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 



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: What are the symptoms of vitamin K deficiency?

Read the following text and answer briefly.

Context 2: Vitamin K - Wikipedia
(Redirected from Vitamin k)
This article needs more medical references for verification or relies too heavily on primary sources  Please review the contents of the article and add the appropriate references if you can  Unsourced or poorly sourced material may be challenged and removed  (November 2015)
This article is about the family of vitamers  For vitamin K1 the form usually used as a supplement, see Phytomenadione 
Vitamin K structures  MK-4 and MK-7 are both subtypes of K2 
Vitamin K deficiency, Warfarin overdose
Vitamin K is a group of structurally similar, fat-soluble vitamins the human body requires for complete synthesis of certain proteins that are prerequisites for blood coagulation and which the body also needs for controlling binding of calcium in bones and other tissues  The vitamin K-related modification of the proteins allows them to bind calcium ions, which they cannot do otherwise  Without vitamin K, blood coagulation is seriously impaired, and uncontrolled bleeding occurs  Low levels of vitamin K also weaken bones and promote calcification of arteries and other soft tissues[citation needed] 
Chemically, the vitamin K family comprises 2-methyl-1,4-naphthoquinone (3-) derivatives  Vitamin K includes two natural vitamers: vitamin K1 and vitamin K2 [1] Vitamin K2, in turn, consists of a number of related chemical subtypes, with differing lengths of carbon side chains made of isoprenoid groups of atoms 
Vitamin K1, also known as phylloquinone, is made by plants, and is found in highest amounts in green leafy vegetables because it is directly involved in photosynthesis  It may be thought of as the plant form of vitamin K  It is active as a vitamin in animals and performs the classic functions of vitamin K, including its activity in the production of blood-clotting proteins  Animals may also convert it to vitamin K2 
Bacteria in the gut flora can also convert K1 into vitamin K2  In addition, bacteria typically lengthen the isoprenoid side chain of vitamin K2 to produce a range of vitamin K2 forms, most notably the MK-7 to MK-11 homologues of vitamin K2  All forms of K2 other than MK-4 can only be produced by bacteria, which use these forms in anaerobic respiration  The MK-7 and other bacterially derived forms of vitamin K2 exhibit vitamin K activity in animals, but MK-7's extra utility over MK-4, if any, is unclear and is a matter of investigation 
Three synthetic types of vitamin K are known: vitamins K3, K4, and K5  Although the natural K1 and all K2 homologues and synthetic K4 and K5 have proven nontoxic, the synthetic form K3 (menadione) has shown toxicity [2]
1 2 Cardiovascular health
1 4 Coumarin poisoning
4 1 Conversion of vitamin K1 to vitamin K2
4 2 Vitamin K2
6 Absorption and dietary need
7 Dietary reference intake
10 Biochemistry
10 1 Function in animals
10 2 Gamma-carboxyglutamate proteins
10 3 Methods of assessment
10 4 Function in bacteria
11 Injection in newborns
11 3 Controversy
A review of 2014 concluded that there is positive evidence that monotherapy using MK-4, one of the forms of Vitamin K2, reduces fracture incidence in post-menopausal women with osteoporosis, and suggested further research on the combined use of MK-4 with bisphosphonates  In contrast, an earlier review article of 2013 concluded that there is no good evidence that vitamin K supplementation helps prevent osteoporosis or fractures in postmenopausal women [3]
A Cochrane systematic review of 2006 suggested that supplementation with Vitamin K1 and with MK4 reduces bone loss; in particular, a strong effect of MK-4 on incident fractures among Japanese patients was emphasized [4]
A review article of 2016 suggested to consider, as one of several measures for bone health, increasing the intake of foods rich in vitamins K1 and K2 [5]
Cardiovascular health[edit]
Adequate intake of vitamin K is associated with the inhibition of arterial calcification and stiffening,[6] but there have been few interventional studies and no good evidence that vitamin K supplementation is of any benefit in the primary prevention of cardiovascular disease [7]
One 10-year population study, the Rotterdam Study, did show a clear and significant inverse relationship between the highest intake levels of menaquinone (mainly MK-4 from eggs and meat, and MK-8 and MK-9 from cheese) and cardiovascular disease and all-cause mortality in older men and women [8]
Vitamin K has been promoted in supplement form with claims it can slow tumor growth; there is however no good medical evidence that supports such claims [9]
Coumarin poisoning[edit]
Vitamin K is part of the suggested treatment regime for poisoning by rodenticide (coumarin poisoning) [10]
Although allergic reaction from supplementation is possible, no known toxicity is associated with high doses of the phylloquinone (vitamin K1) or menaquinone (vitamin K2) forms of vitamin K, so no tolerable upper intake level (UL) has been set [11]
Blood clotting (coagulation) studies in humans using 45 mg per day of vitamin K2 (as MK-4)[12] and even up to 135 mg per day (45 mg three times daily) of K2 (as MK-4),[13] showed no increase in blood clot risk  Even doses in rats as high as 250 mg/kg, body weight did not alter the tendency for blood-clot formation to occur [14]
Unlike the safe natural forms of vitamin K1 and vitamin K2 and their various isomers, a synthetic form of vitamin K, vitamin K3 (menadione), is demonstrably toxic at high levels  The U S  FDA has banned this form from over-the-counter sale in the United States because large doses have been shown to cause allergic reactions, hemolytic anemia, and cytotoxicity in liver cells [2]
Phylloquinone (K1)[15][16] or menaquinone (K2) are capable of reversing the anticoagulant activity of the anticoagulant warfarin (tradename Coumadin)  Warfarin works by blocking recycling of vitamin K, so that the body and tissues have lower levels of active vitamin K, and thus a deficiency of vitamin K 
Supplemental vitamin K (for which oral dosing is often more active than injectable dosing in human adults) reverses the vitamin K deficiency caused by warfarin, and therefore reduces the intended anticoagulant action of warfarin and related drugs [17] Sometimes small amounts of vitamin K are given orally to patients taking warfarin so that the action of the drug is more predictable [17] The proper anticoagulant action of the drug is a function of vitamin K intake and drug dose, and due to differing absorption must be individualized for each patient [citation needed] The action of warfarin and vitamin K both require two to five days after dosing to have maximum effect, and neither warfarin or vitamin K shows much effect in the first 24 hours after they are given [18]
The newer anticoagulants dabigatran and rivaroxaban have different mechanisms of action that do not interact with vitamin K, and may be taken with supplemental vitamin K [19][20]
Vitamin K2 (menaquinone)  In menaquinone, the side chain is composed of a varying number of isoprenoid residues  The most common number of these residues is four, since animal enzymes normally produce menaquinone-4 from plant phylloquinone 
A sample of phytomenadione for injection, also called phylloquinone
The three synthetic forms of vitamin K are vitamins K3 (menadione), K4, and K5, which are used in many areas, including the pet food industry (vitamin K3) and to inhibit fungal growth (vitamin K5) [21]
Conversion of vitamin K1 to vitamin K2[edit]
Vitamin K1 (phylloquinone) – both forms of the vitamin contain a functional naphthoquinone ring and an aliphatic side chain  Phylloquinone has a phytyl side chain 
The MK-4 form of vitamin K2 is produced by conversion of vitamin K1 in the testes, pancreas, and arterial walls [22] While major questions still surround the biochemical pathway for this transformation, the conversion is not dependent on gut bacteria, as it occurs in germ-free rats[23][24] and in parenterally-administered K1 in rats [25][26] In fact, tissues that accumulate high amounts of MK-4 have a remarkable capacity to convert up to 90% of the available K1 into MK-4 [27][28] There is evidence that the conversion proceeds by removal of the phytyl tail of K1 to produce menadione as an intermediate, which is then condensed with an activated geranylgeranyl moiety (see also prenylation) to produce vitamin K2 in the MK-4 (menatetrione) form [29]
Vitamin K2[edit]
Main article: Vitamin K2
Vitamin K2 (menaquinone) includes several subtypes  The two subtypes most studied are menaquinone-4 (menatetrenone, MK-4) and menaquinone-7 (MK-7) 
Vitamin K1, the precursor of most vitamin K in nature, is a stereoisomer of phylloquinone, an important chemical in green plants, where it functions as an electron acceptor in photosystem I during photosynthesis  For this reason, vitamin K1 is found in large quantities in the photosynthetic tissues of plants (green leaves, and dark green leafy vegetables such as romaine lettuce, kale and spinach), but it occurs in far smaller quantities in other plant tissues (roots, fruits, etc )  Iceberg lettuce contains relatively little  The function of phylloquinone in plants appears to have no resemblance to its later metabolic and biochemical function (as "vitamin K") in animals, where it performs a completely different biochemical reaction 
Vitamin K (in animals) is involved in the carboxylation of certain glutamate residues in proteins to form gamma-carboxyglutamate (Gla) residues  The modified residues are often (but not always) situated within specific protein domains called Gla domains  Gla residues are usually involved in binding calcium, and are essential for the biological activity of all known Gla proteins [30]
At this time[update], 17 human proteins with Gla domains have been discovered, and they play key roles in the regulation of three physiological processes:
Blood coagulation: prothrombin (factor II), factors VII, IX, and X, and proteins C, S, and Z[31]
Bone metabolism: osteocalcin, also called bone Gla protein (BGP), matrix Gla protein (MGP),[32] periostin,[33] and the recently discovered Gla-rich protein (GRP) [34][35]
Vascular biology: growth arrest-specific protein 6 (Gas6)[36]
Unknown function: proline-rich γ-carboxyglutamyl proteins (PRGPs) 1 and 2, and transmembrane γ-carboxy glutamyl proteins (TMGs) 3 and 4 [37]
Like other lipid-soluble vitamins (A, D and E), vitamin K is stored in the fatty tissue of the human body 
Absorption and dietary need[edit]
Previous theory held that dietary deficiency is extremely rare unless the small intestine was heavily damaged, resulting in malabsorption of the molecule  Another at-risk group for deficiency were those subject to decreased production of K2 by normal intestinal microbiota, as seen in broad spectrum antibiotic use [38] Taking broad-spectrum antibiotics can reduce vitamin K production in the gut by nearly 74% in people compared with those not taking these antibiotics [39] Diets low in vitamin K also decrease the body's vitamin K concentration [40] Those with chronic kidney disease are at risk for vitamin K deficiency, as well as vitamin D deficiency, and particularly those with the apoE4 genotype [41] Additionally, in the elderly there is a reduction in vitamin K2 production [42]
The National Academy of Medicine (NAM) updated an estimate of what constitutes an adequate intake (AI) for vitamin K in 2001  The NAM does not distinguish between K1 and K2 – both are counted as vitamin K  At that time there was not sufficient evidence to set the more rigorous estimated average requirement (EAR) or recommended dietary allowance (RDA) given for most of the essential vitamins and minerals  The current daily AIs for vitamin K for adult women and men are 90 μg and 120 μg respectively  The AI for pregnancy and lactation is 90 μg  For infants up to 12 months the AI is 2–2 5 μg, and for children aged 1 to 18 years the AI increases with age from 30 to 75 μg  As for safety, the FNB also sets tolerable upper intake levels (known as ULs) for vitamins and minerals when evidence is sufficient  In the case of vitamin K no UL is set, as evidence for adverse effects is not sufficient  Collectively EARs, RDAs, AIs and ULs are referred to as dietary reference intakes [43] The European Food Safety Authority reviewed the same safety question and did not set an UL [44]
For U S  food and dietary supplement labeling purposes, the amount in a serving is expressed as a percentage of daily value (%DV)  For vitamin K labeling purposes the daily value was 80 μg, but as of May 2016 it has been revised upwards to 120 μg  A table of the pre-change adult daily values is provided at reference daily intake  Food and supplement companies have until 28 July 2018 to comply with the change 
See also: Vitamin K2 § Dietary sources
K1 (μg)[45]
Kale, cooked
Collards, cooked
Collards, raw
Swiss chard, cooked
Swiss chard, raw
Turnip greens, raw
Romaine lettuce, raw
Table from "Important information to know when you are taking: Warfarin (Coumadin) and Vitamin K", Clinical Center, National Institutes of Health Drug Nutrient Interaction Task Force [46]
Vitamin K1 is found chiefly in leafy green vegetables such as dandelion greens (which contain 778 4 μg per 100 g, or 741% of the recommended daily amount), spinach, swiss chard, lettuce and Brassica vegetables (such as cabbage, kale, cauliflower, broccoli, and brussels sprouts) and often the absorption is greater when accompanied by fats such as butter or oils; some fruits, such as avocados, kiwifruit and grapes, are also high in vitamin K  By way of reference, two tablespoons of parsley contains 153% of the recommended daily amount of vitamin K [47] Some vegetable oils, notably soybean oil, contain vitamin K, but at levels that would require relatively large calorie consumption to meet the USDA-recommended levels [48] colonic bacteria synthesize a significant portion of humans' vitamin K needs; newborns often receive a vitamin K shot at birth to tide them over until their colons become colonized at five to seven days of age from the consumption of breast milk 
The tight binding of vitamin K1 to thylakoid membranes in chloroplasts makes it less bioavailable  For example, cooked spinach has a 5% bioavailability of phylloquinone, however, fat added to it increases bioavailability to 13% due to the increased solubility of vitamin K in fat [49]
Main article: Vitamin K deficiency
Average diets are usually not lacking in vitamin K, and primary deficiency is rare in healthy adults  Newborn infants are at an increased risk of deficiency  Other populations with an increased prevalence of vitamin K deficiency include those who suffer from liver damage or disease (e g  alcoholics), cystic fibrosis, or inflammatory bowel diseases, or have recently had abdominal surgeries  Secondary vitamin K deficiency can occur in people with bulimia, those on stringent diets, and those taking anticoagulants  Other drugs associated with vitamin K deficiency include salicylates, barbiturates, and cefamandole, although the mechanisms are still unknown  Vitamin K1 deficiency can result in coagulopathy, a bleeding disorder [50]Symptoms of K1 deficiency include anemia, bruising, nosebleeds and bleeding of the gums in both sexes, and heavy menstrual bleeding in women 
Osteoporosis[51][52] and coronary heart disease[53][54] are strongly associated with lower levels of K2 (menaquinone)  Vitamin K2 (as menaquinones MK-4 through MK-10) intake level is inversely related to severe aortic calcification and all-cause mortality [8]
Function in animals[edit]
Mechanism of action of vitamin K1 
The function of vitamin K2 in the animal cell is to add a carboxylic acid functional group to a glutamate (Glu) amino acid residue in a protein, to form a gamma-carboxyglutamate (Gla) residue  This is a somewhat uncommon posttranslational modification of the protein, which is then known as a "Gla protein"  The presence of two −COOH (carboxylic acid) groups on the same carbon in the gamma-carboxyglutamate residue allows it to chelate calcium ions  The binding of calcium ions in this way very often triggers the function or binding of Gla-protein enzymes, such as the so-called vitamin K-dependent clotting factors discussed below 
Within the cell, vitamin K undergoes electron reduction to a reduced form called vitamin K hydroquinone, catalyzed by the enzyme vitamin K epoxide reductase (VKOR) [55] Another enzyme then oxidizes vitamin K hydroquinone to allow carboxylation of Glu to Gla; this enzyme is called gamma-glutamyl carboxylase[56][57] or the vitamin K-dependent carboxylase  The carboxylation reaction only proceeds if the carboxylase enzyme is able to oxidize vitamin K hydroquinone to vitamin K epoxide at the same time  The carboxylation and epoxidation reactions are said to be coupled  Vitamin K epoxide is then reconverted to vitamin K by VKOR  The reduction and subsequent reoxidation of vitamin K coupled with carboxylation of Glu is called the vitamin K cycle [58] Humans are rarely deficient in vitamin K1 because, in part, vitamin K1 is continuously recycled in cells [59]
Warfarin and other 4-hydroxycoumarins block the action of VKOR [60] This results in decreased concentrations of vitamin K and vitamin K hydroquinone in tissues, such that the carboxylation reaction catalyzed by the glutamyl carboxylase is inefficient  This results in the production of clotting factors with inadequate Gla  Without Gla on the amino termini of these factors, they no longer bind stably to the blood vessel endothelium and cannot activate clotting to allow formation of a clot during tissue injury  As it is impossible to predict what dose of warfarin will give the desired degree of clotting suppression, warfarin treatment must be carefully monitored to avoid overdose 
Gamma-carboxyglutamate proteins[edit]
Main article: Gla domain
The following human Gla-containing proteins ("Gla proteins") have been characterized to the level of primary structure: blood coagulation factors II (prothrombin), VII, IX, and X, anticoagulant proteins C and S, and the factor X-targeting protein Z  The bone Gla protein osteocalcin, the calcification-inhibiting matrix Gla protein (MGP), the cell growth regulating growth arrest specific gene 6 protein (Gas6), and the four transmembrane Gla proteins (TMGPs), the function of which is at present unknown  Gas6 can function as a growth factor to activate the Axl receptor tyrosine kinase and stimulate cell proliferation or prevent apoptosis in some cells  In all cases in which their function was known, the presence of the Gla residues in these proteins turned out to be essential for functional activity 
Gla proteins are known to occur in a wide variety of vertebrates: mammals, birds, reptiles, and fish  The venom of a number of Australian snakes acts by activating the human blood-clotting system  In some cases, activation is accomplished by snake Gla-containing enzymes that bind to the endothelium of human blood vessels and catalyze the conversion of procoagulant clotting factors into activated ones, leading to unwanted and potentially deadly clotting 
Another interesting class of invertebrate Gla-containing proteins is synthesized by the fish-hunting snail Conus geographus [61] These snails produce a venom containing hundreds of neuroactive peptides, or conotoxins, which is sufficiently toxic to kill an adult human  Several of the conotoxins contain two to five Gla residues [62]
Methods of assessment[edit]
Vitamin K status can be assessed by:
The prothrombin time (PT) test measures the time required for blood to clot  A blood sample is mixed with citric acid and put in a fibrometer; delayed clot formation indicates a deficiency  This test is insensitive to mild deficiency, as the values do not change until the concentration of prothrombin in the blood has declined by at least 50% [63]
Undercarboxylated prothrombin (PIVKA-II); in a study of 53 newborns, found "PT (prothrombin time) is a less sensitive marker than PIVKA II",[64] and as indicated above, PT is unable to detect subclinical deficiencies that can be detected with PIVKA-II testing 
Plasma phylloquinone was found to be positively correlated with phylloquinone intake in elderly British women, but not men,[65] but an article by Schurgers et al  reported no correlation between FFQ[further explanation needed] and plasma phylloquinone [66]
Urinary γ-carboxyglutamic acid responds to changes in dietary vitamin K intake  Several days are required before any change can be observed  In a study by Booth et al , increases of phylloquinone intakes from 100 μg to between 377 and 417 μg for five days did not induce a significant change  Response may be age-specific [67]
Undercarboxylated osteocalcin (UcOc) levels have been inversely correlated with stores of vitamin K[68] and bone strength in developing rat tibiae  Another study following 78 post-menopausal Korean women found a supplement regimen of vitamins K and D, and calcium, but not a regimen of vitamin D and calcium, was inversely correlated with reduced UcOc levels [69]
Function in bacteria[edit]
Many bacteria, such as Escherichia coli found in the large intestine, can synthesize vitamin K2 (menaquinone-7 or MK-7, up to MK-11),[70] but not vitamin K1 (phylloquinone)  In these bacteria, menaquinone transfers two electrons between two different small molecules, during oxygen-independent metabolic energy production processes (anaerobic respiration) [71] For example, a small molecule with an excess of electrons (also called an electron donor) such as lactate, formate, or NADH, with the help of an enzyme, passes two electrons to menaquinone  The menaquinone, with the help of another enzyme, then transfers these two electrons to a suitable oxidant, such fumarate or nitrate (also called an electron acceptor)  Adding two electrons to fumarate or nitrate converts the molecule to succinate or nitrite plus water, respectively 
Some of these reactions generate a cellular energy source, ATP, in a manner similar to eukaryotic cell aerobic respiration, except the final electron acceptor is not molecular oxygen, but fumarate or nitrate  In aerobic respiration, the final oxidant is molecular oxygen (O2), which accepts four electrons from an electron donor such as NADH to be converted to water  E  coli, as facultative anaerobes, can carry out both aerobic respiration and menaquinone-mediated anaerobic respiration 
Injection in newborns[edit]
The blood clotting factors of newborn babies are roughly 30–60% that of adult values; this may be due to the reduced synthesis of precursor proteins and the sterility of their guts  Human milk contains 1–4 μg/L of vitamin K1, while formula-derived milk can contain up to 100 μg/L in supplemented formulas  Vitamin K2 concentrations in human milk appear to be much lower than those of vitamin K1  Occurrence of vitamin K deficiency bleeding in the first week of the infant's life is estimated at 0 25–1 7%, with a prevalence of 2–10 cases per 100,000 births [72] Premature babies have even lower levels of the vitamin, so they are at a higher risk from this deficiency 
Bleeding in infants due to vitamin K deficiency can be severe, leading to hospitalization, blood transfusions, brain damage, and death  Supplementation can prevent most cases of vitamin K deficiency bleeding in the newborn  Intramuscular administration is more effective in preventing late vitamin K deficiency bleeding than oral administration [73][74]
As a result of the occurrences of vitamin K deficiency bleeding, the Committee on Nutrition of the American Academy of Pediatrics has recommended 0 5–1 mg of vitamin K1 be administered to all newborns shortly after birth [74]
In the UK vitamin K supplementation is recommended for all newborns within the first 24 hours [75] This is usually given as a single intramuscular injection of 1 mg shortly after birth but as a second-line option can be given by three oral doses over the first month [76]
Controversy arose in the early 1990s regarding this practice, when two studies suggested a relationship between parenteral administration of vitamin K and childhood cancer,[77] however, poor methods and small sample sizes led to the discrediting of these studies, and a review of the evidence published in 2000 by Ross and Davies found no link between the two [78] Doctors reported emerging concerns in 2013,[79] after treating children for serious bleeding problems  They cited lack-of newborn vitamin K administration, as the reason that the problems occurred, and recommended that breastfed babies could have an increased risk unless they receive a preventative dose 
In the early 1930s, Danish scientist Henrik Dam investigated the role of cholesterol by feeding chickens a cholesterol-depleted diet [80] He initially replicated experiments reported by scientists at the Ontario Agricultural College (OAC) [81] McFarlane, Graham and Richardson, working on the chick feed program at OAC, had used chloroform to remove all fat from chick chow  They noticed that chicks fed only fat-depleted chow developed hemorrhages and started bleeding from tag sites [82] Dam found that these defects could not be restored by adding purified cholesterol to the diet  It appeared that – together with the cholesterol – a second compound had been extracted from the food, and this compound was called the coagulation vitamin  The new vitamin received the letter K because the initial discoveries were reported in a German journal, in which it was designated as Koagulationsvitamin  Edward Adelbert Doisy of Saint Louis University did much of the research that led to the discovery of the structure and chemical nature of vitamin K [83] Dam and Doisy shared the 1943 Nobel Prize for medicine for their work on vitamin K (K1 and K2) published in 1939  Several laboratories synthesized the compound(s) in 1939 [84]
For several decades, the vitamin K-deficient chick model was the only method of quantifying vitamin K in various foods: the chicks were made vitamin K-deficient and subsequently fed with known amounts of vitamin K-containing food  The extent to which blood coagulation was restored by the diet was taken as a measure for its vitamin K content  Three groups of physicians independently found this: Biochemical Institute, University of Copenhagen (Dam and Johannes Glavind), University of Iowa Department of Pathology (Emory Warner, Kenneth Brinkhous, and Harry Pratt Smith), and the Mayo Clinic (Hugh Butt, Albert Snell, and Arnold Osterberg) [85]
The first published report of successful treatment with vitamin K of life-threatening hemorrhage in a jaundiced patient with prothrombin deficiency was made in 1938 by Smith, Warner, and Brinkhous [86]
The precise function of vitamin K was not discovered until 1974, when three laboratories (Stenflo et al ,[87] Nelsestuen et al ,[88] and Magnusson et al [89]) isolated the vitamin K-dependent coagulation factor prothrombin (factor II) from cows that received a high dose of a vitamin K antagonist, warfarin  It was shown that, while warfarin-treated cows had a form of prothrombin that contained 10 glutamate (Glu) amino acid residues near the amino terminus of this protein, the normal (untreated) cows contained 10 unusual residues that were chemically identified as γ-carboxyglutamate (Gla)  The extra carboxyl group in Gla made clear that vitamin K plays a role in a carboxylation reaction during which Glu is converted into Gla 
The biochemistry of how vitamin K is used to convert Glu to Gla has been elucidated over the past thirty years in academic laboratories throughout the world 
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Binkley, S  B ; Thayer, S  A ; Doisy, E  A  (1939)  "On the constitution of Vitamin K1"  Journal of the American Chemical Society  61 (7): 1928–1929  doi:10 1021/ja01876a510  ^ Fieser, L  F  (1939)  "Synthesis of Vitamin K1"  Journal of the American Chemical Society  61 (12): 3467–3475  doi:10 1021/ja01267a072  ^ Dam, C  P  H  (12 Dec 1946)  "The discovery of vitamin K, its biological functions and therapeutical application" (PDF)  Nobel Prize lecture  ^ Warner, E  D ; Brinkhous, K  M ; Smith, H  P  (1938)  "Bleeding Tendency of Obstructive Jaundice"  Proceedings of the Society of Experimental Biology and Medicine  37 (4): 628–630  doi:10 3181/00379727-37-9668P  ^ Stenflo, J; Fernlund, P ; Egan, W ; Roepstorff, P  (Jul 1974)  "Vitamin K dependent modifications of glutamic acid residues in prothrombin"  Proceedings of the National Academy of Sciences of the United States of America  71 (7): 2730–2733  doi:10 1073/pnas 71 7 2730  PMC 388542  PMID 4528109  ^ Nelsestuen, G  L ; Zytkovicz, T  H ; Howard, J  B  (Oct 1974)  "The mode of action of vitamin K  Identification of gamma-carboxyglutamic acid as a component of prothrombin" (PDF)  Journal of Biological Chemistry  249 (19): 6347–6350  PMID 4214105  ^ Magnusson, S ; Sottrup-Jensen, L ; Petersen, T  E ; Morris, H  R ; Dell, A  (Aug 1974)  "Primary structure of the vitamin K-dependent part of prothrombin"  FEBS Letters  44 (2): 189–193  doi:10 1016/0014-5793(74)80723-4  PMID 4472513  Bibliography[edit]
Rhéaume-Bleue, Kate (2012)  Vitamin K2 and the Calcium Paradox  John Wiley & Sons, Canada  ISBN 1-118-06572-7  External links[edit]
"Vitamin K: Another Reason to Eat Your Greens"  v
TPP / ThDP (B1)
FMN, FAD (B2)
NAD+, NADH, NADP+, NADPH (B3)
Coenzyme A (B5)
PLP / P5P (B6)
THFA / H4FA, DHFA / H2FA, MTHF (B9)
AdoCbl, MeCbl (B12)
Phylloquinone (K1), Menaquinone (K2)
non-vitamins
Coenzyme B
Heme / Haem (A, B, C, O)
Molybdopterin/Molybdenum cofactor
THMPT / H4MPT
Fe2+, Fe3+
vitamins: see vitamins
Antihemorrhagics (B02)
(coagulation)
Phytomenadione (K1)
Menadione (K3)
intrinsic: IX/Nonacog alfa
VIII/Moroctocog alfa/Turoctocog alfa
extrinsic: VII/Eptacog alfa
common: X
II/Thrombin
I/Fibrinogen
XIII/Catridecacog
combinations: Prothrombin complex concentrate (II, VII, IX, X, protein C and S)
Carbazochrome
thrombopoietin receptor agonist (Romiplostim
Eltrombopag)
Tetragalacturonic acid hydroxymethylester
Epinephrine/Adrenalone
amino acids (Aminocaproic acid
Aminomethylbenzoic acid)
serpins (Aprotinin
Alfa1 antitrypsin
Camostat) 
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



ground_truth: Symptoms of vitamin K deficiency include anemia, bruising, nosebleeds, bleeding of the gums, and heavy menstrual bleeding in women.

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 is the score achieved by the authors for Track-2?

Read the following text and answer briefly.

Context 3:  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: 85.61%.