Patent Publication Number: US-10331783-B2

Title: NLP-based systems and methods for providing quotations

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 14/748,137 filed Jun. 23, 2015, entitled “NLP-BASED SYSTEMS AND METHODS FOR PROVIDING QUOTATIONS,” which is a continuation of U.S. application Ser. No. 14/169,972 filed Jan. 31, 2014, entitled “NLP-BASED SYSTEMS AND METHODS FOR PROVIDING QUOTATIONS,” which is a continuation of U.S. application Ser. No. 13/075,799 filed Mar. 30, 2011, entitled “NLP-BASED SYSTEMS AND METHODS FOR PROVIDING QUOTATIONS,” which claims the benefit of U.S. Patent Application No. 61/319,029 filed Mar. 30, 2010, entitled “NLP-BASED SYSTEMS AND METHODS FOR PROVIDING QUOTATIONS,” all of which are incorporated herein by reference, in their entireties. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates to techniques for providing quotations and, in particular, to methods, systems, and techniques for extracting, attributing, indexing, and searching for quotations from text documents using natural language processing based techniques. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an example screen display that includes quotations provided by an example embodiment of a content recommendation system. 
         FIG. 2  illustrates an example block diagram of an example embodiment of a content recommendation system used to provide quotations. 
         FIGS. 3A-3H  illustrate example screen displays provided by an example embodiment of a content recommendation system. 
         FIGS. 4A-4D  illustrate additional example screen displays provided by an example embodiment of a content recommendation system. 
         FIG. 5  is an example block diagram of an example computing system for implementing a content recommendation system according to an example embodiment. 
         FIG. 6  is an example flow diagram of a quotation extractor process performed by a content recommendation system according to an example embodiment. 
         FIG. 7  is an example flow diagram of a quotation locator process performed by a content recommendation system according to an example embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments described herein provide enhanced computer- and network-based methods and systems for providing quotations. Example embodiments provide a content recommendation system (“CRS”) configured to recommend content items such as entity information, documents, video, advertisements, product information, and the like. As part of a content recommendation process, in some embodiments, the CRS is configured to provide quotations by extracting quotations from text documents and providing access to the extracted quotations in response to search requests received from users. Extracting a quotation from a text document includes identifying the quotation in the text document, as well as information about the identified quotation. Information about the identified quotation may include a textual or other representation of the quotation (e.g., what was said), an entity to which the quotation is attributed (e.g., a speaker of the quotation), entities that are referenced by the quotation (e.g., the subject of the quotation), and/or relationships between entities referenced by the quotation, or other context. Entities include people, places (e.g., locations), organizations (e.g., political parties, corporations, groups), events, concepts, products, substances, and the like. Table 4, below, includes a list of example entity types. Fewer or more entity types may be available. Information about a quotation may also or instead include various types of meta-information, such as the title of an article or document in which the quotation appears, a publication date, a credibility indication, and the like. 
     The CRS is further configured to store (e.g., index) the extracted quotations and corresponding information, such as quotation speaker and subject entities. Information about entities (e.g., entity name, entity type, and the like) and the relationships between them are represented and stored by the CRS, such that quotations can be accessed based on the entities associated therewith and based upon semantic relationships with those entities. In one embodiment, as discussed further below, the indexed quotations are recognized and stored according to natural language processing (“NLP”) techniques such that details of the quotations and their context can be “understood” by the CRS, beyond what keyword based pattern matching will yield. 
     For example, in some embodiments, once the CRS has extracted quotations from a corpus of text documents, a user can interact with the CRS (e.g., via an interactive user interface) to search for quotations. Because the CRS has indexed not only quotation text, but various types of information about the quotations (e.g., speaker entities, subject entities, categorizations of the speakers, parts-of-speech related information, etc.), the CRS can provide search functionality that can be utilized to request quotations matching a rich set of search expressions. In one example embodiment, in addition to requesting all quotations by a particular speaker, a user can request all quotations by a particular speaker (e.g., Barack Obama) about a particular subject (e.g., health care). 
     In addition, in some embodiments, entities have one or more associated facets, which include finely grained characteristics of entities such as entities, types, and/or characteristics. Example facets include actor, politician, athlete, nation, drug, sport, automobile, and the like. In such embodiments, users can search for quotations based on facet specifications. For example, a user can request all quotations by a particular class of speaker (e.g., a politician) about a particular class of subject (e.g., sports). Table 5, below, includes a list of example facets for the various entity types used in one embodiment. Other expressive search functionality is contemplated and supported by various embodiments, as discussed below. 
       FIG. 1  illustrates an example screen display that includes quotations provided by an example embodiment of a content recommendation system. In particular,  FIG. 1  illustrates a Web browser  100  that displays a screen  102  (e.g., a Web page) that is being used by a user to interact with the content recommendation system. The screen  102  includes a related quotations area  104  and an advertisements section  106 . The related quotations area  104  displays quotations by a particular entity, in this example, Yogi Berra. Each displayed quotation includes the text of the quotation, the speaker of the quotation, a document title (e.g., the headline of a new article in which the quotation appeared), a source (e.g., the publication in which the quotation appeared), a publication date (e.g., the date the quotation was published and/or uttered), and context information, such as indications of one or more entities referenced by the quotation and/or the document in which the quotation appears. Some embodiments of the CRS automatically determine one or more advertisements that are related to one or more of the displayed quotations, and display such advertisements in the advertisements section  106 . In the illustrated example, the advertisements section  106  displays advertisements that are related to the speaker of the displayed quotations (e.g., Yogi Berra) and/or other information about one or more of the displayed quotations, such as a quotation subject. 
       FIG. 2  illustrates an example functional block diagram of an example embodiment of a content recommendation system used to provide quotations. In particular,  FIG. 2  illustrates a content recommendation system (“CRS”)  200  that includes a content ingester  211 , an entity and relationship identifier  212 , a quotation extractor  213 , a quotation locator  214 , a data store  217 , and optionally one or more other content recommenders  215 . The data store  217  includes a document store  217   a , an entity store  217   b , a relationship index  217   c , and a quotation store  217   d . The content ingester  211  receives and indexes content from various content sources, including sources such as Web sites, Blogs, news feeds, video feeds, and the like. The content ingester  211  may also receive content from non-public or semi-public sources, including subscription-based information services, access-controlled social networks, and the like. The content ingester  211  provides content information, including data included within content items (e.g., text, images, video) and meta-data about content items (e.g., author, title, date, source), to the entity and relationship identifier  212 . The content information may be provided directly (as illustrated) and/or via some intermediary, such as the document store  217   a.    
     The entity and relationship identifier  212  receives content information from the content ingester  211  and identifies entities and relationships that are referenced therein. Various automatic and semi-automatic techniques are contemplated for identifying entities within content items. In one embodiment, the identifier  212  uses natural language processing techniques, such as parts of speech tagging and relationship searching, to identify sentence components such as subjects, verbs, and objects, and to identify and disambiguate entities. Example relationship searching technology, which uses natural language processing to determine relationships between subjects and objects in ingested content, is described in detail in U.S. Pat. No. 7,526,425, filed Dec. 13, 2004, and entitled “METHOD AND SYSTEM FOR EXTENDING KEYWORD SEARCHING FOR SYNTACTICALLY AND SEMANTICALLY ANNOTATED DATA” issued on Apr. 28, 2009, and example entity recognition and disambiguation technology is described in detail in U.S. patent application Ser. No. 12/288,158, filed Oct. 15, 2008, and entitled “NLP-BASED ENTITY RECOGNITION AND DISAMBIGUATION,” both of which are incorporated herein by reference in their entireties. Amongst other capabilities, the use of relationship searching, enables the CRS  200  to establish second order (or greater order) relationships between entities and to store such information in the data store  217 . 
     For example, given a sentence such as “Sean Connery starred in Goldfinger,” the identifier  212  may identify “Sean Connery” as the sentence subject, “starred” as the sentence verb (or action), and “Goldfinger” as the sentence object, along with the various modifiers present in the sentence. These parts-of-speech components of each sentence, along with their grammatical roles and other tags may be stored in the relationship index  217   c , for example as an inverted index as described in U.S. Pat. No. 7,526,425. As part of the indexing process, the CRS recognizes and disambiguates entities that are present in the text. Indications of these disambiguated entities are also stored with the sentences information, when the sentence contains uniquely identifiable entities that the CRS already knows about. These entities are those that have been added previously to the entity store  217   b . In some cases, the indexed text contains subjects and objects that indicate entities that are not necessarily known or not yet disambiguated entities. In this case the indexing of the sentence may store as much information as it has in index  217   c , but may not refer to a unique identifier of an entity in the entity store  217   b . Over time, as the CRS encounters new entities, and in some cases with the aid of manual curation, new entities are added to the entity store  217   b . In the above example, “Sean Connery” and “Goldfinger” may be unique entities already known to the CRS and present in the entity store  217   b . In this case, their identifiers will be stored along with the sentence information in the relationship index  217   c . The identified verbs also define relationships between the identified entities. These defined relationships (e.g., stored as subject-action-object or “SAO” triplets, or otherwise) are then stored in the relationship index  217   c . In the above example, a representation of the fact that the actor Sean Connery starred in the film Goldfinger would be added to the relationship index  217   c . In some embodiments, the process of identifying entities may be at least in part manual. For example, entities may be provisionally identified by the identifier  212 , and then submitted to curators (or other humans) for editing, finalization, review, and/or approval. 
     The entity and relationship identifier  212  may determine various other kinds of information about entities and relationships. In one embodiment, the identifier  212  also determines facets, which include finely grained characteristics of entities, such as entity types, roles, qualities, functions, and the like. For example, the entity Sean Connery may have various associated facets, including that of actor, producer, knight, and Scotsman. The facet information for entities may also be stored in the entity store  217   b.    
     The quotation extractor  213  extracts quotations based on information about content items stored in the data store  217  by the ingester  211  and identifier  212 . Extracting quotations from a document may include performing natural language processing upon the document, including linguistic and/or semantic analysis to perform functions such as paragraph and/or sentence detection, parts-of-speech tagging, lexical analysis to detect phrases, semantic analysis to determine how words are used in the document, and the like. In addition, extracting quotations may include identifying entities and associated relationships within the document, disambiguating entities, detecting quotation boundaries and/or verbs, and the like. Further, extracting quotations may include, storing and/or indexing the detected quotations in a data store. In some embodiments, each quotation is represented by the extractor  213  as a triple that includes a speaker, a verb, and a quote, and this triple is recorded in one or more indexes (for example, inverted indexes) stored in the quotation store  217   d . Additional information may be stored in association with an extracted quotation, such as entity information (e.g., one or more entity identifiers), speaker modifiers (e.g., terms modifying the quotation speaker), action modifiers (e.g., terms modifying the quotation verb), and the like. Additional techniques for quotation extraction are discussed with reference to  FIG. 6  and in the section entitled “ Quotation Recommendation Details in an Example Embodiment ,” below. 
     The quotation locator  214  provides access to stored (e.g., indexed) quotations based on a received quotation request from a user  202  or some other source. In one embodiment, the received quotation request includes a search query that specifies search information, such as speaker, subject, keyterms, or the like. The quotation locator  214  determines one or more quotations that match, or approximately match, the search query, and provides (e.g., transmits, sends, displays) the determined one or more quotations to the user  202 . In some embodiments, the search query uses relationship searching, such as that described in U.S. Pat. No. 7,526,425, to identify matching quotations beyond that provided by simple keyword matching or regular expression techniques. Additional techniques for quotation location are discussed with reference to  FIG. 7  and in the section entitled “ Quotation Recommendation Details in an Example Embodiment ,” below. 
     The other content recommender  215  provides other types of content recommendations, such as for articles, entities, product information, advertisements, and the like. For example, in one embodiment, the other content recommender  215  is or includes an article recommender that determines articles that are related to collections of entities specified by the user  202 . In other embodiments, the other content recommender  215  is or includes an entity recommender that determines entities that are related to collections of entities specified by the user  202 . One such example entity recommender for use with collections of entities is described in detail in U.S. Patent Application No. 61/309,318, filed Mar. 1, 2010, and entitled “CONTENT RECOMMENDATION BASED ON COLLECTIONS OF ENTITIES,” which is incorporated herein by reference in its entirety. 
     The described techniques herein are not limited to the specific architecture shown in  FIG. 2  or described in conjunction with the relationship indexing and searching techniques or the disambiguation techniques described above. For example, in some embodiments, content ingestion and entity and relationship identification may be performed by another (possibly external or remote) system or component. In other embodiments, the CRS  200  may not interact directly with users, but rather provide user interface components (e.g., quotations widgets) that may be embedded or otherwise incorporated in third-party applications or systems, such as Web sites, smart phones, desktop systems, touch devices, personal productivity devices, tablet computers, mobile applications, and the like. In further embodiments, the CRS  200  may provide an application program interface, such that external programs can programmatically receive and/or provide quotations to/from the CRS  200 . 
     In addition, although the described techniques for providing quotations are illustrated primarily with respect to text documents, other forms of content items are contemplated. For example, other embodiments may utilize at least some of the described techniques to provide quotations extracted from other forms of content, including video, audio, and the like. Also, text documents include document types beyond documents represented in a text format (e.g., ASCII or UNICODE documents). In particular, text documents include any documents that have any textual content, independent of format, such as PDF documents, Microsoft Office documents, and the like. 
       FIGS. 3A-3H  illustrate example screen displays provided by an example embodiment of a content recommendation system. More specifically,  FIGS. 3A-3H  depict a running example, in which a user interacts with a Web site operated by the content recommendation system, in order to obtain quotations by and/or about various entities. 
       FIG. 3A  shows an example home page provided by the content recommendation system. In particular,  FIG. 3A  illustrates a Web browser  300  displaying a screen  301  provided to a user by a content recommendation system. The screen  301  is a home or main page provided by the content recommendation system, such as may be provided to the user when he or she first accesses the content recommendation system. Various examples of different user interfaces for accessing a content recommendation system are described in detail in U.S. patent application Ser. No. 12/288,349, filed Oct. 16, 2008, and entitled “NLP-BASED CONTENT RECOMMENDER,” which is incorporated herein by reference in its entirety. The screen  301  includes a search tool  302  and an articles area  304 . As will be described further below, the user can use the search tool  302  to initiate searches for information about content (e.g., articles, entities) known to the content recommendation system. The articles area  304  provides information about articles known to the content recommendation system, possibly arranged by categories, such as popular, politics, business, entertainment, sports, technology, and the like. 
       FIG. 3B  illustrates initiation of a search via the content recommendation system. In particular,  FIG. 3B  shows the Web browser  300  displaying screen  301  during the initiation of a search by the user. As the user enters text into the search tool  302 , a drop down menu  310  is generated and displayed. The drop down menu  310  displays multiple indications of entities that match a string (e.g., “bara”) entered by the user, such that the user can select one of the entities as the subject of the search. In the present example, the user is selecting an entity named “Barack Obama.” 
       FIG. 3C  shows an entity information page provided as a result of the search initiated in  FIG. 3B . In particular,  FIG. 3C  illustrates the Web browser  300  displaying a screen  320  provided in response to a search for the entity named “Barack Obama.” The screen  320  is an entity information page that includes an entity relationship graph  321 , a related content area  322 , and a related quotes control  324  (labeled “Quotes”). The relationship graph  321  graphically presents relationships between Barack Obama and one or more other entities known to the CRS. The related content area  322  displays indications of articles and other content that reference Barack Obama. The related content area is organized by different content types, including articles, tweets, images, quotes, and videos. By selecting the related quotes control  324 , the user can obtain quotes by and/or about the current entity (e.g., Barack Obama), as described with reference to  FIG. 3D . 
       FIG. 3D  depicts quotes about a specified entity. In particular,  FIG. 3D  illustrates screen  320  presenting a quotes area  330  displayed in response to selection of the related quotes control  324  of  FIG. 3C . The quotes area  330  includes multiple quotation records that each provide information about a quotation about a particular entity, in this case, Barack Obama. Each quotation record includes various types of information about or related to the quotation, including quotation text, quotation source, article context information (e.g., other entities referenced in the article), and the like. 
       FIG. 3E  depicts quotes by a specified entity. In particular,  FIG. 3E  illustrates screen  320  presenting a quotes area  340  displayed in response to selection of the related quotes control  324  of  FIG. 3C . The quotes area  340  is similar to the quotes area  330  described with reference to  FIG. 3D , except that the quotes area  340  presents quotations by (rather than about) a particular entity, in this case, Barack Obama. 
       FIG. 3F  depicts quotes made by a specified entity about a specified topic entity. In particular,  FIG. 3F  illustrates a screen  350  presenting quotes by Barack Obama about China. As noted, some embodiments provide an expressive search language that can be utilized to specify quotation searches beyond those simply by or about a particular entity. As illustrated here, the content recommendation system has located quotes by a particular entity and about a particular topic. 
       FIG. 3G  depicts quotes about a specified topic entity made by entities having a specified facet. In particular,  FIG. 3G  illustrates a screen  360  presenting quotes by politicians about the iPhone smart phone. Here, the content recommendation system has located quotes by a particular class, type, or group of entity (e.g., specified here by the politician facet) and about a particular topic (e.g., specified here by an indicator of the iPhone entity). 
       FIG. 3H  depicts quotes that include specified keyterms. In particular,  FIG. 3H  illustrates a screen  370  presenting quotes that include the keyterm “global warming.” 
     The various search techniques discussed above can be combined in various ways in other embodiments. For example, searches can be made for quotes by entities having a specified facet and that include one or more specified keyterms (e.g., quotes by politicians and containing the keyterms “global warming”). Or searches can be made for quotes made by specified entities about entities having a specified facet and including one or more specified keyterms (e.g., quotes by comedians about politicians and containing the keyterms “global warming”). A search language syntax provided and implemented by a specific example embodiment is described with reference to the section entitled “ Quotation Recommendation Details in an Example Embodiment ,” below. 
       FIGS. 4A-4D  illustrate additional example screen displays provided by an example embodiment of a content recommendation system. More specifically,  FIGS. 4A-4C  depict various user interface techniques for creating quotations widgets, and  FIG. 4D  depicts quotation information provided in XML format by one example embodiment. 
       FIG. 4A  shows a widget gallery provided by a content recommendation system. In particular,  FIG. 4A  illustrates a Web browser  400  displaying a screen  401  provided to a user by the content recommendation system. The screen  401  is a widget gallery provided by the content recommendation system. Screen  401  includes a control (e.g., a link)  402  that can be selected by a user to create a quotations widget. A quotations widget is a code module that can be included in an application (e.g., a third-party application or some other software system) and that is configured to provide quotations by or about specified entities. 
       FIG. 4B  illustrates the creation of a quotations widget. In particular,  FIG. 4B  illustrates a screen  410  displayed in response to a user selection of control  402  of  FIG. 4A . The screen  410  includes a quotations widget creation tool  411  that includes a search tool and drop down menu that can be selected by the user to specify an entity that is the speaker or subject of a generated quotations widget. In the present example, the user is creating a quotations widget for the entity Barack Obama. In other embodiments, additional user interface controls may be included, such that the user can specify a more fine-grained search for quotations. For example, one embodiment provides additional controls such as a speaker field, a subject field, one or more date fields (e.g., to specify a date range), a keyword field, and the like. 
       FIG. 4C  shows a quotations widget preview. In particular,  FIG. 4C  illustrates a screen  420  that is a quotations widget preview page displayed in response to the creation of a quotations widget as described with reference to  FIG. 4B . The screen  420  includes a widget preview area  421  and a widget code area  422 . The preview area  421  displays a preview of the created widget, as it would appear if it were embedded in a third-party application, such as a Web page, blog, or the like. The code area  422  provides instructions (e.g., JavaScript code) that can be copied by a user into some other application. The provided instructions are configured, when executed, to display the quotations widget previewed in the preview area  421 . 
       FIG. 4D  shows example quotation information represented in an XML format. In particular  FIG. 4D  illustrates a screen  440  that presents an XML structure (e.g., a tree) that includes multiple quotation sections, such as sections  441  and  442 , that each represent information about a quotation. The illustrated XML structure may be provided in response to, for example, a search request such as those described with reference to  FIGS. 3D-3H , above. The quotation sections  441  and  442  each include various types of information about a quotation, such as a content section that includes the text of the quotation, a speaker section that describes the speaker of the quotation, an source information section (e.g., describing article author, title, publication date), and the like. 
     Although the user interface techniques of  FIGS. 3A-3H and 4A-4D  have been described primarily with reference to Web-based technologies, the described techniques are equally applicable in other contexts. For example, a quotations widget may be implemented as one or more code objects (e.g., a Java object) that can be embedded within a client-side desktop, personal productivity device, tablet computer, or mobile device, and configured to interact with the content recommendation system to obtain and display quotations provided by the content recommendation system. 
       FIG. 5  is an example block diagram of an example computing system for implementing a content recommendation system according to an example embodiment. In particular,  FIG. 5  shows a computing system  500  that may be utilized to implement a content recommendation system  510 . 
     Note that one or more general purpose or special purpose computing systems/devices may be used to implement the content recommendation system  510 . In addition, the computing system  500  may comprise one or more distinct computing systems/devices and may span distributed locations. Furthermore, each block shown may represent one or more such blocks as appropriate to a specific embodiment or may be combined with other blocks. Also, the content recommendation system  510  may be implemented in software, hardware, firmware, or in some combination to achieve the capabilities described herein. 
     In the embodiment shown, computing system  500  comprises a computer memory (“memory”)  501 , a display  502 , one or more Central Processing Units (“CPU”)  504 , Input/Output devices  504  (e.g., keyboard, mouse, CRT or LCD display, and the like), other computer-readable media  505 , and network connections  506 . The content recommendation system  510  is shown residing in memory  501 . In other embodiments, some portion of the contents, some or all of the components of the content recommendation system  510  may be stored on and/or transmitted over the other computer-readable media  505 . The components of the content recommendation system  510  preferably execute on one or more CPUs  503  and extract and provide quotations, as described herein. Other code or programs  530  (e.g., an administrative interface, a Web server, and the like) and potentially other data repositories, such as data repository  520 , also reside in the memory  501 , and preferably execute on one or more CPUs  503 . Of note, one or more of the components in  FIG. 5  may not be present in any specific implementation. For example, some embodiments may not provide other computer readable media  505  or a display  502 . 
     In a typical embodiment, the content recommendation system  510  includes a content ingester  511 , an entity and relationship identifier  512 , a quotation extractor  513 , a quotation locator  514 , a user interface manager  515 , a quotation provider application program interface (“API”)  516 , and a data store  517 . The content ingester  511 , entity and relationship identifier  512 , user interface manager  515 , and recommender API  516  are drawn in dashed lines to emphasize that in other embodiments, functions performed by one or more of these components may be performed externally to the content recommendation system  510 . In other embodiments, the content recommendation system  510  includes other content recommendation modules that are configured to provide other types of content, such as article or entity recommendations based on user searches, user preferences, entity collections, and the like. 
     The content ingester  511  performs functions such as those described with reference to the content ingester  211  of  FIG. 2 . The ingester  511  obtains content items, such as Web pages, Blog postings, videos, audio files, and the like from various content sources  555  via network  550 , and stores information about the obtained content items in the data store  517 , for use by other components, such as the entity and relationship identifier  512 , the quotation extractor  513 , and/or the quotation locator  514 . 
     The entity and relationship identifier  512  performs functions such as those described with reference to the entity and relationship identifier  212  of  FIG. 2 . The identifier  512  processes the information about content items stored in the data store  517  in order to identify entities and relationships between them. Identified entities and relationships are stored in the data store  517 , for use by other components, such as the quotation extractor  513  and/or the quotation locator  514 . 
     The UI manager  515  provides a view and a controller that facilitate user interaction with the content recommendation system  510  and its various components. For example, the UI manager  515  may provide interactive access to the content recommendation system  510 , such that users can obtain quotations, generate quotations widgets, and the like. In some embodiments, access to the functionality of the UI manager  515  may be provided via a Web server, possibly executing as one of the other programs  530 . In such embodiments, a user operating a Web browser executing on one of the client devices  560  can interact with the content recommendation system  510  via the UI manager  515 . 
     The quotation extractor  513  performs functions such as those described with reference to the quotation extractor  213  of  FIG. 2 . The extractor  513  extracts quotations based on information about content items stored in the data store  517  by the ingester  511  and identifier  512 . The extracted quotations are stored in the data store  517  for use by other components, such as the quotation locator  514 , UI manager  515 , and the like. 
     The quotation locator  514  performs functions such as those described with reference to the quotation locator  214  of  FIG. 2 . The quotation locator  514  receives, possibly via the UI manager  515 , a request including a search query specifying one or more entities by or about which quotations are desired. In response, the quotation locator  514  searches the data store  517  to determine one or more quotations that match the received query. The determined quotations can be cached (e.g., stored in the data store  517 ) for later use and/or provided directly in response to a received request, such as may be generated by the UI manager  515 . 
     In one embodiment, the quotation locator  514  operates synchronously in an on-demand manner, in that it performs its functions in response to a received request, such as in response to a user interface event processed by the UI manager  515 . In another embodiment, the quotation locator  514  operates asynchronously, in that it automatically determines quotations for one or more queries. For example, the quotation locator  514  may automatically execute from time to time (e.g., once per hour, once per day) in order to generate bulk quotation information for commonly requested (or recently used) queries. The quotation locator  514  may execute upon the occurrence of other types of conditions, such as when new quotations are extracted and/or stored, and the like. 
     The API  516  provides programmatic access to one or more functions of the content recommendation system  510 . For example, the API  516  may provide a programmatic interface to one or more functions of the content recommendation system  510  that may be invoked by one of the other programs  530  or some other module. In this manner, the API  516  facilitates the development of third-party software, such as user interfaces, plug-ins, widgets, news feeds, adapters (e.g., for integrating functions of the content recommendation system  510  into Web applications), and the like. In addition, the API  516  may be in at least some embodiments invoked or otherwise accessed via remote entities, such as one of the third-party applications  565 , to access various functions of the content recommendation system  510 . For example, a third-party application may request quotations from the content recommendation system  510  via the API  516 . The API  516  may also be configured to provide quotations widgets (e.g., code modules) that can be integrated into third-party applications and that are configured to interact with the content recommendation system  510  to make at least some of the described functionality available within the context of other applications. The section entitled “ Example Quotation Recommendation API ,” below, describes an example API provided by one specific embodiment of an example CRS. 
     The data store  517  is used by the other modules of the content recommendation system  510  to store and/or communicate information. As discussed above, components  511 - 516  use the data store  517  to record various types of information, including content, information about stored content including entities and relationships, information about quotations, user information, and the like. Although the components  511 - 516  are described as communicating primarily through the data store  517 , other communication mechanisms are contemplated, including message passing, function calls, pipes, sockets, shared memory, and the like. 
     The content recommendation system  510  interacts via the network  550  with content sources  555 , third-party applications  565 , and client computing devices  560 . The network  550  may be any combination of media (e.g., twisted pair, coaxial, fiber optic, radio frequency), hardware (e.g., routers, switches, repeaters, transceivers), and protocols (e.g., TCP/IP, UDP, Ethernet, Wi-Fi, WiMAX) that facilitate communication between remotely situated humans and/or devices. The client computing devices  560  include desktop computing systems, notebook computers, mobile phones, smart phones, personal digital assistants, tablet computers, and the like. 
     Other or additional functions and/or data are contemplated. For example, in some embodiments, the content recommendation system  510  includes additional content recommendation components that are specialized to other types of content, such as for video, quotations, images, audio, advertisements, product information, and the like. 
     In an example embodiment, components/modules of the content recommendation system  510  are implemented using standard programming techniques. For example, the content recommendation system  510  may be implemented as a “native” executable running on the CPU  503 , along with one or more static or dynamic libraries. In other embodiments, the content recommendation system  510  may be implemented as instructions processed by a virtual machine that executes as one of the other programs  530 . In general, a range of programming languages known in the art may be employed for implementing such example embodiments, including representative implementations of various programming language paradigms, including but not limited to, object-oriented (e.g., Java, C++, C#, Visual Basic.NET, Smalltalk, and the like), functional (e.g., ML, Lisp, Scheme, and the like), procedural (e.g., C, Pascal, Ada, Modula, and the like), scripting (e.g., Peri, Ruby, Python, JavaScript, VBScript, and the like), and declarative (e.g., SQL, Prolog, and the like). 
     The embodiments described above may also use either well-known or proprietary synchronous or asynchronous client-server computing techniques. Also, the various components may be implemented using more monolithic programming techniques, for example, as an executable running on a single CPU computer system, or alternatively decomposed using a variety of structuring techniques known in the art, including but not limited to, multiprogramming, multithreading, client-server, or peer-to-peer, running on one or more computer systems each having one or more CPUs. Some embodiments may execute concurrently and asynchronously, and communicate using message passing techniques. Equivalent synchronous embodiments are also supported. Also, other functions could be implemented and/or performed by each component/module, and in different orders, and by different components/modules, yet still achieve the described functions. 
     In addition, programming interfaces to the data stored as part of the content recommendation system  510 , such as in the data store  517 , can be available by standard mechanisms such as through C, C++, C#, and Java APIs; libraries for accessing files, databases, or other data repositories; through scripting languages such as XML; or through Web servers, FTP servers, or other types of servers providing access to stored data. The data store  517  may be implemented as one or more database systems, file systems, or any other technique for storing such information, or any combination of the above, including implementations using distributed computing techniques. 
     Different configurations and locations of programs and data are contemplated for use with techniques of described herein. A variety of distributed computing techniques are appropriate for implementing the components of the illustrated embodiments in a distributed manner including but not limited to TCP/IP sockets, RPC, RMI, HTTP, Web Services (XML-RPC, JAX-RPC, SOAP, and the like). Other variations are possible. Also, other functionality could be provided by each component/module, or existing functionality could be distributed amongst the components/modules in different ways, yet still achieve the functions described herein. 
     Furthermore, in some embodiments, some or all of the components of the content recommendation system  510  may be implemented or provided in other manners, such as at least partially in firmware and/or hardware, including, but not limited to one or more application-specific integrated circuits (“ASICs”), standard integrated circuits, controllers executing appropriate instructions, and including microcontrollers and/or embedded controllers, field-programmable gate arrays (“FPGAs”), complex programmable logic devices (“CPLDs”), and the like. Some or all of the system components and/or data structures may also be stored as contents (e.g., as executable or other machine-readable software instructions or structured data) on a computer-readable medium (e.g., as a hard disk; a memory; a computer network or cellular wireless network or other data transmission medium; or a portable media article to be read by an appropriate drive or via an appropriate connection, such as a DVD or flash memory device) so as to enable or configure the computer-readable medium and/or one or more associated computing systems or devices to execute or otherwise use or provide the contents to perform at least some of the described techniques. Some or all of the system components and/or data structures may be stored as non-transitory content on one or more tangible computer-readable mediums. Some or all of the system components and data structures may also be stored as data signals (e.g., by being encoded as part of a carrier wave or included as part of an analog or digital propagated signal) on a variety of computer-readable transmission mediums, which are then transmitted, including across wireless-based and wired/cable-based mediums, and may take a variety of forms (e.g., as part of a single or multiplexed analog signal, or as multiple discrete digital packets or frames). Such computer program products may also take other forms in other embodiments. Accordingly, embodiments of this disclosure may be practiced with other computer system configurations. 
       FIG. 6  is an example flow diagram of quotation extractor process performed by a content recommendation system according to an example embodiment. In particular,  FIG. 6  illustrates a process that may be implemented by, for example, one or more elements of the content recommendation system  200 , such as the quotation extractor  213 , as described with reference to  FIG. 2 . The process extracts quotations from a document or other text source. 
     The illustrated process begins at block  602 , where it receives an indication of a document. Receiving an indication of a document may include receiving a document reference (e.g., a file name, a uniform resource identifier, a database identifier). The received document reference may identify an original source document, such as may be found on a remote Web server or other document source, such as one of the content sources  255  described with reference to  FIG. 2 . In other embodiments, the received document reference may instead identify document information generated about a document by some other component of the content recommendation system, such as may be stored in the document store  217   a  described with reference to  FIG. 2 . 
     At block  604 , the process identifies entities appearing within the text document, such as using an NLP-based recognition and disambiguation process, for use in attributing any quotations found within the text document. Identifying entities may include performing linguistic and/or semantic analysis of the document. Linguistic and semantic analysis may include such operations as sentence/paragraph detection, parts-of-speech and/or grammatical role tagging, phrase or clause detection, and the like. Identifying entities may also include linking references to the same entity across the document, including by resolving pronoun co-references, aliases and abbreviations, and definite-noun anaphora, and the like. Identifying entities may include other operations, such as entity disambiguation, facet assignment, and the like. 
     At block  606 , the process determines whether there are quotations within the text document. One way the CRS detects quotations is by determining whether one of a predetermined verb likely to indicate a quotation is present within the text and whether quotation marks are present in the text. The presence of both will indicate a more likely chance that a quotation is present within the document. Thus, determining quotations includes detecting quotations by extracting one or more sentences of a potential quotation based on detected quotation verbs (e.g., say, comment, suggest) and/or quotation punctuation (e.g., double or single quotation marks). 
     At block  608 , the process attributes the quotation (e.g., by determining the speaker entity of the quotation) using, for example, the identified entities in block  604  and determines additional quotation information, for example determining the quotation verb; determining the quotation text; and determining other entities, facets, and/or keywords referenced in the quotation. 
     At block  610 , the process stores (or otherwise indexes) the determined quotation information. Storing the determined quotation information may include, for each quotation detected at block  606 , indexing a subject-action-object triple, where the subject field identifies the speaker entity of the quotation, the action field identifies the quotation verb, and the object field stores the quotation text. Other information may be indexed, including various modifiers (e.g., subject or verb modifiers), other entity information (e.g., entities and/or facets referenced by the quotation), time and date information, source information, and the like. 
     After block  610 , the process returns. 
     Some embodiments perform one or more operations/aspects in addition to, or instead of, the ones described with reference to the process of  FIG. 6 . For example, in one embodiment, the process includes a loop that repeatedly receives and processes documents, so as to perform bulk quotation extraction upon a batch (e.g., corpus) of documents. In another embodiment, the process of  FIG. 6  is invoked in an on-demand manner in response to a received user interface request. Techniques for quotation extraction used in a specific example embodiment are described with reference to the section entitled “ Quotation Recommendation Details in an Example Embodiment ,” below. 
       FIG. 7  is an example flow diagram of a quotation locator process performed by a content recommendation system according to an example embodiment. In particular,  FIG. 7  illustrates a process that may be implemented by, for example, one or more elements of the content recommendation system  200 , such as the quotation locator  214 , as described with reference to  FIG. 2 . The process provides quotations that match received search requests. 
     The illustrated process begins at block  702 , where it receives a quotation search request. The quotation search request may be received from various sources, such as from an interactive user interface being operated by a user and/or a quotations widget or other code module configured to automatically request quotations. 
     Typically, the quotation search request specifies one or more features that are to be present in any quotation that matches the request. For example, the request may specify one or more quotation speakers (e.g., by indicating an entity or a facet), one or more quotation subjects (e.g., by indicating entities, facets, and/or keywords), and the like. In some embodiments, the specified features can be combined or modified, such as via Boolean operators (e.g., AND, OR, NOT). 
     At block  704 , the process locates one or more quotations that match the received search request. Locating matching quotations may include searching an index or other representation of quotation information, to determine one or more quotations that match or approximately match one or more features of the quotation search request. In one embodiment, locating matching quotations includes searching the quotations store  217   d  based upon NLP-based search techniques as described with reference to  FIG. 2 . 
     At block  706 , the process provides indications of the located quotations. Providing indications of the located quotations may include presenting, or transmitting for presentation, various information about the located quotations, such as quotation text, quotation attribution (e.g., speaker), context (e.g., text surrounding the quotation), and the like. Providing quotations may also include ranking or ordering the located quotations based on one or more factors, such as publication date, source credibility, and number of duplicate quotations. 
     Some embodiments perform one or more operations/aspects in addition to, or instead of, the ones described with reference to the process of  FIG. 7 . For example, some embodiments aggregate located quotations by identifying groups of identical or similar quotations, and then providing only a single quotation for each identified group of quotations. Techniques for quotation location and presentation used in a specific example embodiment are described with reference to the section entitled “ Quotation Recommendation Details in an Example Embodiment ,” below. 
     In the following, additional example techniques for extracting and providing quotations are discussed. 
     Quotation Recommendation Details in an Example Embodiment 
     The following describes an approach to quotation extraction and search used by one example embodiment. 
     Overview 
     In this example embodiment, the quotation extraction and search subsystem of the content recommendation system (“CRS”) has three components: 
     1. Quotation extraction and attribution from text documents 
     2. Indexing of the extracted quotations and attributions in an efficient inverted index 
     3. Search of the indexed quotations 
     EXAMPLES 
     On any entity profile page, if the entity is a person, the CRS presents retrieved quotes about the person, followed by quotes made by the person. For example, on the profile page of President Obama: 
     http://www.evri.com/person/barack-obama-0x16f69/quotes 
     Quotes about Barack Obama 
     Quotes by Barack Obama 
     If the entity is not a person, the CRS presents quotes about the entity, e.g.,
         quotes about iPhone: http://www.evri.com/product/iphone-0x4d735/quotes   quotes about Afghanistan: http://www.evri.com/location/afghanistan-0x31cde/quotes       

     The CRS also surfaces quotes about any keywords or phrases, e.g., http://www.evri.com/find/quotes?query=qlobal+warming returns quotes about phrase “global warming” 
     Evri Public APIs: 
     In addition, the CRS exposes quotation search capability via a set of public APIs (see http://www.evri.com/developer/rest#API-GetQuotations). 
     Quotes Public API examples:
         quotes by a person, e.g., http://api.evri.com/v1/quotations?speaker=/person/barack-obama-0x16f69   quotes about an entity, e.g., http://api.evri.com/v1/quotations/about?entityURI=/product/iphone-0x4d735   quotes by any entities of certain facet, e.g., quotes made by football players http://api.evri.com/v1/quotations?speaker=facet/footbal_player   quotes about any entities of certain facet, e.g., quotes about college football teams http://api.evri.com/v1/quotations/about?facet=college_football_team   quotes about any keyword or phrase, e.g., http://api.evri.com/v1/quotations/about?phrase=hoyas   quotes made by a person about any entities of a particular facet, e.g., quotes made by David Letterman about any politicians, http://abi.evri.com/v1/quotations/about?facet.politician&amp;speaker=/person/david-letterman-0x1b480   quotes made by any entities of a particular facet about something, e.g., quotes made by any football players about super bowl, http://qpi.evri.com/v1/quotations/about?phrase=super%20bowl&amp;speaker=facet/football_player       

     The “facets” of an entity are typically discoverable from an ontology or taxonomy of entities. A list of example facets appears in the section entitled “Example Facets.” Fewer or more facets can be made available, and the set of facets used by the system are generally configurable. The facets may be organized into a hierarchical taxonomy. Therefore, a facet can be represented as a taxonomic path, e.g., [Person/Sports/Athlete/Football_Player]. During search time, the CRS has the flexibility to support query on “Football_Player”, or any of its parent nodes in the taxonomic path, e.g., “Athlete” or “Person”. 
     An example API is discussed in more detail below in the section entitled “Example Quotation Recommendation API.” 
     Quotation Extraction and Attribution 
     I. Linguistic and semantic analysis: Given a text document, the example CRS applies deep linguistic analysis that includes at least some of the following steps:
         a) split the document into paragraphs, and paragraphs into sentences   b) for each sentence, apply linguistic parsing to extract part-of-speech tags (e.g., nouns, verbs), performing lexical analysis (e.g., detecting phrases), and determining grammatical roles (e.g., subjects, verbs, objects)   c) Apply named entity recognition to identify entities and concepts appearing in the text.
           1. Link multiple mentions of the same entity across the document, including resolving pronoun coreference (e.g., “he”, “him”, “she”), aliases and abbreviations (“Obama”, “Gates”, “Bill”), and the definite-noun anaphora (e.g., “the president”, “the coach”). The coreference resolution may be useful in determining quotation attributions, because very often the speaker&#39;s full name is not provided for a given quote. Instead, the writer typically uses pronouns (“he said”), partial names (“said Gates”), or definite nouns (“the president said”). Similarly, in quotations, entities are often mentioned as aliases or pronoun anaphoras. Applying coreference resolution typically helps identify such mentions, that otherwise would be missed by keyword matching techniques.   2. To each entity, the CRS assigns its type and facet categories. For example, the CRS tags the entity “Michael Jackson” with type ‘person’ and facet as ‘musician’.   3. Apply entity disambiguation such that each mention of an entity is linked to an entry in a repository of entities. As a result, different mentions of an entity are all marked with a unique identifier. During search, the CRS supports search of entities by their unique identifiers, instead of using ambiguous keywords. For example, the CRS is able to distinguish between Will Smith the actor and Will Smith the American football player who plays for the New Orleans Saints.   
               

     II. Quotation verb detection:
         a) For each verb detected in a sentence, the CRS checks if the verb belongs to a pre-determined list of verbs that can be potentially used to indicate a quotation (e.g., acknowledge, argue, caution, say, suggest, urge, etc.)   b) Check the appearance and positions of quotation marks in the sentence   c) Determine quotation candidates based on combination of the above two factors, e.g., if there is a quotation verb and there are quotation marks nearby, the CRS has higher confidence there is a quotation contained in the text.       

     III. Attribution and Collapsing: Collapse each detected quotation into a triple of (speaker, verb, quote)
         a) Speaker: the main subject of the verb, as well as its modifier, such as title and affiliation of the speaker (e.g., given “said Microsoft chairman Bill Gates . . . ”, the CRS recognizes “Bill Gates” as the speaker, with “Microsoft” and “chairman” as the modifiers)   b) Verb: quotation verb. In addition, the CRS stores the prepositional modifiers of the verb. The modifiers usually provide context of the quote being made (e.g., given “said Bill Gates in the Microsoft shareholder meeting in Seattle”, the modifiers are “in Seattle” and “in the Microsoft shareholder meeting”)   c) Quote: actual quotes within beginning and ending quotation marks. Note that a quote could span multiple sentences. The CRS searches for starting and ending quotation marks from the neighboring sentences, and determine the proper quote boundaries. Then, the CRS stores all the segments of the same quote here.
 
Indexing
       

     Each extracted quotation and attribution is stored as a triple in an inverted index structure of subject-action-object triples. The quotation triples are distinguished from regular triples by a flag isQuotation. During search, only quotation triples will be retrieved when the isQuotation flag is set in the query.
         Subject field: store the speaker entity name, the entity&#39;s ID, the entity&#39;s facets, and the isQuotation flag   Subject-modifier field: store modifiers of the speaker   Action: store the verb, as well as the isQuotation flag   Action-modifier field: store context modifiers of the quotation   Object field: store the quote, entities recognized in the quote, and the isQuotation flag       

     For entities identified both within and outside of the quote, the CRS indexes not only the entity names, but also their unique identifiers and assigned categories (e.g., types and facets). Therefore, during search, the CRS supports search for quotes by or about entities by their names, as well as by their IDs or categories (e.g., find quotes made by any college football coach, or find quotes about any hybrid cars). 
     The subject-modifier field would support search for quotes made by speakers of certain properties, e.g., “Did anyone from Microsoft say anything about iPhone?” 
     Similarly, the action-modifier field supports searching for quotes within a particular context, e.g., “What did Obama say about global warming during his trip to China?” 
     Example 1 
     Text snippet: Cleveland Cavaliers star LeBron James stuck his nose in the situation, admitting he is counseling Pryor on the pitfalls of being in the spotlight at a young age. 
     “I&#39;m trying to be that guy who can really help him get through a lot of situations which he&#39;s never seen before but now he&#39;s seeing and understanding,” James said. 
     In this example, the CRS are able to link “James” as the last name of LeBron James. Through coreference resolution, the CRS resolves the pronouns “he” and “him” to Terrelle Pryor, a football quarterback of the Ohio State University football team. Furthermore, the CRS tags Pryor with facet “football_player”. Therefore, when user queries about any comments made by LeBron James on any football players, this quote would be returned as one of the results. 
     Example 2 
     Nash said, “I would love to meet him, obviously, and to play hoops with the President would be kind of fun.” 
     This quote is from Steve Nash of the Phoenix Suns NBA basketball team, about President Obama. Through coreference resolution, the CRS recognizes “him” and “the president” refer to President Obama. The CRS assigns the facet “basketball player” to Steve Nash. Therefore, when user queries about any comments made by any basketball players (or any sports athletes) regarding President Obama, this quote would be returned as one of the results. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 Field 
                 What is indexed 
               
               
                   
                   
               
             
            
               
                   
                 Speaker 
                 Entity name = Steve Nash 
               
               
                   
                   
                 Entity ID = 0x49c26 
               
               
                   
                   
                 Facet = Basketball player 
               
               
                   
                 Action 
                 Verb = said, isQuotation 
               
               
                   
                 Quotation 
                 Keywords = Steve Nash, love, meet, 
               
               
                   
                   
                 Barack Obama, obviously, play, hoops, 
               
               
                   
                   
                 president, fun 
               
               
                   
                   
                 Entity 1: 
               
               
                   
                   
                   Name = Barack Obama 
               
               
                   
                   
                   ID = 0x49c26 
               
               
                   
                   
                   Facet = Politician, Country leader 
               
               
                   
                   
               
            
           
         
       
     
     Example 3 
     “They might think they&#39;ve got a pretty good jump shot or a pretty good flow, but our kids can&#39;t all aspire to be LeBron or Lil Wayne,” Obama said. 
     The CRS recognizes LeBron as LeBron James, the NBA basketball player, and Lil Wayne as a musician. The pronoun “they” is linked to “children” in the previous sentence. When the query is for Obama&#39;s quotes regarding basketball player or musician, this quote would be returned. 
                                 TABLE 2                       Field   What is indexed                          Speaker   Entity Name = Barack Obama               Entity ID = 0x16f69               Facet = Politician, Country Leader           Action   Verb = said, isQuotation           Quotation   Keywords = children, think, get, pretty,               good, jump shot, flow, kids, aspire,               LeBron James, Lil Wayne               Entity 1:                 Entity Name = LeBron James                 Entity ID = 0x49c85                 Facet = Basketball player               Entity 2:                 Name = Lil Wayne                 ID = 0x15393                 Facet = Musician                        
Search
 
Query:
 
     The CRS supports querying of quotations in many different ways, in the form of a template: What did &lt;speaker&gt; say about &lt;subject&gt;? 
     The parameter &lt;speaker&gt; can be specified as:
         an entity, by its unique identifier or simply its name   a facet, e.g., football player   or anyone   Furthermore, the speaker field can be constrained by some modifiers, e.g.,   “What did any &lt;football_player&gt; from Notre Dame say?” where Notre Dame is the modifier.   The &lt;subject&gt; can be specified as:   an entity, by its unique identifier or name   an entity facet, e.g., movies, hybrid cars   any keywords   or anything   The CRS also supports boolean combinations (e.g., AND, OR) of the above.
 
For example:
   What did Obama say about China AND global warming?   What did Peyton Maning say about other football teams OR football coaches?   Find quotes by any actors about the Oscars AND any movies.
 
Result Presentation:
       

     The query result returned is a list of quotations, that contain the following:
         actual quote; the starting and ending positions of the quote are marked   quote attribution speaker name and its modifiers   context text outside the quote   document metadata, e.g., document title, publication date, publisher name, and the like.       

     Sometimes, the quote is very long such that the CRS needs to extract a snippet of a specified length that best matches the query request. During processing and indexing, the CRS has identified the entities in each sentence, as well as their positions within the sentence. Given a query request on a particular subject (specified as entity or keyword), the CRS determines the snippet that has most occurrences of the subject entity/keyword. 
     Result Aggregation and Ranking: 
     Sometimes, what was said by a speaker could be quoted in different documents. When retrieving quotes, the CRS applies an aggregation process to detect duplicate quotes by computing the similarity between each pair of quotes. 
     The quotes are then ranked by a combination of the following factors:
         1. publication date. The CRS prefers quotes with fresher date.   2. Number of duplicates. Usually, important or significant quotes are repeated more often.   3. Credibility of the source, e.g., articles from major newspapers have higher credibility than less known blogs.       

     Users can choose to sort the results by their default rank or purely by date. 
     Example Quotation Recommendation API 
     A. Getting Quotations 
     1. Description 
     Returns quotations made about a topic in the Evri corpus of news, blog and other web content. In addition, quotations made by a specific person may be returned. 
     2. Usage 
     The API can be invoked with a request of the following form: quotations/[about]?[inputParameters]&amp;speaker=SPEAKER&amp;[inputParameters] 
     where SPEAKER is the URI, or href, of a person and applicable inputParameters include: facet, entityURI, includeDomains, excludeDomains, includeDates, includeMatchedLocations, and callback. 
     3. Examples 
     Quotations by a person about anything: 
     http://api.evri.com/v1/quotations?speaker=/person/barack-obama-0x16f69&amp;appld=evri.com-restdoc 
     Quotations by anyone about a specific entity: http://api.evri.com/v1/quotations/about?entityURI=/location/united-states-0x2ae4b&amp;appld=evri.com-restdoc 
     Quotations by anyone about a facet: 
     http://api.evri.com/v1/quotations/about?fact=politician&amp;appld.evri.com-restdoc 
     Quotations by a person about an entity: http://api.evri.com/v1/quotations/about?entityURI=/person/george-w.-bush-0x1beeb&amp;speaker=/person/barack-obama-0x16f69&amp;appld=evri.com-restdoc 
     Quotations by a person about any entity of a facet: http://api.evri.com/v1/quotations/about?facet.politician&amp;speaker=/person/barack-obama-0x16f69&amp;appld.evri.com-restdoc 
     B. Input Parameters 
     The following parameters affect output results. Seethe usage section for each resource to assess applicability. 
                                 TABLE 3               Input   Description   Values   Default                  text   Text containing   String of natural   None           natural language   language text.               For articles, title               followed by 2               line breaks and               the article body.       queryToken   Token   String returned   None           encapsulating   as a part of an           trigger text   entity network           context           information       articleSnippetLength   Desired length of   1 . . . N   195           snippets in           characters       includeMatchedLocations   True if matched   1 of: 0, 1, false,   False           locations are   true           desired       entityURI   A unique   An entityURI   None           identifier to an           entity.       facet   A unique   A valid entity   None           identifier to a   facet           facet.       type   A unique   1 or more of:   Article           identifier to a   article, video,           media type   image           unless otherwise           specified in a           particular           resource.       startId   Sets id for start   0 . . . N    0           index       resultsPerPage   Number of   0 . . . N    10           desired results           per page       includeDomains   Constrain article   1 or more   All           results to   domains comma   domains           specified   separated           domains       excludeDomains   Limit articles to   1 or more   None           domains not   domains comma           included   separated       includeDates   Limit articles to   1 to 3 dates   None           dates listed   comma               separated in               YYYYMMDD               format       callback   Response   Any string   None           applies JSONP           format using           callback of the           specified name       appId   Unique   A string of the   None           application   form           identifier   \[hostname\]-               \[app name\]                    
Example Entity Types
 
     The following Table defines several example entity types in an example embodiment. Other embodiments may incorporate different types. 
                             TABLE 4                          Person           Organization           Location           Concept           Event           Product           Condition           Organism           Substance                        
Example Facets
 
     The following Table defines several example facets in an example embodiment. Other embodiments may incorporate different facets. 
     
       
         
           
               
             
               
                 TABLE 5 
               
               
                   
               
             
            
               
                 PERSON actor Evri/Person/Entertainment/Actor 
               
               
                 PERSON animator Evri/Person/Entertainment/Animator 
               
               
                 PERSON cinematographer Evri/Person/Entertainment/Cinematographer 
               
               
                 PERSON comedian Evri/Person/Entertainment/Comedian 
               
               
                 PERSON fashion_designer Evri/Person/Entertainment/Fashion_Designer 
               
               
                 PERSON musician Evri/Person/Entertainment/Musician 
               
               
                 PERSON composer Evri/Person/Entertainment/Musician/Composer 
               
               
                 PERSON producer Evri/Person/Entertainment/Producer 
               
               
                 PERSON director Evri/Person/Entertainment/Director 
               
               
                 PERSON radio_personality Evri/Person/Entertainment/Radio_Personality 
               
               
                 PERSON television_personality Evri/Person/Entertainment/Television_Personality 
               
               
                 PERSON author Evri/Person/Entertainment/Author 
               
               
                 PERSON model Evri/Person/Entertainment/Model 
               
               
                 PERSON screenwriter Evri/Person/Entertainment/Screenwriter 
               
               
                 PERSON playwright Evri/Person/Entertainment/Playwright 
               
               
                 PERSON conductor Evri/Person/Entertainment/Conductor 
               
               
                 PRODUCT film Evri/Product/Entertainment/Movie 
               
               
                 PRODUCT television_show Evri/Product/Entertainment/Television_Show 
               
               
                 PRODUCT album Evri/Product/Entertainment/Album 
               
               
                 PRODUCT musical Evri/Product/Entertainment/Musical 
               
               
                 PRODUCT book Evri/Product/Entertainment/Book 
               
               
                 PRODUCT newspaper Evri/Product/Publication 
               
               
                 PERSON politician Evri/Person/Politics/Politician 
               
               
                 PERSON cabinet_member Evri/Person/Politics/Cabinet_Member 
               
               
                 PERSON government_person Evri/Person/Politics/Government_Person 
               
               
                 PERSON political_party_leader Evri/Person/Politics/Political_Party_Leader 
               
               
                 PERSON judge Evri/Person/Politics/Judge 
               
               
                 PERSON country_leader Evri/Person/Politics/Politician/World_Leader 
               
               
                 PERSON joint_chiefs_of_staff 
               
               
                 Evri/Person/Politics/Politician/Joint_Chiefs_of_Staff 
               
               
                 PERSON white_house_staff Evri/Person/Politics/White_House_Staff 
               
               
                 PERSON activist Evri/Person/Politics/Activist 
               
               
                 PERSON lobbyist Evri/Person/Politics/Lobbyist 
               
               
                 PERSON ambassador Evri/Person/Politics/Ambassador 
               
               
                 PERSON analyst Evri/Person/Analyst 
               
               
                 PERSON journalist Evri/Person/Journalist 
               
               
                 PERSON blogger Evri/Person/Blogger 
               
               
                 ORGANIZATION band Evri/Organization/Entertainment/Band 
               
               
                 ORGANIZATION political_party Evri/Organization/Politics/Political_Party 
               
               
                 ORGANIZATION advocacy_group Evri/Organization/Politics/Advocacy_Group 
               
               
                 EVENT film_award_ceremony Evri/Event/Entertainment/Film_Award_Ceremony 
               
               
                 EVENT music_award_ceremony Evri/Event/Entertainment/Music_Award_Ceremony 
               
               
                 EVENT television_award_ceremony 
               
               
                 Evri/Event/Entertainment/Television_Award_Ceremony 
               
               
                 EVENT court_case Evri/Event/Politics/Court_Case 
               
               
                 ORGANIZATION television_network 
               
               
                 Evri/Organization/Entertainment/Company/Television_Network 
               
               
                 ORGANIZATION music_production_company 
               
               
                 Evri/Organization/Entertainment/Company/Music_Production_Company 
               
               
                 ORGANIZATION film_production_company 
               
               
                 Evri/Organization/Entertainment/Company/Film_Production_Company 
               
               
                 LOCATION congressional_district Evri/Location/Politics/Congressional_District 
               
               
                 LOCATION military_base Evri/Location/Politics/Military_Base 
               
               
                 ORGANIZATION congressional_committee 
               
               
                 Evri/Organization/Politics/Congressional_Committee 
               
               
                 ORGANIZATION international_organization 
               
               
                 Evri/Organization/Politics/International_Organization 
               
               
                 ORGANIZATION government_agency Evri/Organization/Politics/Government_Agency 
               
               
                 ORGANIZATION armed_force Evri/Organization/Politics/Armed_Force 
               
               
                 ORGANIZATION terrorist_organization 
               
               
                 Evri/Organization/Politics/Terrorist_Organization 
               
               
                 ORGANIZATION us_court Evri/Organization/Politics/US_Court 
               
               
                 ORGANIZATION cabinet_department Evri/Organization/Politics/Cabinet_Department 
               
               
                 LOCATION continent Evri/Location/Continent 
               
               
                 LOCATION geographic_region Evri/Location/Geographic_Region 
               
               
                 LOCATION country Evri/Location/Country 
               
               
                 LOCATION province Evri/Location/Province 
               
               
                 LOCATION state Evri/Location/State 
               
               
                 LOCATION city Evri/Location/City 
               
               
                 LOCATION us_city Evri/Location/City 
               
               
                 LOCATION neighborhood Evri/Location/Neighborhood 
               
               
                 LOCATION building Evri/Location/Structure/Building 
               
               
                 LOCATION island Evri/Location/Island 
               
               
                 LOCATION mountain Evri/Location/Mountain 
               
               
                 LOCATION body_of_water Evri/Location/Body_of_Water 
               
               
                 ORGANIZATION 
               
               
                 media_companyEvri/Organization/Entertainment/Company/Media_Company 
               
               
                 ORGANIZATION haute_couture_house 
               
               
                 Evri/Organization/Entertainment/Company/Haute_Couture_House 
               
               
                 ORGANIZATION publishing_company 
               
               
                 Evri/Organization/Entertainment/Company/Publishing_Company 
               
               
                 ORGANIZATION entertainment_company Evri/Organization/Entertainment/Company 
               
               
                 CONCEPT fictional_character Evri/Concept/Entertainment/Fictional_Character 
               
               
                 PERSON military_leader Evri/Person/Politics/Military_Leader 
               
               
                 PERSON military_person Evri/Person/Politics/Military_Person 
               
               
                 EVENT military_conflict Evri/Event/Politics/Military_Conflict 
               
               
                 PERSON terrorist Evri/Person/Politics/Terrorist 
               
               
                 PERSON criminal Evri/Person/Criminal 
               
               
                 PERSON explorer Evri/Person/Explorer 
               
               
                 PERSON inventor Evri/Person/Technology/Inventor 
               
               
                 PERSON lawyer Evri/Person/Lawyer 
               
               
                 PERSON artist Evri/Person/Artist 
               
               
                 PERSON painter Evri/Person/Artist/Painter 
               
               
                 PERSON revolutionary Evri/Person/Revolutionary 
               
               
                 PERSON spiritual_leader Evri/Person/Spiritual_Leader 
               
               
                 PERSON philosopher Evri/Person/Philosopher 
               
               
                 PERSON anthropologist Evri/Person/Anthropologist 
               
               
                 PERSON architect Evri/Person/Architect 
               
               
                 PERSON historian Evri/Person/Historian 
               
               
                 PERSON editor Evri/Person/Editor 
               
               
                 PERSON astronaut Evri/Person/Astronaut 
               
               
                 PERSON photographer Evri/Person/Photographer 
               
               
                 PERSON scientist Evri/Person/Technology/Scientist 
               
               
                 PERSON economist Evri/Person/Economist 
               
               
                 PERSON technology_person Evri/Person/Technology/Technology_Person 
               
               
                 PERSON business_person Evri/Person/Business/Business_Person 
               
               
                 PERSON stock_trader Evri/Person/Business/Business_Person/Stock_Trader 
               
               
                 PERSON first_lady Evri/Person/Politics/First_Lady 
               
               
                 ORGANIZATION us_state_legislature 
               
               
                 Evri/Organization/Politics/Legislative_Body/State_Legislature 
               
               
                 ORGANIZATION legislative_body Evri/Organization/Politics/Legislative_Body 
               
               
                 ORGANIZATION executive_body Evri/Organization/Politics/Executive_Body 
               
               
                 PERSON team_owner Evri/Person/Sports/Team_Owner 
               
               
                 PERSON sports_announcer Evri/Person/Sports/Sports_Announcer 
               
               
                 PERSON sports_executive Evri/Person/Sports/Sports_Executive 
               
               
                 PERSON olympic_medalist Evri/Person/Sports/Olympic_Medalist 
               
               
                 PERSON athlete Evri/Person/Sports/Athlete 
               
               
                 PERSON coach Evri/Person/Sports/Coach 
               
               
                 PERSON sports_official Evri/Person/Sports/Sports_Official 
               
               
                 PERSON motorcycle_driver Evri/Person/Sports/Athlete/Motorcycle_Rider 
               
               
                 PERSON race_car_driver Evri/Person/Sports/Athlete/Race_car_Driver 
               
               
                 ORGANIZATION auto_racing_team Evri/Organization/Sports/Auto_Racing_Team 
               
               
                 PERSON baseball_player Evri/Person/Sports/Athlete/Baseball_Player 
               
               
                 ORGANIZATION baseball_team Evri/Organization/Sports/Baseball_Team 
               
               
                 PERSON basketball_player Evri/Person/Sports/Athlete/Basketball_Player 
               
               
                 ORGANIZATION basketball_team Evri/Organization/Sports/Basketball_Team 
               
               
                 PERSON football_player Evri/Person/Sports/Athlete/Football_Player 
               
               
                 ORGANIZATION football_team Evri/Organization/Sports/Football_Team 
               
               
                 PERSON hockey_player Evri/Person/Sports/Athlete/Hockey_Player 
               
               
                 ORGANIZATION hockey_team Evri/Organization/Sports/Hockey_Team 
               
               
                 PERSON soccer_player Evri/Person/Sports/Athlete/Soccer_Player 
               
               
                 ORGANIZATION soccer_team Evri/Organization/Sports/Soccer_Team 
               
               
                 ORGANIZATION sports_league Evri/Organization/Sports/Sports_League 
               
               
                 PERSON cricketer Evri/Person/Sports/Athlete/Cricketer 
               
               
                 ORGANIZATION cricket_team Evri/Organization/Sports/Cricket_Team 
               
               
                 PERSON cyclist Evri/Person/Sports/Athlete/Cyclist 
               
               
                 ORGANIZATION cycling_team Evri/Organization/Sports/Cycling_Team 
               
               
                 PERSON volleyball_player Evri/Person/Sports/Athlete/Volleyball_Player 
               
               
                 ORGANIZATION volleyball_team Evri/Organization/Sports/Volleyball_Team 
               
               
                 PERSON rugby_player Evri/Person/Sports/Athlete/Rugby_Player 
               
               
                 ORGANIZATION rugby_team Evri/Organization/Sports/Rugby_Team 
               
               
                 PERSON boxer Evri/Person/Sports/Athlete/Boxer 
               
               
                 PERSON diver Evri/Person/Sports/Athlete/Diver 
               
               
                 PERSON golfer Evri/Person/Sports/Athlete/Golfer 
               
               
                 PERSON gymnast Evri/Person/Sports/Athlete/Gymnast 
               
               
                 PERSON figure_skater Evri/Person/Sports/Athlete/Figure_Skater 
               
               
                 PERSON horse_racing_jockey Evri/Person/Sports/Athlete/Horse_Racing_Jockey 
               
               
                 PERSON lacrosse_player Evri/Person/Sports/Athlete/Lacrosse_Player 
               
               
                 ORGANIZATION lacrosse_team Evri/Organization/Sports/Lacrosse_Team 
               
               
                 PERSON rower Evri/Person/Sports/Athlete/Rower 
               
               
                 PERSON swimmer Evri/Person/Sports/Athlete/Swimmer 
               
               
                 PERSON tennis_player Evri/Person/Sports/Athlete/Tennis_Player 
               
               
                 PERSON track_and_field_athlete Evri/Person/Sports/Athlete/Track_and_Field_Athlete 
               
               
                 PERSON wrestler Evri/Person/Sports/Athlete/Wrestler 
               
               
                 PERSON triathlete Evri/Person/Sports/Athlete/Triathlete 
               
               
                 EVENT sports_competition Evri/Event/Sports/Sports_Event/Sporting_Competition 
               
               
                 EVENT sports_event Evri/Event/Sports/Sports_Event 
               
               
                 EVENT olympic_sport Evri/Event/Sports/Olympic_Sports 
               
               
                 EVENT election Evri/Event/Politics/Election 
               
               
                 LOCATION sports_venue Evri/Location/Sports/Sports_Venue 
               
               
                 ORGANIZATION sports_division Evri/Organization/Sports/Sports_Division 
               
               
                 ORGANIZATION sports_event_promotion_company 
               
               
                 Evri/Organization/Sports/Sports_Event_Promotion_Company 
               
               
                 ORGANIZATION sports_organization Evri/Organization/Sports/Sports_Organization 
               
               
                 ORGANIZATION company Evri/Organization/Business/Company 
               
               
                 ORGANIZATION news_agency Evri/Organization/Business/Company/News_Agency 
               
               
                 PRODUCT cell_phone Evri/Product/Technology/Cell_Phone 
               
               
                 PRODUCT computer Evri/Product/Technology/Computer 
               
               
                 PRODUCT software Evri/Product/Technology/Software 
               
               
                 PRODUCT video_game Evri/Product/Technology/Software/Video_Game 
               
               
                 PRODUCT video_game_console Evri/Product/Technology/Video_Game_Console 
               
               
                 PRODUCT media_player Evri/Product/Technology/Media_player 
               
               
                 ORGANIZATION website Evri/Organization/Technology/Website 
               
               
                 ORGANIZATION technology_company Evri/Organization/Technology/Company 
               
               
                 PRODUCT magazine Evri/Product/Publication 
               
               
                 ORGANIZATION financial_services_company 
               
               
                 Evri/Organization/Business/Company/Financial_Services_Company 
               
               
                 ORGANIZATION radio_network 
               
               
                 Evri/Organization/Entertainment/Company/Radio_Network 
               
               
                 ORGANIZATION futures_exchange Evri/Organization/Business/Futures_Exchange 
               
               
                 ORGANIZATION stock_exchange Evri/Organization/Business/Stock_Exchange 
               
               
                 ORGANIZATION government_sponsored_enterprise 
               
               
                 Evri/Organization/Politics/Government_Sponsored_Enterprise 
               
               
                 ORGANIZATION political_organization Evri/Organization/Politics/Political_organization 
               
               
                 ORGANIZATION labor_union Evri/Organization/Politics/Labor_Union 
               
               
                 ORGANIZATION nonprofit_corporation 
               
               
                 Evri/Organization/Business/Company/Nonprofit_Corporation 
               
               
                 ORGANIZATION nonprofit_organization Evri/Organization/Nonprofit_Organization 
               
               
                 ORGANIZATION national_laboratory Evri/Organization/Politics/National_Laboratory 
               
               
                 ORGANIZATION unified_combatant_commands 
               
               
                 Evri/Organization/Politics/Unified_Combatant_Commands 
               
               
                 ORGANIZATION research_institute Evri/Organization/Research_Institute 
               
               
                 CONCEPT stock_market_index Evri/Concept/Business/Stock_Market_Index 
               
               
                 PERSON business_executive Evri/Person/Business/Business_Person/Business_Executive 
               
               
                 PERSON corporate_director Evri/Person/Business/Business_Person/Corporate_Director 
               
               
                 PERSON banker Evri/Person/Business/Business_Person/Banker 
               
               
                 PERSON publisher Evri/Person/Business/Business_Person/Publisher 
               
               
                 PERSON us_politician Evri/Person/Politics/U.S._Politician 
               
               
                 PERSON nobel_laureate Evri/Person/Nobel_Laureate 
               
               
                 PERSON chemist Evri/Person/Chemist 
               
               
                 PERSON physicist Evri/Person/Physicist 
               
               
                 ORGANIZATION business_organization Evri/Organization/Business/Business_Organization 
               
               
                 ORGANIZATION consumer_organization 
               
               
                 Evri/Organization/Business/Consumer_Organization 
               
               
                 ORGANIZATION professional_association 
               
               
                 Evri/Organization/Business/Professional_Association 
               
               
                 PERSON investor Evri/Person/Business/Business_Person/Investor 
               
               
                 PERSON financier Evri/Person/Business/Business_Person/Financier 
               
               
                 PERSON money_manager Evri/Person/Business/Business_Person/Money_Manager 
               
               
                 ORGANIZATION aerospace_company 
               
               
                 Evri/Organization/Business/Company/Aerospace_Company 
               
               
                 ORGANIZATION advertising_agency 
               
               
                 Evri/Organization/Business/Company/Advertising_Company 
               
               
                 ORGANIZATION agriculture_company 
               
               
                 Evri/Organization/Business/Company/Agriculture_Company 
               
               
                 ORGANIZATION airline Evri/Organization/Business/Company/Airline 
               
               
                 ORGANIZATION architecture_firm 
               
               
                 Evri/Organization/Business/Company/Architecture_Firm 
               
               
                 ORGANIZATION automotive_company 
               
               
                 Evri/Organization/Business/Company/Automotive_Company 
               
               
                 ORGANIZATION chemical_company 
               
               
                 Evri/Organization/Business/Company/Chemical_Company 
               
               
                 ORGANIZATION clothing_company 
               
               
                 Evri/Organization/Business/Company/Clothing_Company 
               
               
                 ORGANIZATION consulting_company 
               
               
                 Evri/Organization/Business/Company/Consulting_Company 
               
               
                 ORGANIZATION cosmetics_company 
               
               
                 Evri/Organization/Business/Company/Cosmetics_Company 
               
               
                 ORGANIZATION defense_company 
               
               
                 Evri/Organization/Business/Company/Defense_Company 
               
               
                 ORGANIZATION distribution_company 
               
               
                 Evri/Organization/Business/Company/Distribution_Company 
               
               
                 ORGANIZATION gaming_company 
               
               
                 Evri/Organization/Business/Company/Gaming_Company 
               
               
                 ORGANIZATION electronics_company 
               
               
                 Evri/Organization/Business/Company/Electronics_Company 
               
               
                 ORGANIZATION energy_company Evri/Organization/Business/Company/Energy_Company 
               
               
                 ORGANIZATION hospitality_company 
               
               
                 Evri/Organization/Business/Company/Hospitality_Company 
               
               
                 ORGANIZATION insurance_company 
               
               
                 Evri/Organization/Business/Company/Insurance_Company 
               
               
                 ORGANIZATION law_firm Evri/Organization/Business/Company/Law_Firm 
               
               
                 ORGANIZATION manufacturing_company 
               
               
                 Evri/Organization/Business/Company/Manufacturing_Company 
               
               
                 ORGANIZATION mining_company Evri/Organization/Business/Company/Mining_Company 
               
               
                 ORGANIZATION pharmaceutical_company 
               
               
                 Evri/Organization/Business/Company/Pharmaceutical_Company 
               
               
                 ORGANIZATION railway_company Evri/Organization/Business/Company/Railway 
               
               
                 ORGANIZATION real_estate_company 
               
               
                 Evri/Organization/Business/Company/Real_Estate_Company 
               
               
                 ORGANIZATION retailer Evri/Organization/Business/Company/Retailer 
               
               
                 ORGANIZATION shipping_company 
               
               
                 Evri/Organization/Business/Company/Shipping_Company 
               
               
                 ORGANIZATION software_company 
               
               
                 Evri/Organization/Technology/Company/Software_Company 
               
               
                 ORGANIZATION steel_company Evri/Organization/Business/Company/Steel_Company 
               
               
                 ORGANIZATION telecommunications_company 
               
               
                 Evri/Organization/Business/Company/Telecommunications_Company 
               
               
                 ORGANIZATION utilities_company 
               
               
                 Evri/Organization/Business/Company/Utilities_Company 
               
               
                 ORGANIZATION wholesaler Evri/Organization/Business/Company/Wholesaler 
               
               
                 ORGANIZATION television_production_company 
               
               
                 Evri/Organization/Entertainment/Company/Television_Production_Company 
               
               
                 ORGANIZATION food_company Evri/Organization/Business/Company/Food_Company 
               
               
                 ORGANIZATION beverage_company 
               
               
                 Evri/Organization/Business/Company/Food_Company/Beverage_Company 
               
               
                 ORGANIZATION restaurant 
               
               
                 Evri/Organization/Business/Company/Food_Company/Restaurant 
               
               
                 ORGANIZATION winery 
               
               
                 Evri/Organization/Business/Company/Food_Company/Beverage_Company 
               
               
                 EVENT film_festival Evri/Event/Entertainment/Film_Festival 
               
               
                 ORGANIZATION film_festival Evri/Event/Entertainment/Film_Festival 
               
               
                 PRODUCT anime Evri/Product/Entertainment/Anime 
               
               
                 PRODUCT aircraft Evri/Product/Aircraft 
               
               
                 PRODUCT military_aircraft Evri/Product/Aircraft/Military_Aircraft 
               
               
                 PRODUCT vehicle Evri/Product/Vehicle 
               
               
                 PRODUCT ballet Evri/Product/Entertainment/Ballet 
               
               
                 PRODUCT opera Evri/Product/Entertainment/Opera 
               
               
                 PRODUCT painting Evri/Product/Entertainment/Painting 
               
               
                 PRODUCT song Evri/Product/Entertainment/Single 
               
               
                 EVENT technology_conference Evri/Event/Technology/Technology_Conference 
               
               
                 CONCEPT legislation Evri/Concept/Politics/Legislation 
               
               
                 CONCEPT treaty Evri/Concept/Politics/Treaty 
               
               
                 ORGANIZATION trade_association Evri/Organization/Business/Trade_Association 
               
               
                 ORGANIZATION technology_organization 
               
               
                 Evri/Organization/Technology/Technology_Organization 
               
               
                 ORGANIZATION educational_institution Evri/Organization/Educational_Institution 
               
               
                 LOCATION museum Evri/Location/Structure/Building/Museum 
               
               
                 LOCATION religious_building Evri/Location/Structure/Building/Religious_Building 
               
               
                 PERSON astronomer Evri/Person/Astronomer 
               
               
                 PERSON mathematician Evri/Person/Mathematician 
               
               
                 PERSON academic Evri/Person/Academic 
               
               
                 PERSON dancer Evri/Person/Entertainment/Dancer 
               
               
                 PRODUCT play Evri/Product/Entertainment/Play 
               
               
                 LOCATION botanical_garden Evri/Location/Botanical_Garden 
               
               
                 LOCATION hospital Evri/Location/Health/Hospital 
               
               
                 PERSON psychiatrist Evri/Person/Health/Psychiatrist 
               
               
                 PERSON physician Evri/Person/Health/Physician 
               
               
                 PERSON nurse Evri/Person/Health/Nurse 
               
               
                 ORGANIZATION journalism_organization Evri/Organization/Journalism_Organization 
               
               
                 ORGANIZATION healthcare_company 
               
               
                 Evri/Organization/Business/Company/Healthcare_Company 
               
               
                 ORGANIZATION religious_organization Evri/Organization/Religious_Organization 
               
               
                 PERSON biologist Evri/Person/Scientist/Biologist 
               
               
                 PERSON biochemist Evri/Person/Scientist/Biochemist 
               
               
                 PERSON botanist Evri/Person/Scientist/Botanist 
               
               
                 PERSON poet Evri/Person/Entertainment/Author/Poet 
               
               
                 PERSON curler Evri/Person/Sports/Athlete/Curler 
               
               
                 PERSON biathlete Evri/Person/Sports/Athlete/Biathlete 
               
               
                 PERSON alpine_skier Evri/Person/Sports/Athlete/Alpine_Skier 
               
               
                 PERSON cross-country_skier Evri/Person/Sports/Athlete/Cross-country_Skier 
               
               
                 PERSON freestyle_skier Evri/Person/Sports/Athlete/Freestyle_Skier 
               
               
                 PERSON luger Evri/Person/Sports/Athlete/Luger 
               
               
                 PERSON nordic_combined_skier Evri/Person/Sports/Athlete/Nordic_Combined_Skier 
               
               
                 PERSON speed_skater Evri/Person/Sports/Athlete/Speed_Skater 
               
               
                 PERSON skeleton_racer Evri/Person/Sports/Athlete/Skeleton_Racer 
               
               
                 PERSON ski_jumper Evri/Person/Sports/Athlete/Ski_Jumper 
               
               
                 PERSON snowboarder Evri/Person/Sports/Athlete/Snowboarder 
               
               
                 PERSON bobsledder Evri/Person/Sports/Athlete/Bobsledder 
               
               
                 PERSON bodybuilder Evri/Person/Sports/Athlete/Bodybuilder 
               
               
                 PERSON equestrian Evri/Person/Sports/Athlete/Equestrian 
               
               
                 PERSON fencer Evri/Person/Sports/Athlete/Fencer 
               
               
                 PERSON hurler Evri/Person/Sports/Athlete/Hurler 
               
               
                 PERSON martial_artist Evri/Person/Sports/Athlete/Martial_Artist 
               
               
                 PERSON canoer Evri/Person/Sports/Athlete/Canoer 
               
               
                 LOCATION music_venue Evri/Location/Entertainment/Music_Venue 
               
               
                 LOCATION aquarium Evri/Location/Aquarium 
               
               
                 LOCATION cemetery Evri/Location/Cemetery 
               
               
                 LOCATION national_park Evri/Location/National_Park 
               
               
                 LOCATION volcano Evri/Location/Volcano 
               
               
                 LOCATION zoo Evri/Location/Zoo 
               
               
                 LOCATION structure Evri/Location/Structure 
               
               
                 LOCATION airport Evri/Location/Structure/Airport 
               
               
                 LOCATION bridge Evri/Location/Structure/Bridge 
               
               
                 LOCATION hotel Evri/Location/Structure/Hotel 
               
               
                 LOCATION palace Evri/Location/Structure/Palace 
               
               
                 LOCATION monument Evri/Location/Structure/Monument 
               
               
                 LOCATION street Evri/Location/Street 
               
               
                 LOCATION amusement_park Evri/Location/Amusement_Park 
               
               
                 LOCATION unitary_authority Evri/Location/Unitary_Authority 
               
               
                 PRODUCT drug_brand Evri/Product/Health/Drug_Brand 
               
               
                 PRODUCT weapon Evri/Product/Weapon 
               
               
                 PRODUCT missile_system Evri/Product/Weapon/Missile_System 
               
               
                 PRODUCT firearm Evri/Product/Weapon/Firearm 
               
               
                 PRODUCT artillery Evri/Product/Weapon/Artillery 
               
               
                 PRODUCT anti-aircraft_weapon Evri/Product/Weapon/Anti-aircraft_Weapon 
               
               
                 PRODUCT anti-tank_weapon Evri/Product/Weapon/Anti-tank_Weapon 
               
               
                 PRODUCT biological_weapon Evri/Product/Weapon/Biological_Weapon 
               
               
                 PRODUCT chemical_weapon Evri/Product/Weapon/Chemical_Weapon 
               
               
                 CHEMICAL chemical_weapon Evri/Product/Weapon/Chemical_Weapon 
               
               
                 SUBSTANCE chemical_weapon Evri/Product/Weapon/Chemical_Weapon 
               
               
                 PRODUCT explosive Evri/Product/Weapon/Explosive 
               
               
                 PRODUCT weapons_launcher Evri/Product/Weapon/Weapons_Launcher 
               
               
                 PERSON chess_player Evri/Person/Chess_Player 
               
               
                 PERSON sculptor Evri/Person/Artist/Sculptor 
               
               
                 PRODUCT game Evri/Product/Game 
               
               
                 ORGANIZATION theater_company 
               
               
                 Evri/Organization/Entertainment/Company/Theater_Company 
               
               
                 PERSON badminton_player Evri/Person/Sports/Athlete/Badminton_Player 
               
               
                 PRODUCT naval_ship Evri/Product/Watercraft/Naval_Ship 
               
               
                 PRODUCT battleship Evri/Product/Watercraft/Naval_Ship/Battleship 
               
               
                 PRODUCT cruiser Evri/Product/Watercraft/Naval_Ship/Cruiser 
               
               
                 PRODUCT aircraft_carrier Evri/Product/Watercraft/Naval_Ship/Aircraft_Carrier 
               
               
                 PRODUCT destroyer Evri/Product/Watercraft/Naval_Ship/Destroyer 
               
               
                 PRODUCT frigate Evri/Product/Watercraft/Naval_Ship/Frigate 
               
               
                 PRODUCT submarine Evri/Product/Watercraft/Naval_Ship/Submarine 
               
               
                 PRODUCT cruise_ship Evri/Product/Watercraft/Cruise_Ship 
               
               
                 PRODUCT yacht Evri/Product/Watercraft/Yacht 
               
               
                 PRODUCT ocean_liner Evri/Product/Watercraft/Ocean_Liner 
               
               
                 LOCATION county Evri/Location/County 
               
               
                 PRODUCT symphony Evri/Product/Entertainment/Symphony 
               
               
                 ORGANIZATION television_station 
               
               
                 Evri/Organization/Entertainment/Company/Television_Station 
               
               
                 ORGANIZATION radio_station Evri/Organization/Entertainment/Company/Radio_Station 
               
               
                 CONCEPT constitutional_amendment Evri/Concept/Politics/Constitutional_Amendment 
               
               
                 PERSON australian_rules_footballer 
               
               
                 Evri/Person/Sports/Athlete/Australian_Rules_Footballer 
               
               
                 ORGANIZATION australian_rules_football_team 
               
               
                 Evri/Organization/Sports/Australian_Rules_Football_Team 
               
               
                 ORGANIZATION criminal_organization Evri/Organization/Criminal_Organization 
               
               
                 PERSON poker_player Evri/Person/Poker_Player 
               
               
                 PERSON bowler Evri/Person/Sports/Athlete/Bowler 
               
               
                 PERSON yacht_racer Evri/Person/Sports/Athlete/Yacht_Racer 
               
               
                 PERSON water_polo_player Evri/Person/Sports/Athlete/Water_Polo_Player 
               
               
                 PERSON field_hockey_player Evri/Person/Sports/Athlete/Field_Hockey_Player 
               
               
                 PERSON skateboarder Evri/Person/Sports/Athlete/Skateboarder 
               
               
                 PERSON polo_player Evri/Person/Sports/Athlete/Polo_Player 
               
               
                 PERSON gaelic_footballer Evri/Person/Sports/Athlete/Gaelic_Footballer 
               
               
                 PRODUCT programming_language Evri/Product/Technology/Programming_Language 
               
               
                 PERSON engineer Evri/Person/Technology/Engineer 
               
               
                 EVENT cybercrime Evri/Event/Technology/Cybercrime 
               
               
                 EVENT criminal_act Evri/Event/Criminal_Act 
               
               
                 PERSON critic Evri/Person/Critic 
               
               
                 PERSON pool_player Evri/Person/Pool_Player 
               
               
                 PERSON snooker_player Evri/Person/Snooker_Player 
               
               
                 PERSON competitive_eater Evri/Person/Competitive_Eater 
               
               
                 PRODUCT data_storage_medium Evri/Product/Technology/Data_Storage_Medium 
               
               
                 PRODUCT data_storage_device Evri/Product/Technology/Data_Storage_Device 
               
               
                 PERSON mountain_climber Evri/Person/Mountain_Climber 
               
               
                 PERSON aviator Evri/Person/Aviator 
               
               
                 ORGANIZATION cooperative Evri/Organization/Cooperative 
               
               
                 CONCEPT copyright_license Evri/Concept/Copyright_License 
               
               
                 EVENT observance Evri/Event/Observance 
               
               
                 PERSON outdoor_sportsperson Evri/Person/Sports/Outdoor_Sportsperson 
               
               
                 PERSON rodeo_performer Evri/Person/Sports/Rodeo_Performer 
               
               
                 PERSON sports_shooter Evri/Person/Sports/Athlete/Sports_Shooter 
               
               
                 CONCEPT award Evri/Concept/Award 
               
               
                 CONCEPT entertainment_series Evri/Concept/Entertainment/Entertainment_Series 
               
               
                 PERSON chef Evri/Person/Chef 
               
               
                 PERSON cartoonist Evri/Person/Entertainment/Cartoonist 
               
               
                 PERSON comics_creator Evri/Person/Entertainment/Comics_Creator 
               
               
                 PERSON nobility Evri/Person/Nobility 
               
               
                 PERSON porn_star Evri/Person/Porn_Star 
               
               
                 PERSON archaeologist Evri/Person/Scientist/Archaeologist 
               
               
                 PERSON paleontologist Evri/Person/Scientist/Paleontologist 
               
               
                 PERSON victim_of_crime Evri/Person/Victim_of_Crime 
               
               
                 LOCATION region Evri/Location/Region 
               
               
                 PERSON linguist Evri/Person/Linguist 
               
               
                 PERSON librarian Evri/Person/Librarian 
               
               
                 PERSON bridge_player Evri/Person/Bridge_Player 
               
               
                 PERSON choreographer Evri/Person/Entertainment/Choreographer 
               
               
                 PRODUCT camera Evri/Product/Technology/Camera 
               
               
                 PRODUCT publication Evri/Product/Publication 
               
               
                 PRODUCT comic Evri/Product/Entertainment/Comic 
               
               
                 PRODUCT short_story Evri/Product/Entertainment/Short_Story 
               
               
                 ORGANIZATION irregular_military_organization 
               
               
                 Evri/Organization/Irregular_Military_Organization 
               
               
                 SUBSTANCE chemical_element Evri/Substance/Chemical_Element 
               
               
                 SUBSTANCE alkaloid Evri/Substance/Organic_Compound/Alkaloid 
               
               
                 SUBSTANCE glycoside Evri/Substance/Glycoside 
               
               
                 SUBSTANCE amino_acid Evri/Substance/Amino_Acid 
               
               
                 SUBSTANCE protein Evri/Substance/Protein 
               
               
                 SUBSTANCE enzyme Evri/Substance/Enzyme 
               
               
                 SUBSTANCE hormone Evri/Substance/Hormone 
               
               
                 SUBSTANCE hydrocarbon Evri/Substance/Organic_Compound/Hydrocarbon 
               
               
                 SUBSTANCE inorganic_compound Evri/Substance/Inorganic_Compound 
               
               
                 SUBSTANCE lipid Evri/Substance/Organic_Compound/Lipid 
               
               
                 SUBSTANCE steroid Evri/Substance/Organic_Compound/Lipid/Steroid 
               
               
                 SUBSTANCE molecule Evri/Substance/Molecule 
               
               
                 SUBSTANCE polymer Evri/Substance/Molecule/Polymer 
               
               
                 SUBSTANCE terpene Evri/Substance/Organic_Compound/Terpene 
               
               
                 SUBSTANCE toxin Evri/Substance/Toxin 
               
               
                 SUBSTANCE antibiotic Evri/Substance/Health/Antibiotic 
               
               
                 SUBSTANCE antioxidant Evri/Substance/Health/Antioxidant 
               
               
                 SUBSTANCE anti-inflammatory Evri/Substance/Health/Anti-inflammatory 
               
               
                 SUBSTANCE antiasthmatic_drug Evri/Substance/Health/Antiasthmatic_drug 
               
               
                 SUBSTANCE anticonvulsant Evri/Substance/Health/Anticonvulsant 
               
               
                 SUBSTANCE antihistamine Evri/Substance/Health/Antihistamine 
               
               
                 SUBSTANCE antihypertensive Evri/Substance/Health/Antihypertensive 
               
               
                 SUBSTANCE antiviral Evri/Substance/Health/Antiviral 
               
               
                 SUBSTANCE painkiller Evri/Substance/Health/Painkiller 
               
               
                 SUBSTANCE Painkiller Evri/Substance/Health/Painkiller 
               
               
                 SUBSTANCE anesthetic Evri/Substance/Health/Anesthetic 
               
               
                 SUBSTANCE antibody Evri/Substance/Antibody 
               
               
                 SUBSTANCE chemotherapeutic_drug Evri/Substance/Health/Chemotherapeutic 
               
               
                 SUBSTANCE anti-diabetic_drug Evri/Substance/Health/Anti-diabetic 
               
               
                 SUBSTANCE antianginal_drug Evri/Substance/Health/Antianginal 
               
               
                 SUBSTANCE muscle_relaxant Evri/Substance/Health/Muscle_relaxant 
               
               
                 SUBSTANCE hypolipidemic_drug Evri/Substance/Health/Hypolipidemic_Drug 
               
               
                 SUBSTANCE psychoactive_drug Evri/Substance/Health/Psychoactive_Drug 
               
               
                 SUBSTANCE vaccine Evri/Substance/Health/Vaccine 
               
               
                 SUBSTANCE gastrointestinal_drug Evri/Substance/Health/Gastrointestinal_Drug 
               
               
                 SUBSTANCE erectile_dysfunction_drug Evri/Substance/Health/Erectile_Dysfunction_Drug 
               
               
                 SUBSTANCE organometallic_compound 
               
               
                 Evri/Substance/Organic_Compound/Organometallic_Compound 
               
               
                 SUBSTANCE phenol Evri/Substance/Organic_Compound/Phenol 
               
               
                 SUBSTANCE ketone Evri/Substance/Organic_Compound/Ketone 
               
               
                 SUBSTANCE amide Evri/Substance/Organic_Compound/Amide 
               
               
                 SUBSTANCE ester Evri/Substance/Organic_Compound/Ester 
               
               
                 SUBSTANCE ether Evri/Substance/Organic_Compound/Ether 
               
               
                 SUBSTANCE heterocyclic_compound 
               
               
                 Evri/Substance/Organic_Compound/Heterocyclic_Compound 
               
               
                 SUBSTANCE organic_compound Evri/Substance/Organic_Compound 
               
               
                 SUBSTANCE carbohydrate Evri/Substance/Organic_Compound/Carbohydrate 
               
               
                 SUBSTANCE peptide Evri/Substance/Organic_Compound/Peptide 
               
               
                 SUBSTANCE organohalide Evri/Substance/Organic_Compound/Organohalide 
               
               
                 SUBSTANCE organosulfur_compound 
               
               
                 Evri/Substance/Organic_Compound/Organosulfur_Compound 
               
               
                 SUBSTANCE aromatic_compound 
               
               
                 Evri/Substance/Organic_Compound/Aromatic_Compound 
               
               
                 SUBSTANCE carboxylic_acid Evri/Substance/Organic_Compound/Carboxylic_Acid 
               
               
                 SUBSTANCE nucleic_acid Evri/Substance/Nucleic_Acid 
               
               
                 SUBSTANCE ion Evri/Substance/Ion 
               
               
                 ORGANISM cyanobacterium Evri/Organism/Health/Cyanobacterium 
               
               
                 ORGANISM gram-positive_bacterium Evri/Organism/Health/Gram-positive_Bacterium 
               
               
                 ORGANISM gram-negative_bacterium Evri/Organism/Health/Gram-negative_Bacterium 
               
               
                 ORGANISM acid-fast_bacterium Evri/Organism/Health/Acid-fast_Bacterium 
               
               
                 ORGANISM dna_virus Evri/Organism/Health/DNA_Virus 
               
               
                 ORGANISM rna_virus Evri/Organism/Health/RNA_Virus 
               
               
                 CONDITION symptom Evri/Condition/Health/Symptom 
               
               
                 CONDITION injury Evri/Condition/Health/Injury 
               
               
                 CONDITION inflammation Evri/Condition/Health/Inflammation 
               
               
                 CONDITION disease Evri/Condition/Health/Disease 
               
               
                 CONDITION cancer Evri/Condition/Health/Disease/Cancer 
               
               
                 ORGANISM medicinal_plant Evri/Organism/Health/Medicinal_Plant 
               
               
                 ORGANISM poisonous_plant Evri/Organism/Poisonous_Plant 
               
               
                 ORGANISM herb Evri/Organism/Herb 
               
               
                 CONCEPT medical_procedure Evri/Concept/Health/Medical_Procedure 
               
               
                 ORGANISM bacterium Evri/Organism/Health/Bacterium 
               
               
                 ORGANISM virus Evri/Organism/Health/Virus 
               
               
                 ORGANISM horse Evri/Organism/Horse 
               
               
                 PERSON fugitive Evri/Person/Fugitive 
               
               
                 ORGANIZATION military_unit Evri/Organization/Politics/Military_Unit 
               
               
                 ORGANIZATION law_enforcement_agency 
               
               
                 Evri/Organization/Politics/Law_Enforcement_Agency 
               
               
                 LOCATION golf_course Evri/Location/Golf_Course 
               
               
                 PERSON law_enforcement_agent Evri/Person/Politics/Law_Enforcement_Agent 
               
               
                 PERSON magician Evri/Person/Entertainment/Magician 
               
               
                 LOCATION educational_institution Evri/Organization/Educational_Institution 
               
               
                 CONCEPT social_program Evri/Concept/Politics/Social_Program 
               
               
                 EVENT international_conference Evri/Event/Politics/International_Conference 
               
               
                   
               
            
           
         
       
     
     All of the above U.S. patents, U.S. patent application publications, U.S. patent applications, foreign patents, foreign patent applications and non-patent publications referred to in this specification and/or listed in the Application Data Sheet, including but not limited to U.S. application Ser. No. 14/748,137 filed Jun. 23, 2015, entitled “NLP-BASED SYSTEMS AND METHODS FOR PROVIDING QUOTATIONS,” U.S. application Ser. No. 14/169,972 filed Jan. 31, 2014, entitled “NLP-BASED SYSTEMS AND METHODS FOR PROVIDING QUOTATIONS,” U.S. application Ser. No. 13/075,799 filed Mar. 30, 2011, entitled “NLP-BASED SYSTEMS AND METHODS FOR PROVIDING QUOTATIONS,” which claims the benefit of U.S. Patent Application No. 61/319,029 filed Mar. 30, 2010, entitled “NLP-BASED SYSTEMS AND METHODS FOR PROVIDING QUOTATIONS,” are incorporated herein by reference, in their entireties. 
     From the foregoing it will be appreciated that, although specific embodiments have been described herein for purposes of illustration, various modifications may be made without deviating from the spirit and scope of this disclosure. For example, the methods, techniques, and systems for content recommendation are applicable to other architectures. For example, instead of utilizing a Vector Space Model of document indexing, systems that are programmed to perform natural language processing (e.g., parts of speech tagging) can be employed. Also, the methods, techniques, and systems discussed herein are applicable to differing query languages, protocols, communication media (optical, wireless, cable, etc.) and devices (such as wireless handsets, mobile communications devices, electronic organizers, personal digital assistants, portable email machines, game machines, pagers, navigation devices such as GPS receivers, etc.).