Patent Publication Number: US-2022237235-A1

Title: Apparatus, system, and method for natural language processing

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 17/062,752, filed Oct. 5, 2020, which is a continuation of U.S. patent application Ser. No. 15/412,573, filed Jan. 23, 2017, now U.S. Pat. No. 10,795,944, which is a continuation of U.S. patent application Ser. No. 14/315,852, filed Jun. 26, 2014, now U.S. Pat. No. 9,552,350, which is a continuation of U.S. patent application Ser. No. 12/564,546, filed Sep. 22, 2009, now U.S. Pat. No. 8,943,094, which applications and patents are hereby incorporated by this reference in their entireties as if fully set forth herein. 
    
    
     BACKGROUND 
     Traditional information retrieval (IR) techniques typically rely on vocabulary term matching when searching through documents to identify documents for a response. Specifically, these IR techniques typically sort through large numbers of documents (a “knowledge base”) to identify those documents having vocabulary words and/or phrases that match a user&#39;s typed input. As a result, documents that are potentially valuable to the user, and relevant to their input, but that do not happen to have matching vocabulary words and/or phrases often are neither retrieved nor returned to the user. These are referred to as “missed” results. Conversely, documents that are not of value to the user, but that happen to have matching vocabulary words and/or phrases, are often retrieved and/or returned to the user. These are “false alarm” results. One aspect of an IR system is to reduce both the number of misses and the number of false alarms. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The same numbers are used throughout the drawings to reference like features. 
         FIG. 1  illustrates a block diagram of a natural language processing system. 
         FIG. 2  illustrates a diagram of one embodiment of an opportunistic context switching module. 
         FIG. 3  illustrates a diagram of one embodiment of a meta search module. 
         FIG. 4  illustrates a diagram of one embodiment of an auto-clarification module. 
         FIG. 5  illustrates one embodiment of a computing device which can be used in one embodiment of a system. 
     
    
    
     DETAILED DESCRIPTION 
     Various embodiments may be generally directed to information retrieval techniques for reducing both the number of misses and the number of false alarms when searching for documents in a knowledge base. Various embodiments may be generally directed to searching and retrieving documents based on a natural language input. Such natural language processing techniques provide specific answers to queries submitted by a user while avoiding the need for the user to sort through a set of search results such as might be provided by standard keyword-based searches. Some embodiments may be particularly directed to natural language processing techniques for improving the efficiency of accessing knowledge bases. 
     Knowledge bases provide a way in which a suite of intelligent applications, referred herein as ActiveAgent, can provide users with specific pre-defined responses. ActiveAgent can take the form of a virtual expert or agent that understands phrases inputted by a user and provides a response to the user. Knowledge bases can cover the entire scope of information that ActiveAgent uses, along with all of its capabilities. In at least some embodiments, knowledge base files themselves are written in a programming language known as FPML (Functional Presence Markup Language), a language similar to XML. This includes master FPML files, optional FPML files, lex files, and other auxiliary files, such as, input files, dictionary files, other text files to impact scoring, and contextual awareness files, for example. For additional information on FPML, the reader is referred to commonly owned U.S. patent application Ser. No. 10/839,425 titled “DATA DISAMBIGUATION SYSTEMS AND METHODS” and U.S. patent application Ser. No. 11/169,142 titled “METHODS AND SYSTEMS FOR ENFORCING NETWORK AND COMPUTER USE POLICY,” the disclosures of which are incorporated herein by reference in their entirety. 
     Various embodiments may be directed to “implicature” based natural language processing techniques for acquiring and maintaining concepts during an interactive session between a natural language processing system and the user for the purpose of resolving “ambiguous” input patterns provided in a natural language input. 
     Various embodiments may be directed to “goal” based natural language processing techniques for providing an abstract representation of a user&#39;s intention based on the content of the natural language input. 
     Various embodiments may be directed to “meta search” based natural language processing techniques for providing additional related information in response to a pattern provided in a natural language input submitted by the user. The m to search based natural language processing technique enables the natural language processing system to provide multiple responses to the user rather than providing only a single response to the user, e.g., the “meta search” provides expanded search results in addition to the response that is associated with a unit whose natural language input pattern was matched by the user&#39;s input pattern. 
     Various embodiments may be directed to “auto-clarification” based natural language processing techniques for resolving ambiguities that arise when the concepts found in a pattern in the natural language input submitted by the user are not sufficient for the natural language processing system to identify a single matching unit upon which to base a response to the user. 
     Various embodiments may comprise a combination of two or more of the above embodiments. Various other embodiments are described and claimed and may provide various advantages associated with natural language processing, which will be described with reference to specific embodiments below. 
       FIG. 1  illustrates a block diagram of a natural language processing system  100 . In the illustrated embodiment shown in  FIG. 1 , the natural language processing system  100  may include an interface device  102  and a natural language processor  104  coupled by a communication interface  125 . A user  106  interacts with the interface device  102  to submit the natural language input  108  to the natural language processor  104  via the communication interface  125 . In response to the natural language input  108 , the natural language processor  104  provides a response  110  to the user  106  via the interface device  102 . 
     In one embodiment, the interface device  102  may be implemented as a handheld portable device  112  such as a personal digital assistant (PDA), mobile telephone, sometimes referred to as a smart phone  114 , tablet personal computer  116  (PC), kiosk  118 , desktop computer  120 , or laptop computer  122 , or any combination thereof. Examples of smart phones  114  include, for example, Palm® products such as Palm® Treo® smart phones, Blackberry® smart phones, and the like. Although some embodiments of the interface device  102  may be described with a mobile or fixed computing device implemented as a smart phone, personal digital assistant, laptop, desktop computer by way of example, it may be appreciated that the embodiments are not limited in this context. For example, a mobile computing device may comprise, or be implemented as, any type of wireless device, mobile station, or portable computing device with a self-contained power source (e.g., battery) such as the laptop computer  122 , ultra-laptop computer, PDA, cellular telephone, combination cellular telephone/PDA, mobile unit, subscriber station, user terminal, portable computer, handheld computer  116 , palmtop computer, wearable computer, media player, pager, messaging device, data communication device, and so forth. A fixed computing device, for example, may be implemented as a desk top computer, workstation, client/server computer, and so forth. In one embodiment, the interface device  102  may be implemented as a conventional landline telephone for voice input and/or speech recognition applications, for example. 
     The interface device  102  may provide voice and/or data communications functionality in accordance with different types of cellular radiotelephone systems. Examples of cellular radiotelephone systems may include Code Division Multiple Access (CDMA) systems, Global System for Mobile Communications (GSM) systems, North American Digital Cellular (NADC) systems, Time Division Multiple Access (TDMA) systems, Extended-TDMA (E-TDMA) systems, Narrowband Advanced Mobile Phone Service (NAMPS) systems, 3G systems such as Wide-band CDMA (WCDMA), CDMA-2000, Universal Mobile Telephone System (UMTS) systems, and so forth. 
     The interface device  102  may be configured as a mobile computing device to provide voice and/or data communications functionality in accordance with different types of wireless network systems or protocols. Examples of suitable wireless network systems offering data communication services may include the Institute of Electrical and Electronics Engineers (IEEE) 802.xx series of protocols, such as the IEEE 802.1a/b/g/n series of standard protocols and variants (also referred to as “WiFi”), the IEEE 802.16 series of standard protocols and variants (also referred to as “WiMAX”), the IEEE 802.20 series of standard protocols and variants, and so forth. The mobile computing device also may utilize different types of shorter range wireless systems, such as a Bluetooth system operating in accordance with the Bluetooth Special Interest Group (SIG) series of protocols, including Bluetooth Specification versions v1.0, v1.1, v1.2, v1.0, v2.0 with Enhanced Data Rate (EDR), as well as one or more Bluetooth Profiles, and so forth. Other examples may include systems using infrared techniques or near-field communication techniques and protocols, such as electromagnetic induction (EMI) techniques. An example of EMI techniques may include passive or active radio-frequency identification (RFID) protocols and devices. 
     The interface device  102  is configured to couple to the communications interface  125 . The interface device  102  may form part of a wired communications system, a wireless communications system, or a combination of both. For example, the interface device  102  may be configured to communicate information over one or more types of wired communication links such as a wire, cable, bus, printed circuit board (PCB), Ethernet connection, peer-to-peer (P2P) connection, backplane, switch fabric, semiconductor material, twisted-pair wire, co-axial cable, fiber optic connection, and so forth. The interface device  102  may be arranged to communicate information over one or more types of wireless communication links such as a radio channel, satellite channel, television channel, broadcast channel infrared channel, radio-frequency (RF) channel, WiFi channel, a portion of the RF spectrum, and/or one or more licensed or license-free frequency bands. In wireless implementations, the interface device  102  may comprise one more interfaces and/or components for wireless communication such as one or more transmitters, receivers, transceivers, amplifiers, filters, control logic, wireless network interface cards (WNICs), antennas, and so forth. 
     In one embodiment, the communication interface  125  may be implemented as a leased line point-to-point connection between the interface device  102  and the natural language processor  104  over a Local Area Network (LAN). In another embodiment, the communication interface  125  may be implemented as a circuit switched dedicated circuit path created between the interface device  102  and the natural language processor  104 . In another embodiment, the communication interface  125  may be implemented as a packet switched device for transporting packets via a shared single point-to-point or point-to-multipoint link across a carrier internetwork. Variable length packets may be transmitted over Permanent Virtual Circuits (PVC) or Switched Virtual Circuits (SVC). In yet another embodiment, the communication interface  125  may be implemented as a cell relay similar to packet switching, but using fixed length cells instead of variable length packets. Data may be divided into fixed-length cells and then transported across virtual circuits. 
     In one embodiment, the natural language processor  104  may be implemented as a general purpose or dedicated computer system configured to execute a core of specific algorithms, functions, and/or software applications to provide natural language processing functionality. The natural language processor  104  may comprise a computer system, or network of computer systems, referred to herein as a language and response processor  124 , designated for executing (e.g., running) one or more than one specific natural language software application  126  to provide natural language processing in response to the natural language input  108  submitted by the user  106  via the interface device  102 . Each of the specific natural language software applications  126  may be representative of a particular kind of natural language processing algorithm. 
     In various embodiments, the natural language software applications  126  may include without limitation an implicature module  128  for acquiring and maintaining concepts during an interactive session for the purpose of resolving “ambiguous” input patterns, a meta search module  130  for providing additional related information in response to a user&#39;s input pattern, an auto-clarification module  132  for resolving ambiguities that arise when the concepts found in a user&#39;s input pattern are not sufficient for the system to identify a single matching unit, and an opportunistic context switching module  134  for providing an abstract representation of a user&#39;s intention when the user does not respond to a prompt with the information that was requested and instead asks a question that is unrelated to the current information retrieval goal. The opportunistic context switching module  134  is described more particularly in  FIG. 2 . Specific implementations of each of these software applications  126  are subsequently discussed in accordance with the described embodiments. 
     In one embodiment, the language and response processor  124  may comprise an information retrieval engine (IRE) component to retrieve and return relevant documents in the response  110  based on the natural language input  108  submitted by the user  106 . In one embodiment, the IRE may utilize concepts techniques, as described in commonly owned U.S. Provisional Patent Application Ser. No. 61/122,203, titled “LEVERAGING CONCEPTS WITH INFORMATION RETRIEVAL TECHNIQUES AND KNOWLEDGE BASES,” which is incorporated herein by reference in its entirety. The IRE may be implemented as a search engine designed to search for information on a variety of networks or knowledge bases  136 . The results of the search may be presented in the response  110  as a list commonly called search results. The information in the response  110  may comprise web pages, images, information, documents, and/or other types of files collectively referred throughout the remainder of this specification as “documents.” In various embodiments, the IRE may be maintained by human editors, may operate algorithmically, or may be implemented as a combination of algorithmic and human input. The knowledge base  136  may be contained within the natural language processor  104  or may be coupled thereto over one or more networks. 
     The natural language input  108  entered by the user  106  may comprise one or more than one phrase. The natural language processor  104  attributes zero or more concepts to a phrase within the natural language input  108  entered by the user  106 . The natural language processor  104  can index (i.e., build an index or indices) documents in the knowledge base  136  based on the respective concept(s) attributed to the phrase in the natural language input  108 . In this manner, the natural language processor  104  is able to relatively quickly provide the response  110  to the user  106  by querying the index and returning/retrieving any documents with one or more concepts matching those attributed to the phrase within the natural language input  108 . 
     In one embodiment, the knowledge base  136  may comprise a collection of documents (e.g., web pages, printer document format [PDF] files, images, information, and other types of files) that contain specific elements or components (e.g., pieces) of the information that the user  108  may wish to access. These individual elements or components of the information are referred to as the responses  110 . The knowledge base  136  may contain a very large number of responses  110 . 
     A unit  138  is a pairing of patterns of words, terms, concepts, or phrases provided in the natural language input  108  with a suitable response  110  from the knowledge base  136  that should trigger that response  110 . The unit  138  pairings associated with any given response  110  may comprise many patterns contained in the natural language input  108  that should elicit that response  110 . For example, the response  110  “Our savings accounts are free, but do require that you maintain a balance of $300,” from the natural language processor  104 , may be the appropriate response  110  for any one of the following patterns contained in the natural language input  108 :
         How much does it cost to have a savings account?   What&#39;s the price of a savings account?   $$ of a savings account?   Saving&#39;s accounts: cost?   Do I have to pay for savings accounts?   What are the restrictions of a savings account?   Is there a minimum balance I have to maintain to have a savings account?   How much is a savings account?       

     A concept  140  is a technique employed by the natural language processor  104  to return and/or retrieve more relevant documents in a response  110 . As previously discussed, the natural language processor  104  may employ techniques associated with leveraging concepts. In this context, the concept  140  may be defined as a breakdown of critical ideas contained in phrases in the natural language input  108 . Zero or more concepts  140  can be attributed to a phrase entered by the user  106  when the natural language input  108  is received by the natural language processor  104 . One or more concepts  140  can also be attributed to individual documents available to the natural language processor  104  for responding to the user&#39;s phrase in the natural language input  108 . The natural language processor  104  can index the documents (i.e., build an index or indices) based on the respective concept(s)  140  in order to respond relatively quickly to the phrase in the natural language input  108  submitted by the user  106 . The natural language processor  104  queries the index and returns and/or retrieves any documents having one or more concepts  140  matching those attributed to the phrase in the natural language input  108 . 
     A concept  140  may comprise various components. As previously discussed, the concept  140  may be defined as a breakdown of critical ideas. In at least some implementations, the concept  140  comprises patterns of one or more components. Although these components may vary based on specific implementations, a concept  140  may comprise a vocabulary component (“Vocab”), a helper term component (“Helper Term”), and/or a building block component (“Building Block”). As subsequently described, a concept  140  may comprise each of these components alone or in combination. Various examples of “Vocabs,” “Helper Terms,” and/or “Building Blocks” are described individually below. In addition, some concepts  140  also may comprise one or more wild cards (“Wild Cards”), also described below. A concept  140  is considered to be triggered (or “hit”) when a phrase in the natural language input  108  received by the natural language processor  104  completely matches at least one of the patterns associated with a concept  140 . 
     A vocabulary component of a concept  140  (e.g., Vocab) comprises a grouping or list of unambiguous synonyms and misspellings. The name of a particular grouping or list of synonyms and misspellings of a vocabulary component may be identified as a vocabulary term (“Vocab Term”). For convenience and clarity, Vocab Terms often end with the suffix “vocab.” The particular groupings of unambiguous synonyms and misspellings associated with the Vocab Terms: “AccountVocab,” “PriceVocab,” and “BankVocab” are described below as illustrative examples. 
     For example, the Vocab Term “AccountVocab” may comprise a list of the following particular groupings of unambiguous synonyms and misspellings of the word “account”:
         AccountVocab
           Account   Accounts   Accts   Account&#39;s   
               

     As another example, the Vocab Term “PriceVocab” may comprise a list of the following particular groupings of unambiguous synonyms and misspellings of the word “price”:
         PriceVocab
           Price   Prices   Prise   Prises   Cost   Costs   Cost&#39;s   
               

     In the PriceVocab example above, the word “cost” is included in the Vocab Term because a user  106  would most likely consider the vocabulary terms/words “price” and “cost” to be synonymous. 
     As a further example, the Vocab Term “BankVocab” may comprise a list of the following particular groupings of unambiguous synonyms and misspellings of the word “bank”:
         BankVocab
           Bank   Banks   Bank&#39;s   Lender   Lenders   Credit union   Credit Unions   
               

     In the BankVocab example above, the user  106  would most likely consider the vocabulary terms/words “bank,” “lender,” and “credit union” to be synonymous. 
     Vocabulary terms/words that do not have unambiguous synonyms but nevertheless function substantially in the same manner as vocabulary terms/words are referred to as Helper Terms. A typical Helper Term does not have an associated vocabulary component (Vocab) like a concept  140  does. Helper Terms consist primarily of conjunctions, such as, for example:
         and   is   for   the       

     Building Blocks are a list of either Vocab/Helper Terms or a list of concepts  140  that may be useful when categorized together. For example, the Building Block “Anatomy (Vocab Building Block)” may be defined using the following vocabulary terms, where each of these vocabulary terms would comprise a list of particular groupings of unambiguous synonyms and misspellings of various words associated with the word “anatomy”:
         armvocab   legvocab   headvocab   shouldervocab   feetvocab       

     Once the Vocab Terms are bundled together they can be used in a concept  140  pattern. The Anatomy Building Block also includes Vocab Terms, which include unambiguous synonyms and misspellings associated with the word “anatomy.” The following example illustrates the use of an Anatomy Building Block that contains five Vocab Terms and reduces the number of concept patterns from ten to two:
         * Anatomy (Building Block) surgeryvocab *   * brokenvocab myvocab Anatomy (Building Block) *       

     The following example of a Vocab Building Block named “Types of Accounts (concept Building Block)” or simply Accounts Building Block may be used to reduce the number of necessary concept patterns.
         Savings Accounts   Checking Accounts   Money Market Accounts   Investment Accounts   Mortgage Accounts       

     Wild Cards function as placeholders within Concepts for any random word or words. 
     Concepts  140  may be created or built through any suitable means and this can be performed manually, automatically, or any combination thereof. As noted above, a concept  140  is usually made up of components comprising patterns of Vocabs, Helper Terms, and Building Blocks (and occasionally Wild Cards) listed within the concept  140 . For example, the above concept Building Block “types of accounts” may be all or part of a pattern making up the concept “account types.” Additional examples of patterns that may make up a savings account concept, where the Helper Term “for” does not end with the suffix “vocab,” include:
         * savingsvocab accountvocab *   * accountvocab for savingsvocab *   * interestvocab bearingvocab accountvocab *
 
Additionally, patterns may include, without limitation, dictionary text files, tabular text data files, regular expressions, lex types and other constructs to impact scoring, and contextual awareness, for example.
       

     In concepts  140 , both order and proximity are important, both of which are optional when creating any given pattern. To select a particular order for a pattern of a concept, the pattern should specify a particular order (i.e., ordering) with respect to two or more of the pattern&#39;s Vocab, Helper Terms, and/or Building Blocks. For example, with respect to order, a pattern of a concept specifying the order “savings account” is different from the pattern of a concept specifying the order “account savings.” To select a particular proximity for a pattern of a concept  140 , the pattern should specify the proximity of two or more of the pattern&#39;s Vocab, Helper Terms, and/or Building Blocks. A pattern of a concept  140  specifying that the terms “savings” and “account” are to be positioned next to one another would be different from the pattern of a concept  140  with the phrase “savings in my account.” 
     It will be appreciated that for most patterns in the natural language input  108 , it is advantageous to specify both an order and a proximity for a pattern of a concept  140 . In the above example, a pattern of a concept  140  “Savings Account” in the natural language input  108  has a very different meaning than the patterns of concepts “Account Savings” and “Savings in my Account.” Concepts  140  also have their own associated test questions for the purposes of testing. Examples of test questions that the. user  06  may include in the natural language input  108  for the pattern of a concept  140  “Savings Account” may comprise:
         Do you have savings accounts at your bank?   What&#39;s a savings account?   Do you have any interest bearing accounts?       

     A unit  138 , among other features described herein, matches concepts  140  extracted from the natural language input  108 . A unit  138  is comprised of one or many individual units where each unit generates a single response  110 . The concept  140  patterns for an individual unit are specified with no regard to order. This improves the likelihood of a correct answer and limits the number of individual units  138  in the FPML knowledge base  136  and allows autoclarification. 
     It will be appreciated that a single natural language input  108  from the user  106  may not always be sufficient for the knowledge base  136  to provide a meaningful response  110 . In such cases, the natural language processor  104  may prompt the user  106  to provide additional information. This “back and forth” interactive dialog between the natural language processor  104  and the user  106  may continue until there is sufficient information collected from the user  106  to enable the natural language processor  104  to provide a suitable response  110 . 
     As previously discussed, various embodiments may be directed to “implicature” based natural language processing techniques for acquiring and maintaining concepts during an interactive session between the natural language processor  104  and the user  106  for the purpose of resolving “ambiguous” input patterns provided in the natural language input  108  by the user  106 . The implicature  128  module (e.g., software application) acquires and maintains the concepts  140  during an interactive session between the natural language processor  104  and the user  106  for the purpose of resolving “ambiguous” input patterns entered by the user  106 . For example, 
     Query 1: I would like a ticket to New York 
     Query 2: How much does it cost? 
     After processing Query 1, the natural language processor  104  stores in memory the word “ticket” as the value of the concept  140  “user&#39;s desire” so that when the system sees the word “it” in Query 2 it will be able to associate it to the word “ticket.” Such ambiguities often arise with the use of pronouns such as “he,” “she,” and “it,” and with other words/phrases as in “Tell me about the first one,” or “I&#39;ll take the second option.” 
     As another example, consider the following interaction between the user  106  and the natural language processor  104  that provides information about the United States (US) Army:
         User: What is AIT?   System: Advanced Individual Training is Army training specifically designed to maximize the potential of each soldier.   User: Where is it?   [The concept “AIT” is carried over]   System: Advanced Individual Training takes place all over the world and depends on the type of training.   User: What is BCT? [nothing carried over]   System: Basic Combat Training is where civilians learn to be soldiers. It is tough but rewarding work.   User: Where is AIT? [nothing carried over]   System: Advanced Individual Training takes place all over the world and depends on the type of training.   User: BCT?   [The concept “where” is carried over]   System: Basic Combat Training takes place in Alabama or Texas.       

     A goal  142  is an abstract representation of an intention of either the user  106  or the natural language processor  104 . For example, consider the fictional airlines “Next Airlines.” The user  106  may navigate to the Next Airlines&#39; web site with the intention (i.e., the “goal”) of booking air travel. In order to achieve the goal  142  (e.g., booking air travel), certain pieces of information are required. For example, if the goal  142  is to book air travel, it is necessary to obtain the departure city, the destination city, days of travel, and so on, from the user  106 . Thus the goal  142  has “slots” of information or goal variables  144  that must be filled in before the natural language processor  104  can provide the user  106  with the response  110 , thus completing the goal  142 . It will be appreciated that the “slots” referring to the goal variables  144  tracked during an interactive session are variable storage memory locations allocated by the natural language processor  104  as needed. 
     A goal  142  can extract multiple goal variables  144  from a single natural language input  108  or through multiple inputs. The order that the information is provided does not matter to the Goal  142 . In other words, a goal  142  is able to extract multiple goal variables  144  when the user  106  supplies more than one piece of information without regard to the ordering of information. For example, a goal  142  may extract departure city, destination city, and day of the week with a single user input  106  even when the sentences are structured differently, such as the sentences below:
         User: I would like to fly from Seattle to Spokane on Monday   User: On Monday, I would like to fly to Seattle from Spokane       

     If the user  106  does not provide, in a single natural language input  108  pattern, all of the slot information, e.g., goal variables  144 , needed to complete the goal  142 , then the natural language processor  104  will enter into, initiate, an interactive dialog with the user  106  to obtain the missing information and the prompts presented to the user  106  by the natural language processor  104  will be based on the empty slots, which represent goal variables  144  with unknown values. 
     A goal  142  may comprise a portion of the overall knowledge base  136 . When a goal  142  is active, the natural language processor  104  preferentially tries to complete the goal  142 , but does not exclude other goals or units  138 . The user  106  may be non-responsive to a prompt and instead ask for information more appropriately answered by another goal or by a unit  138 , in which case a tangential goal or unit is returned.  FIG. 2  illustrates a diagram  200  of one embodiment of an opportunistic context switching module  134  ( FIG. 1 ). In the illustrated embodiment, the opportunistic context switching module  134  handles the scenario where the user  106  does not respond to a prompt with the information that was requested by the natural language processor  104  and instead asks a question that is unrelated to the current active goal. The user  106  may initiate an interactive dialog with the natural language processor  104 , as previously discussed. The interactive dialog may result in session goals  202  comprising one or more primary goals  202   1  to  202   n , where n is an integer. Often times, the user  106  will not respond to the prompt from the language processor  104  with the information that was requested. Instead, the user  106  may ask a question that is unrelated to one of the primary goals  202   1-n  at hand at which time the opportunistic context switching module  134  initiates the tangential request and will delay activity related to the primary goal. As shown in the illustrated diagram  200 , by way of example and not limitation, the primary goal  202   2  is active and has captured two variables, variable 1 and variable 4, which are complete. The primary goal  202   2  has three unknown variables, variable 2 (in progress), variable 3 (unknown), and variable m (unknown). The primary goal  202   2  has prompted the user  106  for information related to variable 2. In this example, variable 2 is not yet determined because the user  106  responded to the prompt with an unrelated natural language input. Hence, variable 2 is labeled “unknown.” Since the user  106  is non-responsive to the prompt and instead asks for information more appropriately answered by another goal  142  or by a unit  138  ( FIG. 1 ), the opportunistic context switching module  134  redirects the dialog to the tangential goal or unit. Upon completion of the tangential request, the opportunistic context switching module  134  returns the user  106  to the primary goal  202   2 . 
     It will be appreciated by those skilled in the art that typical information retrieval engines are generally unable to process such tangent requests  208  or goals in general. The natural language processor  104  ( FIG. 1 ), however, is able to deal with such tangents by “shelving” the current goal  202   2 , and beginning to work on completing a new goal. Tangential requests may be initiated by either the user  106  or the natural language processor  104 . Once the new goal is complete, the natural language processor  104  will switch back to a previously uncompleted goal and will continue where it left off, trying to fill in missing slot information. Opportunistic context switching enables sophisticated interactive dialogs between the user  106  and the natural language processor  104  such as the following:
         System: How many tickets would you like to buy? (Current goal: book a flight)   User: Do I have to purchase a ticket for my infant? (Not a direct answer to the system&#39;s prompt.)   System: How old is the child? (New goal: determine age of child) User: 12 months old   System: Children under 24 months do not require a separate ticket. (New goal complete.)   System: How many tickets would you like to buy? (Back to original goal.)       

       FIG. 3  illustrates a diagram  300  of one embodiment of the meta search module  130  ( FIG. 1 ). In the illustrated embodiment, the meta search module  130  is for providing additional related information in response to an input pattern provided in the natural language input  108  by the user  106 . The meta search based natural language processing technique enables the natural language processor  104  to provide multiple responses, e.g., a primary response  310  and a related response  308 , which together form the response  110  back to the user  106  rather than providing only a single response to the user  106 . Accordingly, the response  110  that is associated with the unit  138  ( FIG. 1 ) whose input pattern was matched by the user&#39;s natural language input  108  pattern. The meta search based natural language processing technique allows the natural language processor  104  to provide additional related information in the response  110  to the natural language input  108  pattern submitted by the user  106  (e.g., Here is more information you may be interested in . . . ”). Without the meta search based natural language processing technique, the natural language processor  104  will provide only a single response  110  to the user—the response  110  that is associated with the unit  138  whose input pattern was matched by the user&#39;s natural language input  108  pattern. 
     Once the user  106  submits a natural language input  108 , the natural language processor  104  initiates a primary search  302  to search for concepts  140  ( FIG. 1 ) in an agent database  304 . The meta search based natural language processing technique then performs a secondary search  306  across all the units  138  ( FIG. 1 ) using only the “material” concepts that were found in the user&#39;s natural language input  108  pattern. This allows the natural language processor  104  to locate the units  138  which did not perfectly match the original natural language input  108  pattern, but which contains the important concepts  140  from the natural language input  108  pattern, thus allowing the natural language processor  104  to provide additional related responses  308  to the user  106 . The natural language processor  104  provides the additional related responses  308  information in response to the natural language input  108  pattern submitted by the user  106 , e.g., “here is more information you may be interested in . . . ” as previously discussed, the primary response  310  and the additional related responses  308  together form the response  110  back to the user  106 . 
       FIG. 4  illustrates a diagram  400  of one embodiment of the auto-clarification module  132  ( FIG. 1 ). In the illustrated embodiment, the auto-clarification module  132  is for resolving ambiguities that arise when the concepts  140  found in a natural language input  108  pattern submitted by the user  106  ( FIG. 1 ) are not sufficient for the natural language processor  104  to identify a single matching unit  138  ( FIG. 1 ) upon which to base a response  110  to the user  106 . Accordingly, as shown in FPML block  404 , multiple matching units  138   1  to  138   n  may be created. For example, assume that a first matching unit  138   1  “Unit 1” contains concepts  140  “A,” “G,” and “L,” and a second matching unit  138   2  “Unit 2” contains concepts  140  “A,” “G,” and “M.” If the natural language input  108  submitted by the user  106  provides only the concepts  140  “A” and “G,” the natural language processor  104  will prompt  402  the user  106  for clarification-either the concept  140  “L” or “M.” 
       FIG. 5  illustrates one embodiment of a computing device  500  which can be used in one embodiment of a system to implement the various described embodiments. The computing device  500  may be employed to implant one or more of the computing devices, such as the natural language processor  104  described above with reference to  FIGS. 1-4 , or any other suitably configured computing device. For the sake of clarity, the computing device  500  is illustrated and described here in the context of a single computing device. However, it is to be appreciated and understood that any number of suitably configured computing devices can be used to implement a described embodiment. For example, in at least some implementations, multiple communicatively linked computing devices are used. One or more of these devices can be communicatively linked in any suitable way such as via one or more networks. One or more networks can include, without limitation: the Internet, one or more local area networks (LANs), one or more wide area networks (WANs) or any combination thereof. 
     In this example, the computing device  500  comprises one or more processor circuits or processing units  502 , one or more memory circuits and/or storage circuit component(s)  504  and one or more input/output (I/O) circuit devices  506 . Additionally, the computing device  500  comprises a bus  508  that allows the various circuit components and devices to communicate with one another. The bus  508  represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. The bus  508  may comprise wired and/or wireless buses. 
     The processing unit  502  may be responsible for executing various software programs such as system programs, applications programs, and/or modules to provide computing and processing operations for the computing device  500 . The processing unit  502  may be responsible for performing various voice and data communications operations for the computing device  500  such as transmitting and receiving voice and data information over one or more wired or wireless communications channels. Although the processing unit  502  of the computing device  500  is shown in the context of a single processor architecture, it may be appreciated that the computing device  500  may use any suitable processor architecture and/or any suitable number of processors in accordance with the described embodiments. In one embodiment, the processing unit  502  may be implemented using a single integrated processor. 
     The processing unit  502  may be implemented as a host central processing unit (CPU) using any suitable processor circuit or logic device (circuit), such as a as a general purpose processor. The processing unit  502  also may be implemented as a chip multiprocessor (CMP), dedicated processor, embedded processor, media processor, input/output (I/O) processor, co-processor, microprocessor, controller, microcontroller, application specific integrated circuit (ASIC), field programmable gate array (FPGA), programmable logic device (PLD), or other processing device in accordance with the described embodiments. 
     As shown, the processing unit  502  may be coupled to the memory and/or storage component(s)  504  through the bus  508 . The memory bus  508  may comprise any suitable interface and/or bus architecture for allowing the processing unit  502  to access the memory and/or storage component(s)  504 . Although the memory and/or storage component(s)  504  may be shown as being separate from the processing unit  502  for purposes of illustration, it is worthy to note that in various embodiments some portion or the entire memory and/or storage component(s)  504  may be included on the same integrated circuit as the processing unit  502 . Alternatively, some portion or the entire memory and/or storage component(s)  504  may be disposed on an integrated circuit or other medium (e.g., hard disk drive) external to the integrated circuit of the processing unit  502 . In various embodiments, the computing device  500  may comprise an expansion slot to support a multimedia and/or memory card, for example. 
     The memory and/or storage component(s)  504  represent one or more computer-readable media. The memory and/or storage component(s)  504  may be implemented using any computer-readable media capable of storing data such as volatile or non-volatile memory, removable or non-removable memory, erasable or non-erasable memory, writeable or re-writeable memory, and so forth. The memory and/or storage component(s)  504  may comprise volatile media (e.g., random access memory (RAM)) and/or nonvolatile media (e.g., read only memory (ROM), Flash memory, optical disks, magnetic disks and the like). The memory and/or storage component(s)  504  may comprise fixed media (e.g., RAM, ROM, a fixed hard drive, etc.) as well as removable media (e.g., a Flash memory drive, a removable hard drive, an optical disk). Examples of computer-readable storage media may include, without limitation, RAM, dynamic RAM (DRAM), Double-Data-Rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM (SRAM), read-only memory (ROM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory (e.g., NOR or NAND flash memory), content addressable memory (CAM), polymer memory (e.g., ferroelectric polymer memory), phase-change memory, ovonic memory, ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or optical cards, or any other type of media suitable for storing information. 
     The one or more I/O devices  506  allow a user to enter commands and information to the computing device  500 , and also allow information to be presented to the user and/or other components or devices. Examples of input devices include a keyboard, a cursor control device (e.g., a mouse), a microphone, a scanner and the like. Examples of output devices include a display device (e.g., a monitor or projector, speakers, a printer, a network card). The computing device  500  may comprise an alphanumeric keypad coupled to the processing unit  502 . The keypad may comprise, for example, a QWERTY key layout and an integrated number dial pad. The computing device  500  may comprise a display coupled to the processing unit  502 . The display may comprise any suitable visual interface for displaying content to a user of the computing device  500 . In one embodiment, for example, the display may be implemented by a liquid crystal display (LCD) such as a touch-sensitive color (e.g., 76-bit color) thin-film transistor (TFT) LCD screen. The touch-sensitive LCD may be used with a stylus and/or a handwriting recognizer program. 
     The processing unit  502  may be arranged to provide processing or computing resources to the computing device  500 . For example, the processing unit  502  may be responsible for executing various software programs including system programs such as operating system (OS) and application programs. System programs generally may assist in the running of the computing device  500  and may be directly responsible for controlling, integrating, and managing the individual hardware components of the computer system. The OS may be implemented, for example, as a Microsoft® Windows OS, Symbian OS™, Embedix OS, Linux OS, Binary Run-time Environment for Wireless (BREW) OS, JavaOS, or other suitable OS in accordance with the described embodiments. The computing device  500  may comprise other system programs such as device drivers, programming tools, utility programs, software libraries, application programming interfaces (APIs), and so forth. 
     Various embodiments have been set forth which provide information retrieval techniques for reducing both the number of misses and the number of false alarms when searching for documents in a knowledge base. Various embodiments of language processing techniques have been set forth for searching and retrieving documents based on a natural language input. Such natural language processing techniques provide specific answers to queries submitted by a user while avoiding the need for the user to sort through a set of search results such as might be provided by standard keyword-based searches. Various embodiments of natural language processing techniques have been set forth for improving the efficiency of accessing knowledge bases. 
     Various embodiments may be described herein in the general context of computer executable instructions, such software, program modules, components, being executed by a computer. Generally, program modules include any software element arranged to perform particular operations or implement particular abstract data types. Software can include routines, programs, objects, components, data structures and the like that perform particular tasks or implement particular abstract data types. An implementation of these modules or components and techniques may be stored on and/or transmitted across some form of computer-readable media. In this regard, computer-readable media can be any available medium or media useable to store information and accessible by a computing device. Some embodiments also may be practiced in distributed computing environments where operations are performed by one or more remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices. 
     Although some embodiments may be illustrated and described as comprising functional components or modules performing various operations, it can be appreciated that such components or modules may be implemented by one or more hardware components, software components, and/or combination thereof. The functional components and/or modules may be implemented, for example, by logic (e.g., instructions, data, and/or code) to be executed by a logic device (e.g., processor). Such logic may be stored internally or externally to a logic device on one or more types of computer-readable storage media. Examples of hardware elements may include processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, and so forth), integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), logic gates, registers, semiconductor device, chips, microchips, chip sets, and so forth. Examples of software may include software components, programs, applications, computer programs, application programs, system programs, machine programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application program interfaces (API), instruction sets, computing code, computer code, code segments, computer code segments, words, values, symbols, or any combination thereof. Determining whether an embodiment is implemented using hardware elements and/or software elements may vary in accordance with any number of factors, such as desired computational rate, power levels, heat tolerances, processing cycle budget, input data rates, output data rates, memory resources, data bus speeds and other design or performance constraints. 
     It also is to be appreciated that the described embodiments illustrate example implementations, and that the functional components and/or modules may be implemented in various other ways which are consistent with the described embodiments. Furthermore, the operations performed by such components or modules may be combined and/or separated for a given implementation and may be performed by a greater number or fewer number of components or modules. 
     It is worthy to note that any reference to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in one embodiment” in the specification are not necessarily all referring to the same embodiment. 
     Unless specifically stated otherwise, it may be appreciated that terms such as “processing,” “computing,” “calculating,” “determining,” or the like, refer to the action and/or processes of a computer or computing system, or similar electronic computing device, that manipulates and/or transforms data represented as physical quantities (e.g., electronic) within registers and/or memories into other data similarly represented as physical quantities within the memories, registers or other such information storage, transmission or display devices. 
     It is worthy to note that some embodiments may be described using the expression “coupled” and “connected” along with their derivatives. These terms are not intended as synonyms for each other. For example, some embodiments may be described using the terms “connected” and/or “coupled” to indicate that two or more elements are in direct physical or electrical contact with each other. The term “coupled,” however, may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other. With respect to software elements, for example, the term “coupled” may refer to interfaces, message interfaces, API, exchanging messages, and so forth. 
     While certain features of the embodiments have been illustrated as described above, many modifications, substitutions, changes and equivalents will now occur to those skilled in the art. It is therefore to be understood that the appended claims are intended to cover all such modifications and changes as fall within the scope of the embodiments.