 217 
The content stream generator 304 scores 714 each candidate content item by comparing it to a model generated by the model generation engine 207  The score is calculated by determining an interestingness of the candidate content item to the user  The content stream generator 304 then generates 716 the stream of content based on the scores for each candidate content item  The channel engine 240 then generates 718 a channel with the stream of content and transmits it to the user 
The foregoing description of the embodiments has been presented for the purposes of illustration and description  It is not intended to be exhaustive or to limit the specification to the precise form disclosed  Many modifications and variations are possible in light of the above teaching  It is intended that the scope of the embodiments be limited not by this detailed description, but rather by the claims of this application  As will be understood by those familiar with the art, the examples may be embodied in other specific forms without departing from the spirit or essential characteristics thereof  Likewise, the particular naming and division of the modules, routines, features, attributes, methodologies, and other aspects are not mandatory or significant, and the mechanisms that implement the description or its features may have different names, divisions and/or formats  Furthermore, as will be apparent to one of ordinary skill in the relevant art, the modules, routines, features, attributes, methodologies, and other aspects of the specification can be implemented as software, hardware, firmware, or any combination of the three  Also, wherever a component, an example of which is a module, of the specification is implemented as software, the component can be implemented as a standalone program, as part of a larger program, as a plurality of separate programs, as a statically or dynamically linked library, as a kernel loadable module, as a device driver, and/or in every and any other way known now or in the future to those of ordinary skill in the art of computer programming  Additionally, the specification is in no way limited to implementation in any specific programming language, or for any specific operating system or environment  Accordingly, the disclosure is intended to be illustrative, but not limiting, of the scope of the specification, which is set forth in the following claims 
providing, with the one or more processors, the customized stream of content 
2  The computer-implemented method of claim 1 comprising removing pre-existing content items included in the customized stream of content for the channel 
3  The computer-implemented method of claim 1 wherein the historical trend is one of an increase in a number of the new content items for a content category and an increase in a number of times one of the new content items is accessed 
categorizing the new content items 
5  The computer-implemented method of claim 3 wherein the heterogeneous data sources include at least two from the group of a news article post, a news feed, a social feed, a blog post, a micro-blog post, a photo, a video, an audio, an email message, and a text based message 
6  The computer-implemented method of claim 1 comprising receiving a request from the user to subscribe to an existing channel 
7  The computer-implemented method of claim 1 wherein the channel category is also based on an interest of the user and a connection of the user 
8  The computer-implemented method of claim 1 wherein the user activity is an interaction of the user with an application, wherein the interaction of the user with the application includes providing at least one of a user preference, a user interest, a comment, a tag, and a search 
provide the customized stream of content 
10  The computer program product of claim 9, wherein the computer readable program when executed on the computer also causes the computer to remove pre-existing content items included in the customized stream of content for the channel 
11  The computer program product of claim 9, wherein the historical trend is one of an increase in a number of the new content items for a content category and an increase in a number of times one of the new content items is accessed 
categorize the new content items 
13  The computer program product of claim 12, wherein the heterogeneous data sources include at least two from the group of a news article post, a news feed, a social feed, a blog post, a micro-blog post, a photo, a video, an audio, an email message, and a text based message 
14  The computer program product of claim 9, wherein the computer readable program when executed on the computer also causes the computer to receive a request from the user to subscribe to an existing channel 
15  The computer program product of claim 9, wherein the channel category is also based on an interest of the user and a connection of the user 
16  The computer program product of claim 9, wherein the user activity is an interaction of the user with an application, wherein the interaction of the user with the application includes providing at least one of a user preference, a user interest, a comment, a tag, and a search 
18  The system of claim 17 wherein the system is further configured to remove pre-existing content items included in the customized stream of content for the channel 
19  The system of claim 17 wherein the historical trend is one of an increase in a number of the new content items for a content category and an increase in a number of times one of the new content items is accessed 
21  The system of claim 20 wherein the heterogeneous data sources include at least two from the group of a news article post, a news feed, a social feed, a blog post, a micro-blog post, a photo, a video, an audio, an email message, and a text based message 
22  The system of claim 17 wherein the system is further configured to receive a request from the user to subscribe to an existing channel 
23  The system of claim 17 wherein the channel category is also based on an interest of the user and a connection of the user 
24  The system of claim 17 wherein the user activity is an interaction of the user with an application, wherein the interaction of the user with the application includes providing at least one of a user preference, a user interest, a comment, a tag, and a search 
Adamic et al , "Search in power-law networks," Physical Review E, 2001, vol  64, HP Labs/Stanford University, The American Physical Society 
Boyd, et al , "Social Network Sites: Definition, History, and Scholarship," Journal of Computer-Mediated Communication, International Communication Association, 2008, pp  210-230  8 
MediaSift Ltd , DataSift: Realtime Social Data Mining Platform, Curate and Data Mine the Real Time Web with DataSift, Dedipower, Managed Hosting, May 13, 2011, 1 pg 
Ring Central, Inc , Internet, retrieved at http://www ringcentral com, Apr  19, 2007, 1 pg  28 
Singh et al , "CINEMA: Columbia InterNet Extensible Multimedia Architecture," Department of Computer Science, Columbia University, May 2002 pp  1-83 
Yu et al , "It Takes Variety to Make a World: Diversification in Recommender Systems," 2009, pp  1-11, downloaded from https://openproceedings org/2009/conf/edbt/YuLA09 pdf 
Context 2: The Official 2006 NBA Draft Early-Entry List  30 International Players, 62 underclassmen and one lone 5th year high school player make up this year's list, for a total of 93 early-entrants  Extensive commentary and early draft status projections are included   For comparison, in 2005, 108 players declared (61 NCAA, 12 high school, 35 internationals), up from 94 in 2004, and 73 in 2003 
There were no major surprises on the early-entry list, besides a few mid-major, Division II and NAIA players that decided to enter, as well as 5th year high school player Clarence Holloway  Amongst the International players, Hrvoje Peric, Renaldas Seibutis, and Kyrylo Fesenko are considered mild surprises who could end up cracking the 2nd round  NCAA Lottery prospects Joakim Noah, Al Horford, Corey Brewer, Josh McRoberts, Brandon Rush and Tyler Hansbrough decided to sit this one out as expected, as did Marco Belinelli, Uros Tripkovic and Goran Dragic on the international front 
Akbar Abdul-Ahad 6-0, PG, Idaho State Junior No Undrafted Averaged under 6 points in 20 minutes per game playing in the Big Sky  Being the first player on the NBA Draft Early-Entry list will likely go down as the highlight of his basketball career 
Arron Afflalo, 6-5, SG, UCLA Sophomore No Undrafted Afflalo initially told the LA media hes returning to school, but after a deep run in the NCAA tournament-- more in spite of his play than because of it--Afflalo will be testing the waters  Afflalo has very average size, athleticism, perimeter shooting and ball-handling skills  Hes clearly receiving bad advice on where his stock lies 
LaMarcus Aldridge, 6-11, PF/C, Texas Sophomore Yes Top 5 pick Aldridge made his announcement official to enter the draft some weeks ago  He will hire an agent soon (Arn Tellem?) and is considered a lock for the top 5 and a strong candidate for #1 overall 
Morris Almond, 6-6, SG, Rice Junior No ??? Almond announced hell be entering the draft, without an agent  He might be the best scorer in the NCAA youve never heard about  His stats are terrific, despite being the sole focal point of opposing defenses, and hes capable of scoring in a variety of ways, particularly with his jumper  Hes hoping for an invite to Orlando 
Renaldo Balkman, 6-8, PF, South Carolina Junior No Undrafted After winning the NIT MVP award, Balkman has decided to see where he stands in the eyes of the NBA by testing the waters  Hes likely to find them downright freezing, as hes a skinny and undersized power forward with little to no skills who came off the bench for a very average team 
Larry Blair,6-1, SG, Liberty Junior No Undrafted The 22 point per game scorer Blair is attempting to get some exposure for himself by testing the waters 
Will Blalock, Iowa State, 5-11, PG, Junior No Second round pick? Declared for the draft together with Curtis_Stinson after Iowa States coach was fired  Size is a big question mark  Will likely hope to attend the pre-draft camp in Orlando and try to show scouts hes a 1st rounder  Likely returns for his senior year 
Jahsha Bluntt, 6-6, SG, Deleware State Junior No Undrafted Puts up fairly average numbers (14 6 ppg, 41% FG) in one of the worst conferences in America  Looking for exposure at the Orlando pre-draft camp but its highly unlikely to receive it 
Josh Boone, 6-10, PF/C, UConn Junior No First round pick? Boone announced hell be entering the draft without an agent  An up and down season has left his stock in the air, and will likely force him to prove himself at the Orlando pre-draft camp  Would greatly benefit from a productive senior season as an offensive focal point now that UConn has lost almost all of its firepower from last year 
Ronnie Brewer, 6-6, PG/SG, Arkansas Junior No Lottery pick? After initially wavering a bit on his decision, Brewer announced hell be entering the draft without an agent in a press conference  Brewer is considered a likely late lottery pick to mid-first rounder pick, as his physical attributes and array of versatile skills on both ends of the floor are highly sought after 
Bobby Brown, 6-1, PG, Cal-State Fullerton Junior No First round pick? DraftExpress exclusively reported that Brown will be testing the waters  Still considered a bit of a sleeper because of the school he plays for, he will not be hiring an agent at this point  Some scouts are very high on his quickness and perimeter shooting ability and feel he will help his stock tremendously in private workouts 
Shannon Brown, 6-4, SG, Michigan State Junior No First round pick? As exclusively reported by DraftExpress, Brown will be testing the waters  He will likely conduct a number of workouts and attend the Orlando pre-draft camp to attempt and gauge where his stock lies  Scouts compare him to Celtic guard Tony Allen, but with a better attitude  Hes a very borderline first rounder in a draft that is stacked with shooting guards 
Derek Burditt, 6-7, SG, Blinn Junior College Sophomore No Undrafted Unknown Junior College prospect  Not ranked as one of the top 25 JUCO players in the country, averaged around 17 points per game  Not burning his draft card as hes not yet an NCAA player, so really doesnt have much to lose, or gain 
Leroy Dawson, 6-2, SG, Emporia State Junior No Undrafted Anonymous Division II player from the MIAA conference  2nd team all conference, averaged 20 points per game  Like MANY on this list, only declaring because he can and has nothing to lose 
Travis DeGroot, 6-4, SG, Delta State Junior No Undrafted Plays in a strong Division II conference, but is at best only the 3rd best prospect on his own team after Jasper Johnson and Jeremy Richardson, and is therefore not a prospect at all 
Guillermo Diaz, 6-2, PG/SG, Miami Junior Yes First round pick? As reported by DraftExpress all year long, Diaz decided to forgo his senior year of college by hiring an agent, Miami based Jason Levien  One of the top athletes and shooters in the draft, which makes for an intriguing combination 
Cem Dinc, 6-10, SF/PF, Indiana Freshman No Undrafted As exclusively reported by DraftExpress, Dinc will be testing the waters  The coach that recruited him and then never played him, Mike Davis, resigned, so it would not shock anyone to see Dinc return to play in Europe and become automatically eligible next year after pulling out of this years draft 
Quincy Douby, 6-3, PG/SG, Rutgers Junior No First round pick As exclusively reported by DraftExpress, Douby sent out his paperwork to enter the draft  NBA scouts are all over the board on him, with some saying they consider him a 2nd round pick and others saying they would not be surprised if he ended up in the lottery  Terrific shooter and shot creator, averaged 28 ppg in the Big East conference  A real sleeper who will likely play in Orlando 
Mike Efevberha, 6-5, SG, Cal State Northridge Junior ??? Undrafted Ramona Shelburne of the LA Daily News reported that Efevberha will be testing the waters  Efevberha was the leading scorer in the country until he had a falling out with his coach and saw his playing time reduced significantly  Hell likely be looking for an invite to the Orlando pre-draft camp, and does not appear to be likely to head back to school 
Carl Elliot, 6-4, PG, George Washington Junior No Undrafted Elliot is using his use it or lose it draft card as a junior to get some exposure for himself through workouts and try to figure out where he stands in the eyes of the NBA  Elliot has excellent size for the PG position, but is still lacking plenty of all-around polish  His senior year will be essential to his development as a player  Reportedly has a family to support, which makes his decision tough considering how old he is already, despite only being a junior 
Jordan Farmar, 6-2, PG, UCLA Sophomore No First round pick? Farmar was the engine that led his team to the Finals of the NCAA tournament, and the only player that showed up once they got there  He is one of the top playmakers in the country, a Steve Nash type point guard, but his average athleticism, defense and outside shooting means hes only a bubble first-rounder  DraftExpress has been on his bandwagon since day one at UCLA, but is the NBA on it too?
Nick Fazekas, 6-11, PF, Nevada Junior No First round pick? Fazekas announced hell be entering the draft without an agent and will likely return to Nevada if it looks like hes not going to be a first round pick  If hes not a first rounder this year, its hard to imagine him ever being one since there isnt much left for him to accomplish individually in the NCAA  An interesting candidate for the pre-draft camp in Orlando 
Thomas Gardner, 6-5, SG, Missouri Junior No Second round pick? The St  Louis Post Dispatch reported that Gardner will enter the draft  Firing of underachieving Missouri coach Quin Snyder appeared to be the straw the broke the camels back  Gardner will have to hope to get invited to Orlando, but moving into the first round appears unlikely without an incredible performance there 
Rudy Gay, 6-8, SF, UConn Sophomore Yes Top 10 pick Gay announced hes leaving UConn at a press conference on campus, with Coach Calhoun by his side  He will hire an agent eventually  Size, length, incredible talent and athleticism means he might have the most upside of any player in this draft  Does he have the fire to capitalize on it though?
Reggie George, 6-10, PF, Robert Morris Chicago (NAIA) Junior No Undrafted Transfer from Iowa State had a nice season in the NAIA and is looking to capitalize on it by gaining some exposure for himself 
Daniel Gibson, 6-2, PG/SG, Texas Sophomore ??? Second round pick As exclusively reported by DraftExpress, Gibson will be entering the draft  There appears to be a conflict between Gibson and Texas regarding what his role will be next year, specifically whether or not hell be playing the point, meaning its unclear whether or not hell be returning  Gibson will likely go to Orlando to help him decide what his next step is  Showing off some PG skills will be essential there 
Aaron Gray, 7-0, Center, Pitt Junior No First round pick? After a disappointing end to his season, being outplayed by Patrick OBryant in the NCAA tournament, Gray has put that behind him and entered his name in the draft without an agent  Hes yet another underclassmen with huge questions marks about his pro potential that will likely have to go to the Orlando pre-draft camp to show he is worthy of a first round pick  Made some great strides this year, but still has a ways to go, especially conditioning-wise 
LeShawn Hammett 6-0, PG, St  Francis Junior No Undrafted Undersized combo guard played only 7 minutes in the mighty Northeast Conference before being suspended indefinitely for conduct detrimental to team  The NBA is clearly the only goal left for him to achieve 
Brandon Heath, 6-3, PG/SG, San Diego State Junior No Second round pick? Streaky shooting combo guard Heath announced the he will test the NBA draft process this summer, and is hoping for an invite to the Orlando pre-draft camp  MWC player of the year; has a lot of wrinkles to his game that need to be ironed out before he can legitimately think about the NBA 
Tedric Hill, 6-10, PF, Gulf Coast Community College Sophomore Yes Undrafted Ineligible to return to school after flunking out of college once again  Has bounced around over the past few years, and received some early hype from wannabe draftniks such as Gregg Doyel (CBS-Sportsline) and Sam Smith (Chicago Tribune) who compare him to Kevin Garnett  Very athletic we're told, but has absolutely no idea how to play the game  Has no chance of being drafted without an amazing showing at the Orlando pre-draft camp 
Clarence Holloway 7-0, Center, IMG Academy (Prep School) 5th year High School No Undrafted Lone high school player in this years age-limit depleted draft  Former Louisville commit never got eligible for college and was always considered too slow and heavy to make much of an impact anyway  Reportedly lost weight and improved his grades this past year at IMG and is currently being recruited by UConn, Kansas State and Oklahoma, amongst others 
Ekene Ibekwe, 6-9, PF, Maryland Junior No Undrafted Sources told DraftExpress exclusively that Ibekwe will be testing the waters  Likely only making this move because he can, as his chances of being drafted are very low  Athletic and long, but still lacking any type of polish 
Donald Jeffers, 6-8, PF, Roxbury Community College Sophomore No Undrafted Anonymous junior college player 
Alexander Johnson, 6-9, PF, Florida State Junior Yes First round pick? Sources told DraftExpress, that Johnson will be hiring an agent, mainly because he is already 23 years old  Hes considered intriguing because of his strength, raw offensive tools and freakish athleticism at the 4 position, and could work his way into the 1st round with strong workouts 
David Johnson, 6-7, PF, Clinton Junior College Sophomore No Undrafted 6-7 JUCO power forward who averaged 2 points and 3 rebounds per game 
Trey Johnson, 6-5, SG, Jackson State Junior No Undrafted Small school prolific scorer and one of the most accurate perimeter shooters in the country will attempt to draw some more attention to himself by testing the waters this summer  Johnson is hoping for a chance to prove himself in the Orlando pre-draft camp in June 
Coby Karl, 6-4, PG/SG, Boise State Junior No Undrafted Son of Denver Nuggets head Coach George Karl put up nice numbers (17 ppg, 5 rebs, 4 assists, 39 5% 3P) in the underrated WAC conference  Had surgery in March to remove a cancerous lump from his thyroid 
Mark Konecny, 6-10, Center, Lambuth (NAIA) Junior No Undrafted Transfer from Syracuse with mediocre production is looking for any type of exposure he can get before he graduates next season 
Kyle Lowry, 6-1, PG, Villanova Sophomore No First round pick NCAA tournament performance showed that he definitely needs another year, but regardless, Lowry is in  For now its without an agent  Considering the lack of quality point guard prospects in this draft, Lowry is likely a first round pick  Says he will attend the Orlando pre-draft camp if invited 
Aleks Maric, 6-11, Center, Nebraska Sophomore No Undrafted As exclusively reported by DraftExpress, Maric will be testing the waters  What may have played a role in this is the fact that the assistant coach that recruited him at Nebraska, Scott Spinelli, just moved on to Wichita State  Maric is considered a very average athlete who is still very raw and is therefore likely to go undrafted should he decide to stay in  Thanks to his Croatian passport, there is money waiting for him overseas if he chooses to take it 
Japhet McNeil, 5-10, PG, East Carolina Junior No Undrafted Severely undersized PG averaged 4 points and 5 6 assists in watered down Conference USA 
Paul Millsap, 6-8, PF, Louisiana Tech Junior Yes First round pick? As expected, Millsap has declared his intentions to enter the NBA draft, and according to sources hired an agent as well  Millsap has likely achieved just about everything he can in college at this point, and will land somewhere in the 20-40 part of the draft depending on workouts and measurements 
Matt Mitchell, 6-0, PG, Southern University-New Orleans Junior No Undrafted Anonymous NAIA player 
Adam Morrison, 6-8, SF, Gonzaga Junior Yes Top 5 pick As DraftExpress exclusively reported that Morrison will be declaring for the draft and hiring Chicago based agent Mark Bartelstein  Morrison, the top scorer in college basketball, is expected to be a top 5 pick and potentially the #1 pick overall  Questions linger about his athleticism and defense, but no one questions his passion, talent or feel for the game 
Patrick O'Bryant, 7-0, Center, Bradley Sophomore Likely First round pick NBA sources in Portsmouth told DraftExpress exclusively that OBryant will be testing the waters without an agent, but is likely to go all the way once he hears that hes a lock for the 1st round  His steady improvement, strong sophomore season, outstanding NCAA tournament and considerable upside means hes probably gone  O'Bryant since confirmed both DraftExpresss reports, particularly the one about hiring an agent in the Tri-State area (Andy Miller) should he decide to go all the way 
Evan Patterson, 6-7, SF, Texas Wesleyan Junior No Undrafted Mediocre numbers (11 ppg, 2 rebs) in a mediocre Southland conference 
Danilo Pinnock, 6-5, SG, George Washington Junior No Undrafted The extremely athletic Pinnock has told GWs student paper hell be testing the waters  Pinnock will attempt to capitalize on his teams success this year by potentially attending the NBA pre-draft camp in Orlando  Pinnock will have to show better ball-handling and perimeter shooting ability than he did during the regular season 
Leon Powe, 6-7, PF, Cal Sophomore No Second round pick Powe announced hell be testing the waters in a statement released by Cal  Where he ends up being projected depends heavily on how his knee checks out  Powe is already considered a serious tweener by NBA scouts, and had a hard time this season gaining back much of the explosiveness he had earlier in his career  Could realistically go undrafted should he decide to stay in 
Richard Roby, 6-5, SG, Colorado Sophomore Likely Second round pick As first indicated by DraftExpress Roby has decided to test the waters  Disappeared against any major competition he went up against, particularly towards the end of the season  Roby will likely have to put on weight in the next few months and show off his perimeter stroke in the Orlando pre-draft camp  Sources tell us that he is on the verge of making a huge mistake by hiring an agent



ground_truth: By comparing candidate content items to a model and scoring them.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: How many underclassmen are on the NBA Draft Early-Entry List?

Read the following text and answer briefly.

Context 2: 
Rajon Rondo, 6-2, PG, Kentucky Sophomore Yes First round pick As expected, Rondo has decided to enter the NBA draft, and has also hired an agent, Bill Duffy  Despite an inconsistent sophomore season, most scouts weve spoken to still had him as at least the #2 point guard on their board because of his intriguing upside  Workouts will be huge for him 
Blake Schilb, 6-7, SG/SF, Loyola Chicago Junior No Undrafted Declared his intentions to enter the draft, without an agent, and is hoping for an invite to Orlando  Schlib is sorely lacking in the quickness and explosiveness departments that scouts demand from swingman prospects, but he makes up for it with his skill set to a certain extent  Regardless, sources tell us he wont be invited to Orlando, meaning he has to go back to school 
Mustafa Shakur, 6-4, PG, Arizona Junior No Second round pick? According to the Arizona Star, Shakur will likely enter his name in the draft, without an agent  Lute Olson confirmed it, saying he is not concerned about it  Shakur is hoping for an Orlando invite to show what he thinks he couldnt at Point Guard U 
Cedric Simmons, 6-9, PF/C, NC State Sophomore No First round pick? Simmons is reportedly "exploring his options," in regards to the 2006 NBA draft, but will do so without an agent  Nice size, frame, length, athleticism and defensive skills make him a very intriguing prospect 
Marcus Slaughter, 6-8, PF, San Diego State Junior Yes Second round pick? After burning his lone draft card a year early last June, despite being considered a marginal prospect, Slaughter has announced that he will be hiring agent Dan Fegan and forfeiting his remaining college eligibility  Slaughters father thinks that There was nothing else for Marcus to do at San Diego State  Many would disagree with that 
Curtis Stinson, 6-3, PG/SG, Iowa State Junior Yes Second round pick After swearing up and down last month that he has no intention on entering the draft, Stinson did just that  His coach Wayne Morgan, who he was very close to, was fired, resulting in him hiring agent Kevin Bradbury  The 23 year old combo guard will have to go to the Orlando pre-draft camp and impress if he wants to come close to being a 1st rounder 
Tyrus Thomas, 6-9, PF, LSU Freshman Yes Top 5 pick As DraftExpress exclusively reported Thomas called a press conference to announce his intentions to enter the 2006 NBA draft, as well as hire agents Brian Elfus and Mike Siegel  SEC Freshman of the year could be the most athletic player in the draft, as well as the player with the most overall upside 
PJ Tucker, 6-5, SF, Texas Junior No Second round pick As reported all year long by DraftExpress, Tucker will be entering the draft without an agent  Considering that hes a 6-5 combo forward with tremendous skills, his stock widely fluctuates depending on who is being asked  Phenomenal basketball player, but is severely lacking in 2-3 inches of height  Will likely need a strong showing at the Orlando pre-draft camp to have a legitimate shot at the 1st round  Some scouts compare him to Bonzi Wells 
Junior No Undrafted Undersized Division II post player has no chance of being drafted despite 20+8 averages 
Ian Vouyoukas, 6-10, Center, St  Louis Junior ??? Undrafted Vouyoukas declared his intentions to enter the draft, supposedly without an agent  Sources in Europe tell us he is likely to return to Greece to take a large contract offer from a first division team once he realizes he has no chance of being drafted  Vouyoukas is a nice mid-major big man who has improved somewhat in his junior season, but does not possess the necessary combination of athleticism and size required of an NBA center 
Darius Washington, 6-2, PG, Memphis Sophomore Likely First round pick? DraftExpress exclusively reported that Washington will be in the draft  It appears that hell be hiring an agent as well, despite not being anywhere near a lock for the first round 
Albert Weber, 6-3, SG, Connors State Sophomore No Undrafted Transfer from Alabama led his conference in scoring and is considered one of the top Junior College players in the country  Not officialy an NCAA player yet, and has not committed to any school yet, so really doesn't stand much to lose (or gain) from this move 
Marcus Williams, 6-3, PG, UConn Junior Yes Late Lottery-Mid-First As expected, Williams is expected to announce that hes hired Calvin Andrews of BDA Sports Management as his agent at a press conference next week  A strong junior season and outstanding NCAA tournament, establishing himself as one of the purest playmakers in the nation, means hes likely one of the first PGs taken 
Andriy Agafonov, 6-8, PF, Khimik 1986 Ukraine Undrafted Ukrainian power forward played 15 minutes and scored 6 points with 4 4 rebounds per game playing for FIBA EuroCup participants, and is declaring in hopes of getting his name out as he has one more draft card to burn after this before becoming automatically eligible 
Nemanja Aleksandrov, 7-0, SF/PF, KK Reflex 1987 Serbia & Montenegro ??? American agent has been telling us all year that hes likely to enter  Still hasnt played a game this year after taking slow recovery process from torn ACL  Once regarded as a prodigy and potential #1 overall pick, but injuries mean he hasnt played in nearly two years and is now considered damaged goods  Might just look for an attractive team to guarantee him in the 2nd round and develop him in the NBDL 
Pape-Philippe Amagou, 6-1, PG, Le Mans 1985 France ??? Amagous American agent has informed us that he will enter the NBA Draft this year, and participate in the Reebok Eurocamp in Treviso  Shares playmaking duties and spotlight with fellow early-entrant Yannick Bokolo 
Andrea Bargnani, 7-0, PF, Benetton Treviso 1985 Italy Top 5 pick Bargnani's Italian agent Stefano Meller told DraftExpress in Portsmouth that the Italian star power forward will definitely be entering the NBA draft  Bargnani is in the process of hiring an American agent and the only question is how long will it take for him to make it over to the US after Benetton finishes up in the Italian playoffs, which could last as far as mid-June  He is expected to be a top 5 pick with a shot at going #1 depending on how the lottery plays out  Considered a phenomenal talent thanks to his excellent size, perimeter skills and athleticism relative to height 
Yannick Bokolo, 6-3, PG/SG, Le Mans 1985 France ??? Terrific athlete who is still making the transition to playing the point full time 
Carlos Cedeno, 6-5, SG, Guaiqueries 1985 Venezuela Undrafted Relatively unknown Venezuelan player  Has some international experience at the junior levels 
Tadija Dragicevic, 6-8, PF, Red Star Belgrade 1986 Serbia & Montenegro Undrafted Undersized power forward barely played in the Adriatic League this past season 
Lior Eliyahu, 6-9, SF/PF, Galil Elyon 1985 Israel Second round pick? Prolific and athletic Israeli combo forward will be entering the NBA draft this year looking for certain guarantees from an NBA team in the 1st or 2nd round  Eliyahu is still in the Israeli army and will stay overseas for another year regardless of what happens  He'll be represented by the American agency Entersport in the United States  A midseason injury set him back from being the top Israeli player in the league despite his youth 
Rudy Fernández, 6-5, SG, DKV Joventut 1985 Spain First round pick? Has some minor buyout issues to deal with to make sure he can stay in the draft  Excellent season in Spain has him projected as a pretty solid first round pick  Improved outside shooting, and still the same excellent athlete, passer, defender and all-around player hes always been  Still very skinny too 
Kyrylo Fesenko, 6-11, PF, Azovmash 1986 Ukraine Second Round Pick More to come 
Rafael Hettsheimeir, 6-9, Center, Akasvayu Girona 1986 Brazil Undrafted Undersized Brazilian center did not overly impress at the Nike Hoop Summit, showing that he will likely lack mobility until he takes off some weight 
Marko Lekic, 6-11, PF, Atlas 1985 Serbia & Montenegro ??? American agent Marc Cornstein, Lekic told us hell be putting his name in the draft this year once again  Still a bit of an unknown, numbers are fairly average in the Serbian YUBA league 
Damir Markota, 6-11, SF/PF, Cibona Zagreb 1985 Croatia Second round pick American agent Marc Cornstein told us Markota will definitely be putting his name in the draft once again  He had a breakout season in the Euroleague and Adriatic league before a groin injury slowed him down and eventually forced him to have minor surgery  Likely wont be able to come to the States until very late in the process  Does not have a buyout 
Mickael Mokongo, 5-11, PG, Chalon 1986 France ??? DraftExpress was exclusively informed hell be in the draft  Considered a talented athlete, but lack of size and the fact that he missed a large chunk of the season due to injury means his draft stock is very much up in the air still 
Brad Newley, 6-6, SG, 1985 Australia Second round pick Newely has told the Australian media that hes entering the draft  Hired Philadelphia based agent Leon Rose  Scouts who saw him play in Argentina last summer like his athleticism  Desperately lacking exposure, but agent appears to be unwilling to provide him with it 
Oleksiy Pecherov, 6-11, PF, Racing Basket 1985 Ukraine Second round pick DraftExpress received indication that Pecherov will be entering his name in the draft after a nice 2nd half regular season in France  Pecherov has his draft card in hand one year before he becomes automatically eligible, meaning he has nothing to lose  Has some nice skills facing the basket, but is still very soft and underdeveloped 
Hrvoje Peric, 6-8, SF, KK Split 1985 Croatia Second round pick? Good athlete who is still coming into his own as a basketball player  Did not play in the Adriatic League this season  Definitely needs at least another year in Europe, but could use the exposure that declaring for the draft provides 
Kosta Perovic, 7-2, Center, Partizan 1985 Serbia & Montenegro Undrafted? DraftExpress has been told that Partizan needs Perovic to be drafted this year to relieve them of his 500,000$ salary next year as well as help them financially with buyout money for their budget  Unfortunately this is happening about 3 years too late as weve seen little to no improvement from Perovic over that span 
Georgios Printezis, 6-9, PF, Olympiakos 1985 Greece Undrafted Greek power forward played 9 minutes and scored 4 points per game playing for a Euroleague team, and is declaring in hopes of getting his name out before he becomes automatically eligible next year 
Milovan Rakovic, 6-10, PF, Atlas 1985 Serbia & Montenegro ??? American agent Marc Cornstein told us Rakovic will be putting his name in the draft  Still an unknown player, puts up nice numbers on occasion in the fairly weak Serbian YUBA league 
Alexandr Rindin, 7-5, Center, Gala Baku 1985 Azerbaijan Undrafted Huge body, complete unknown  5 points, 5 rebounds per game in FIBA Europe Cup 
Sergio Rodríguez, 6-3, PG, Estudiantes 1986 Spain First round pick Rodríguezs agent in the States told DraftExpress exclusively hell be in the draft, likely for good if he gets a commitment in the 1st round  A disappointing start to his season both in Spain and the ULEB cup made this European prodigy point guard fall on most teams draft boards, but Rodríguez picked things up substantially towards the end of the year and is now playing terrific basketball  Weak NCAA PG crop could put him in the lottery with good workouts 
Dusan Sakota, 6-10, SF/PF, Panathinaikos 1986 Greece Undrafted Fairly unathletic perimeter oriented big man was in the draft last year already  Plays for one of the best teams in Europe and rarely sees the floor for meaningful minutes 
Renaldas Seibutis 6-5, SG, Olympiakos 1985 Lithuania Undrafted One of the most productive players in Europe in his age group considering the level he plays at  Important cog on an excellent team, but lacks athleticism and isnt as good of a shooter as you would hope at this point in his career 
Saer Sene, 7-0, Center, Pepinster 1986? Senegal First round pick? Freakishly long and athletic African prospect who played extremely well at the Nike Hoop Summit  Many question his age and lack of productivity in the very average Belgian league A player teams will want to look at closely 
Sidiki Sidibe, 7-1, Center, Levallois 1985 France ??? 7-1, 265 pound volleyball player and former Kansas State commit will be in this years draft according to his American agent  Too raw to get any playing time whatsoever in French 2nd division 
Tiago Splitter, 7-0, PF/C, Tau Vitoria 1985 Brazil Lottery pick Splitters American agent Herb Rudoy told DraftExpress exclusively hes entering the draft Splitter is having a terrific season in both the ACB Spanish League and the Euroleague, but lack of buyout in his contract means he might not be able to stay in  CBA rules allow him to withdraw and become automatically eligible next season  Tau Vitorias president was quoted saying Splitter will be back in Spain next season 
Sun Yue, 6-9, PG/SF, Aoshen 1985 China Second round pick? Super talented tall point guard with decent athleticism and nice defensive skills  Lacks strength and outside shooting ability  Level of competition is mediocre in American semi-pro ABA league, which makes him an intriguing candidate for Orlando pre-draft camp 
Ali Traore, 6-9, PF, Roanne 1985 France ??? Puts up nice numbers in France  Will participate at the Reebok Eurocamp in Treviso 
Ejike Ugboaja, 6-8, PF, Union Bank Lagos 1985 Nigeria Undrafted Plays for Nigerian National Team 
Goran Dragic, 6-4, PG, Geoplin Slovan 1986 Agent initially notified us that Dragic will be entering the draft, but in the end decided to keep him out  His buyout was always a question mark 
Leigh Enobakhare, 6-10, Center, Oostende 1986 Agent Ugo Udezue from BDA Sports Management told us that Enobakhare will be entering the draft  In the end he must have heard that he is not considered a prospect at all, and decided to keep him out of the draft 
Cartier Martin, 6-8, SF/PF, Kansas State Junior Martin pondered entering his name in the draft, especially after the firing of Kansas State coach Jim Wooldridge 
Nick Young, 6-6, SG, USC Sophomore Young told the LA Daily News in February that hes staying at USC for another year 
D J  Strawberry, 6-5, SG/SF, Maryland Junior Strawberry initially intended to test the waters, but eventually ended up not doing so once he found out that his chances of being drafted are almost non-existent 
Al Thornton, 6-7, SF/PF, Florida State Sophomore Implied earlier on in the year that he might put his name in, but sources recently told us it appears that he will return for his senior year  Tallahassee media backs this up 
Marcus Williams (AZ), 6-8, SG/SF, Arizona Freshman After initially appearing to be gone after numerous definitive reports, Williams surprised everyone and thrilled Arizona fans by announcing in a press conference hell be returning for his sophomore year 
Josh McRoberts, 6-11, PF, Duke Freshman After being upset by LSU in the Sweet Sixteen, McRoberts was quoted saying Ill be at Duke next year   Duke issued a press release a month later confirming this 
Yi Jianlian, 7-0, PF, Guangdong 1987? International Jianlian announced in a press conference that hell be staying in China  A CBA official was also quoted on this matter, sounding as if they were the main factor for him staying put 
Acie Law, 6-3, PG, Texas A&M Junior After a fantastic showing in the NCAA tournament, Law helped his NBA draft stock considerably but will return for his senior year where A&M is expected to make a run at possibly winning the Big 12 
Joakim Noah, 6-11, PF/C, Florida Sophomore Huge 2nd half regular of the regular season and NCAA tournament boosted his stock into as high as the top 5  Noah came out and said afterwards hes staying regardless 
Al Horford, 6-9, PF, Florida Sophomore Horford indicated all season long that hes staying at least one more year, but playing extremely well in winning the national championship gave him a realistic chance at being a lottery pick  Regardless, Horford announced he'll return 
Corey Brewer, 6-8, SF, Florida Sophomore Brewer indicated all season long that hes staying at least one more year, but a terrific performance in the NCAA tournament gave him a realistic chance at being a top 20 pick  Regardless, Brewer announced he'll return 
Glen Davis, 6-8, Center, LSU Sophomore Davis announced hell be returning to LSU immediately after an absolutely horrendous showing in the Final Four which exposed all of his glaring weaknesses  Made it official as an LSU press conference alongside Tyrus Thomas 
Jason Smith, 7-0, PF/C, Colorado State Sophomore Smith announced that hes returning for his junior year, stating that "a little further down the road, it [the NBA] might be in my plans  I'm continuing to concentrate on my academics and see how I can help CSU as much as possible "
Jermareo Davidson, 6-10, PF, Alabama Junior > After burning his lone draft card a year early last June, Davidson considered entering the draft again, but eventually made the right decision in announcing hell be returning for his senior year 
Richard Hendrix, 6-8, PF, Alabama Freshman Told Alabama media after NCAA tournament loss that hell be back in Tuscaloosa next year 
Ja'Vance Coleman, 6-3, SG, Fresno State Junior Testing the waters according to the Fresno Bee  Whoops, no hes not 
Sean Singletary, 5-11, PG, Virginia Sophomore Singletary told The Daily Progress in early February that hes returning 
Context 3: Paper Info

Title: Two-stage Pipeline for Multilingual Dialect Detection
Publish Date: Unkown
Author List: Ankit Vaidya (from Pune Institute of Computer Technology), Aditya Kane (from Pune Institute of Computer Technology)

Figure

Figure 1: Class distribution of dialects
Figure 2: System diagram for dialect classification The LID classifies the input into one of 3 languages The sample is then further classified into dialects by language specific models 
Figure 3: Confusion matrix of 9-way classification Note that rows are normalized according to the number of samples is that class 
Our complete results for Track-1 using the two-stage dialect detection pipeline Model-* denotes the language of the models used for the experiments 
Performance on Track-1 validation dataset of individual models used in the two-stage pipeline "Lg" stands for language of the model used 
Comparative results of two-way classification using the finetuned (F T ) predictions and predictions adapted from three-way classification models 

abstract

Dialect Identification is a crucial task for localizing various Large Language Models  This paper outlines our approach to the VarDial 2023 DSL-TL shared task  Here we have to identify three or two dialects from three languages each which results in a 9-way classification for Track-1 and 6-way classification for Track-2 respectively 
Our proposed approach consists of a two-stage system and outperforms other participants' systems and previous works in this domain  We achieve a score of 58 54% for Track-1 and 85 61% for Track-2  Our codebase is available publicly 1  

Introduction

Language has been the primary mode of communication for humans since the pre-historic ages  Studies have explored the evolution of language and outlined mathematical models that govern the intricacies of natural language   Inevitably, as humans established civilization in various parts of the world, this language was modified by, and for the group of people occupied by that particular geographical region 
This gave rise to multiple national dialects of the same language  The VarDial workshop (colocated with EACL 2023) explores various dialects and variations of the same language  We participated in the Discriminating Between Similar Languages -True Labels (DSL-TL) shared task  In this task, the participants were provided with data from three languages, with each language having three varieties 
This shared task consisted of two tracks -Track-1 featuring nine-way classification and Track-2 featuring six-way classification  The second track included two particular national dialects of each language (eg  American English and British English), and the first track had one general  We ranked 1 st in both of the tracks 
Moreover, we beat the next best submission by a margin of 4 5% in the first task and 5 6% in the second task We were the only team to surpass the organizer baseline scores  We present our winning solution in this paper  We used an end-to-end deep learning pipeline which consisted of a language identification model and three language-specific models, one for each language 
We converged upon the best combination by doing an elaborate analysis of various models available  Furthermore, in this work we also analyze the performance of the pipeline as a whole and also provide an ablation study  Lastly, we provide some future directions in this area of research 

Related Work

The present literature encompasses various aspects of dialect identification  We study this from three perspectives: large language models, language identification and dialect classification problems 

Large Language Models

The success of transformers and BERT based models was inevitable since the initial boom of the transformer  2017) model  In recent years, many other architectures like RoBERTa and ELECTRA have further pushed the state-of-the-art in this domain  Moreover, autoregressive models like GPT and GPT-2 have also shown their prowess 
Multilingual versions of RoBERTA, namely XLM-RoBERTa are also available  Lastly, language specific models like Spanish BERT (la Rosa y Eduardo G  Ponferrada y Manu Romero y Paulo Villegas y Pablo González de Prado Salas y María Grandury, 2022) and Portuguese BERT are available as well  Our winning solution makes use of these large language models trained on specific languages 

Language Identification Models

Many multilingual language identification models have been developed in order to classify the language of the input sentence beforehand  Even though the initial works used n-gram models and generative mixture models or even conditional random fields and other classical machine learning methods like naive bayes , modern methods have shifted to the use of deep learning for language identification  
Recent works have mainly focused on deep learning based language identification, where handling codemixed data is a big challenge in the domain  For our experiments, we use a version of XLM-RoBERTa finetuned on a language identification dataset 2   This model has near-perfect test accuracy of 99 6% 

Dialect Classification

Dialect classification has been previously solved using statistical methods like Gaussian Mixture Models and Frame Selection Decoding or Support Vector Machines (SVM)   It has been explored relatively sparsely, mostly in the case for local languages   Deep learning approaches have been explored in previous editions of the VarDial workshop shared tasks and otherwise  
Dialect classification was also explored previously as a part of other shared tasks   We want to stress that given the multilingual nature of the dataset, using the present methods directly was not an option  In our work, although we take inspiration from the previous works, we propose a novel system that surpasses the performance of the previous systems by a large margin 

Data

The dataset  We observed that the class PT-BR had the most number of samples (2,724) and the class EN had the least number of samples (349), and thus the imbalance ratio was almost 1:8  We have illustrated the data distribution in Figure   We tried to mitigate this imbalance using over-sampling and weighted sampling methods 
However, the improved data sampling method did not affect the performance 

System Description

This was a problem of multi-class classification having 9 classes for Track-1 and 6 classes for Track-2  The samples were belonging to 3 languages having 3 varieties each, so the classification pipeline was made in 2 stages  The Language Identification (LID) model which is the first stage classifies the sentence into 3 languages: English (EN), Spanish (ES) and Portuguese (PT) 
The LID is a pretrained XLM-RoBERTa that is fine-tuned for the task of language identification  It is able to classify the input sentence into 20 languages  We classify and separate the samples according to their language  The samples corresponding to the specific languages are then fed into the language specific models for dialect identification 
For dialect identification we have used models like BERT and RoBERTa with a linear layer connected to the pooler output of the models  Then fine-tuning is done on the models for dialect identification   using the samples corresponding to the specific languages  For the task of dialect identification we experimented with several pretrained models like XLM-RoBERTa, BERT, ELECTRA, GPT-2 and RoBERTa 
All models were fine-tuned for 20 epochs with a learning rate of 1e-6 and weight decay 1e-6 with a batch size of 8  The best performing model checkpoint was chosen according to the epoch-wise validation macro-F1 score  5 Experiments and Results

Experiments using Large Language Models

For the task of Dialect Identification we have tried various language specific models like XLM-RoBERTa, BERT, ELECTRA, RoBERTa and GPT- 2  The base variant of all these models were used and all the models were used through the Hugging-Face library  The pooler output of these models was passed through a linear layer and the models were fine-tuned 
First, we experimented with different models for Track-1  All the models were trained for 20 epochs with learning rate 1e-6, weight decay 1e-6 and a batch size of 8  We used XLM-RoBERTa as the baseline for all 3 languages  The best performing models for the English language were RoBERTa and BERT whereas GPT-2 was the worst performing 
Similarly the language specific versions of RoBERTa and BERT performed well for the Spanish and Portuguese respectively  Overall the worst performing model was GPT-2 across all 3 languages  The validation F1 scores are present in Table   The two best-performing models for every language were chosen for Track-2 
The same procedure as specified above was used and the F1 scores are present in Table   The train and validation F1 scores for 2-class classification are higher for all models as compared to the F1 score of the same models for 3-class classification  This was mainly due to the poor representation and accuracy of classification of the third class 
We observed symptoms of overfitting in all models after 12-15 epochs and the best validation F1 score was obtained in the range of 4-8 epochs 

LID experiments

The pipeline for dialect identification is divided into two parts as the sentences in the dataset belong to different languages  The stages are described in Section 4  The XLM-RoBERTa we have used for language classification has a test accuracy of 99 6% meaning it correctly classifies all input sentences and hence, can be considered as a perfect classifier 
For the final pipeline we experimented using the two best performing models for each language in Track-1 and Track-2  For both the tracks we experimented with all 8 (2 3 ) possible combinations of models and calculated the validation F1 score for the combined validation dataset which had sentences belonging to all languages 
The validation scores for Track-1 and Track-2 are shown in Table and Table respectively  For both the tracks, the three pipelines with the best validation F1 scores were chosen for submission 

Using 3-way classifier as a 2-way classifier

In Track-1, participants are expected to train a classifier which classifies amongst 9 classes, and in Track-2, participants are expected to train a classifier which classifies amongst 6 classes  These 6 classes are a proper subset of the 9 classes from Track-1  Thus, an intuitive baseline for Track-2 is to use the model finetuned for Track-1, whilst considering only the relevant classes for the latter task 
The classes EN , ES and P T , i e  the classes without any national dialect associated with them are not included in Track-2 as compared to Track-1  Thus, we calculate the predictions for the Track-2 validation dataset using the models for Track-1 and exclude the metrics for Track-1 specific classes to get the metrics for this "adapted" 2-way classification 
We show the results of this experiment in Table and observe that, as expected, the adapted 2-way classification performs worse compared to the explicitly finetuned variant 

Results for Track-1 and Track-2

We now present our experiments and their performance for both tracks  Our experiments for Track-1 are described in Table and our experiments for Track-2 are described in Table   The participants were allowed three submissions for evaluation on the test set, so we submitted predictions using the three systems which performed the best on the validation set 
As mentioned in Section 5 2, we performed 2 3 , i e  a total of 8 experiments using the two best models for each language  We observed that RoBERTa base on English, Spanish BERT base on Spanish and Portuguese BERT base performed the best on the testing set for Track-1  The same combination, with RoBERTa base for English, worked best for Track-2 
All of our submissions were the top submissions for each track, which surpassed the next best competitors by a margin of 4 5% and 5 6% for Track-1 and Track-2 respectively 

Ablation of best submissions

We hereby make some observations of our submissions and other experiments  To assist this, we plot the confusion matrices of our best submissions for Track-1 and Track-2 in Figures respectively  Note that these confusion matrices have their rows (i e  true labels axes) normalized according to the number of samples in the class 
Here are observations from our experiments: 1  BERT-based models outperform other models across all languages: We observe that BERT-based models outperform ELECTRA-based and GPT-2-based models, as shown in Table   We speculate this is because of the inherent architecture of BERT, which combines semantic learning with knowledge retention 
This combination of traits is particularly useful for this task  2  Common labels perform the worst across all languages: We observe that the common labels EN , ES and P T perform the worst, both in the individual as well as the two-stage setup  We hypothesize this is because of the absence of dialect specific words, or words that are specific to the geographical origin of the national dialect (for example, "Yankees" for EN-US and "Oxford" for EN-GB) 
3  English models work better than models of other languages: It can be noted from Figures 4 and 3 that the English models have the best performance across all classes  This can be attributed to two reasons: absence of national dialect specific words and lesser pretraining data in the case of Portuguese 
4  British English is most correctly classified class: We can observe that the Spanish or Portuguese models make equal number of mistakes in the case of either national dialect, in the case of Track-2 (see Figure )  However, in the case of English, the label EN − GB is correctly classified for more than 95% of the cases 
We speculate this is because British English involves slightly distinctive grammar and semantics, which help the model separate it from other classes  5  The proposed 2-step method is scalable for multiple language dialect classification: We can strongly assert that the novel 2-step deep learning method for multilingual dialect classification is a scalable method for the task due to two specific reasons: firstly, the multilingual models (like XLM-RoBERTa) might not have the vocabulary as well as the learning capabilities to learn the minute differences between individual dialects 
Secondly, this system can be quickly expanded for a new language by simply adding a language specific dialect classifier, provided the language identification model supports that particular language 

Conclusion

In this paper we propose a two-stage classification pipeline for dialect identification for multilingual corpora  We conduct thorough ablations on this setup and provide valuable insights  We foresee multiple future directions for this work  The first is to expand this work to many languages and dialects 
Secondly, it is a worthwhile research direction to distill this multi-model setup into a single model with multiple prediction heads  The obvious limitation of this system is the excessive memory consumption due to the usage of language specific models  For low resource languages this system is difficult to train and scale 
We hope that these problems will be addressed by researchers in future works 



ground_truth: 62.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What models were used for dialect identification?

Read the following text and answer briefly.

Context 4:  The loss function was optimized using the Adam algorithm   As we consider a linear ODE we can analytically compute the eigenvalues involved and compare it with the parameters λ identified by our algorithm 
We observe in Figure that the algorithm was able to recover the correct values, i e  the eigenvalues 7 and 3 of the given linear ODE  The system does not have a periodic component and the two imaginary parts correctly go to zero, whereas the real parts converge to the reference value  Moreover we are also able to identify for the linear mapping between our latent variables z and the training data a matrix consisting of a multiple of the eigenvectors (1,1) and (1,-1) and thus the correct solution 
This example was chosen to show that the algorithm is able to quickly identify the exact solution of a linear ODE in terms of its linearly independent components 

Hidden multiscale dynamics

We consider eight-dimensional synthetic time series data produced by an underlying twodimensional complex valued process  In particular, the data points x are generated by first solving for the temporal evolution for the two complex-valued processes p 1 and p 2 and than mapping to the eight-dimensional space by using a randomly sampled linear mapping W  
One of the two processes used to generate the data is chosen to be much slower than the other one and both processes have a periodic component  dp 2 dt = (−0 9 + 1 5i)p 2 (8) As training data we consider 40 time series with 150 data points each, obtained by simulating the described processes for a maximum of t = 15 s and then sampling from the obtained data points 
Hence the training data consists of: • 40 time-series • with each consisting 150 observations of the x at a uniform time-step ∆t = 0 0025 The autoencoder obtained consists of one linear layer for both the decoder as well as the encoder  The model is trained for 5000 iterations using the Adam optimizer and a learning rate of 10 −3  
The results for the convergence of the parameters λ 1 and λ 2 can be found in Figure   We note that the process which is slower decaying and thus more responsible for the long-term evolution of the system has a higher convergence rate than the faster process  With the obtained parameters λ as well as the trained autoencoder, we compute predictions based on the last time step used for training, i e  we apply the encoder to obtain our latent representation and than use the latent dynamics to advance the latent representation in time 
Afterwards, we employ the decoder to reconstruct the full high-dimensional system  The results can be found in Figure and show very good agreement between predictions and reference data  This example shows that our model is successfully able to carry out dimensionality reduction and moreover indicates that the convergence rate between latent processes can be different 
The latter is relevant when training models as for accurate predictions all latent processes and their dynamics should be converged 

Kuramoto-Sivashinsky

Finally, we applied our algorithm to the KS equation and aim to identify a reduced-order model for the solution u(y, t): We employed periodic boundary conditions, µ = 1 and a domain size y ∈ [0, 22]  For this domain-size, the KS-equation exhibits a structurally stable chaotic attractor as discussed in The black lines divides the area for which training data was given from the area without raining data 
;   The equation is discretized in space using a discretization step of 22 64 resulting in a state vector x of dimension 64 and a nonlinear system of coupled ODEs  This is solved using a stiff fourth-order solver  We employed a non-linear encoder and decoder with four fully-connected layers each and ReLU-activation functions as well as Dropout Layers between the fully-connected layers 
We trained the model for 200000 iterations using Adam and a learning rate of 5 • 10 4 and assuming a five-dimensional latent space  We obtained the λ's in Figure   Four latent variables have λ's close to zero and thus a slow temporal dynamic that is responsible for the long-term evolution whereas one latent variable is quickly decaying 
Based on the obtained parameters, we do predictions based on an unseen initial condition not contained in the training data  We are able to reconstruct the correct phase space based on our predictions despite only using a very limited amount of training data  The results for the phase space can be seen in Figure  
Although the small-scale fluctuations in the temporal dynamics are not well captured, the model identifies the correct manifold which has a good accuracy compared to the reference solution  All phase-spaces were obtained by using a finite-difference operator on the data or predictions  These results are in accordance Interpretable reduced-order modeling with time-scale separation with whose LSTM-based temporal dynamic model was also able to find the correct phase space but not to track the actual dynamics for long-term predictions 
Our model is not able to account for noise in the temporal evolution and thus dealing with chaotic, small-scale fluctuations is challenging  We believe that a probabilistic version of our algorithm could be advantageous here  This section contains a fully probabilistic formulation for the deterministic model discussed before 
We replace the Autoencoder with a Variational Autoencoder and the ODE in the latent space with a SDE  The loss function which we optimize is the Evidence-Lower Bound (ELBO) 

Model Structure

We postulate the following relations for our probabilistic model using an Ornstein-Uhlenbeck (OU) for each dimension i of the latent space and a Wiener process W t in the latent space: We again assume that the latent variables z t are complex-valued and a priori independent  Complex variables were chosen as their evolution includes a harmonic components which are observed in many physical systems 
We assume an initial conditions z 0,i ∼ CN (0, σ 2 0,i )  The total parameters associated with the latent space dynamics of our model are thus {σ 2 0,i , σ 2 i , λ i } c i=1 and will be denoted by θ together with all parameters responsible for the decoder mapping G (see next section)  These parameters along with the state variables z t have to be inferred from the data x t  
Based on probabilistic Slow Feature Analysis (SFA) , we set σ 2 i = 2; (λ j ) and σ 2 0,i = 1  As a consequence, a priori, the latent dynamics are stationary  A derivation and reasoning for this choice can be found in Appendix A  Hence the only independent parameters are the λ i , the imaginary part of which can account for periodic effects in the latent dynamics 

Variational Autoencoder

We employ a variational autoencoder to account for a probabilistic mappings from the lower-dimensional representation z n to the high-dimensional system x n   In particular we are employing a probabilistic decoder The encoder is used to infer the state variables z based on the given data and thus defined in the inference and learning section 

Inference and Learning

Given the probabilistic relations , our goal is to infer the latent variables z 0:T as well as all model parameters θ  We follow a hybrid Bayesian approach in which the posterior of the state variables is approximated using amortized Variational Inference and Maximum-A-Posteriori (MAP) point-estimates for θ are computed 
The application of Bayes' rule for each data sequence x 0:T leads to the following posterior: where p(θ) denotes the prior on the model parameters  In the context of variational inference, we use the following factorization of the approximate posterior i e  we infer only the mean µ and variance σ for each state variable based on the given data points 
This conditional density used for inference is the encoder-counterpart to the probabilistic decoder defined in the section before  It can be readily shown that the optimal parameter values are found by maximizing the Evidence Lower Bound (ELBO) F(q φ (z 0:T ), θ) which is derived in Appendix B  We compute Monte Carlo estimates of the gradient of the ELBO with respect to φ and θ with the help of the reparametrization trick and carry out stochastic optimization with the ADAM algorithm  

Results for the probabilistic extension

We applied our probabilistic version to the KS-equation  We used the same settings as for the deterministic approach but considered up to 10 complex latent variables  The obtained λ's are in Figure   The probabilistic model allows us to quantify the uncertainty in predictions  In Figure predictions for various time-steps and the respective uncertainty bounds are shown for an unseen initial condition 
Due to the chaotic nature of the KS-equation and the small amount of training data, the underlying linear dynamic of our model is only able to capture the full dynamics for a limited time horizon  Fortunately, due to the probabilistic approach the model is capable of capturing chaotic fluctuations with increasingly wide uncertainty bounds 
We also computed the phase space representation for the KS-equation based on the predictions obtained by our model and compare it with the reference solution  The probabilistic model identifies the correct manifold with a better accuracy than the deterministic model  As some of the small-scale fluctuations are accounted as noise, the resulting manifold is more concentrated at the origin and the obtained values are slightly smaller than the reference manifold although their shape is very similar 



ground_truth: BERT, RoBERTa, ELECTRA, GPT-2, and XLM-RoBERTa.