Patent Publication Number: US-2023148228-A1

Title: Computer assisted answering boolean questions with evidence

Description:
BACKGROUND 
     The field of embodiments of the present invention relates to use of a computing device using natural language software to respond to a Boolean question and automatically providing evidence and providing more fine-grained answers to Boolean questions in a question answering (QA) system. 
     QA systems are an important tool used by people seeking answers. In factoid question answering, there is a short answer to the question. In some cases, the answer is Boolean. Existing QA systems expect Boolean questions to be answered with Yes, No, or No Answer without providing justification. Further, a simple Yes/No is not always appropriate. 
     SUMMARY 
     Embodiments relate to use of a computing device using natural language software to respond to a Boolean question. One embodiment provides a method of using a computing device using natural language software to respond to a Boolean question is presented. The method includes receiving, by a computing device, a question and at least one passage. The computing device classifies the question as a Boolean type question. The computing device generates evidence from the at least one passage to the Boolean question. The computing device generates a response to the Boolean question from the generated evidence. The computing device provides the generated evidence that supports the response to the Boolean question. Some features contribute to the advantage of automatically providing evidence demarcation, and more fine-grained answers to Boolean questions in a QA system. This provides the user with a better understanding of the answer to the question. Other features provide that the Boolean questions and answers can be in one or more languages, and need not be the same language. Further features provide that multiple answers, even if conflicting, may be in one or more passages in a corpus. 
     One or more of the following features may be included. In some embodiments, the method may further include the feature that the classifying employs a question type classifier to determine the question type. 
     In some embodiments, the method may additionally include the feature that the response is a single Boolean type of answer. Another feature that may be included is that the response is more than one Boolean type of answer. 
     In one or more embodiments, the method may further include that the response is a fine-grained Boolean type of answer, and the fine-grained Boolean type of answer is labeled as a weak-No answer, a weak-Yes answer, both Yes and No answer or a scaled type of answer. 
     In some embodiments, the method may include that the generated evidence that supports the response is indicated by demarcating the generated evidence in the at least one passage. 
     In some embodiments, the method may include that the generated evidence comprises text-based evidence in one or more languages. 
     These and other features, aspects and advantages of the present embodiments will become understood with reference to the following description, appended claims and accompanying figures. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates a Boolean question answering (QA) pipeline, according to one embodiment; 
         FIG.  2    illustrates a machine reading comprehension (MRC) pipeline with Boolean questions, according to one embodiment; 
         FIG.  3    illustrates an example display of evidence in response to a proposed Boolean question, according to one embodiment; 
         FIG.  4    illustrates another example display of evidence in response to a proposed Boolean question, according to one embodiment; 
         FIG.  5    illustrates yet another example display of evidence in response to a proposed Boolean question, according to one embodiment; 
         FIG.  6    illustrates a process for using natural language software to respond to a Boolean question and automatically providing evidence and providing more fine-grained answers to Boolean questions, according to an embodiment; 
         FIG.  7    depicts a cloud computing environment, according to an embodiment; 
         FIG.  8    depicts a set of abstraction model layers, according to an embodiment; 
         FIG.  9    is a network architecture of a system for using natural language software to respond to a Boolean question and automatically providing evidence and providing more fine-grained answers to Boolean questions, according to an embodiment; 
         FIG.  10    shows a representative hardware environment that may be associated with the servers and/or clients of  FIG.  7   , according to an embodiment; and 
         FIG.  11    is a block diagram illustrating a distributed system for using natural language software to respond to a Boolean question and automatically providing evidence and providing more fine-grained answers to Boolean questions, according to one embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     The descriptions of the various embodiments have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. 
     Embodiments relate to use of a computing device using natural language software to respond to a Boolean question. One embodiment provides a method of using a computing device using natural language software to respond to a Boolean question is presented. The method includes receiving, by a computing device, a question and at least one passage. The computing device classifies the question as a Boolean type question. The computing device generates evidence from the at least one passage to the Boolean question. The computing device generates a response to the Boolean question using the generated evidence. The computing device provides the generated evidence that supports the response to the Boolean question. Some features contribute to the advantage of automatically providing evidence demarcation, and more fine-grained answers to Boolean questions in a QA system. This provides the user with a better understanding of the answer to the question. Other features provide that the Boolean questions and answers can be in one or more languages, and need not be the same language. Further features provide that multiple answers, even if conflicting, may be in one or more passages in a corpus. 
     In some embodiments, the method may further include the feature that the classifying employs a question type classifier to determine the question type. 
     In some embodiments, the method may additionally include the feature that the response is a single Boolean type of answer. Another feature that may be included is that the response is more than one Boolean type of answer. 
     In one or more embodiments, the method may further include that the response is a fine-grained Boolean type of answer, and the fine-grained Boolean type of answer is labeled as a weak-No answer, a weak-Yes answer, both Yes and No answer or a scaled type of answer. 
     In some embodiments, the method may include that the generated evidence that supports the response is indicated by demarcating the generated evidence in the at least one passage. 
     In some embodiments, the method may include that the generated evidence comprises text-based evidence in one or more languages. 
       FIG.  1    illustrates a Boolean question answering (QA) pipeline  100 , according to one embodiment. In some embodiments, the pipeline  100  is an MRC pipeline. In one embodiment, in block  110 , a Boolean question and context are received by a computing device using natural language software (e.g., conventional natural language understanding software, etc.) of a QA system. In block  120 , evidence is added for Boolean questions, such as information from a corpus or collection of documents, publications, internal documents, etc., with text that relates to the Boolean question (e.g., found through a conventional natural language search application, etc.). In block  130 , a QA model (e.g., monolingual or multilingual) is executed. In block  140 , the output is generated from the QA model with a Y(es), N(o), No answer, Yes and No, weak-No, weak-Yes, scaled, etc. along with evidence (e.g., textual information) supporting the output. In one or more embodiments, the QA model has the ability for conflicting answers in a single passage to be highlighted for the user. The use of evidence for Boolean questions improves the QA system as the user has the text automatically displayed and highlighted to provide more support for the Boolean of fine-grained answers. This also provides the user with a better understanding of the answer to the question. 
       FIG.  2    illustrates a machine reading comprehension (MRC) pipeline  200  with Boolean questions, according to one embodiment. In block  210  a textual (which may be in natural language or converted from voice to natural language using a conventional application, etc.) question is provided, such as by obtaining from another passage, obtained from a computing device or memory from an initiating user, resulted from a search query, etc. In block  205 , one or more supporting passages are provided based on one or more topics or words obtained from the question  210 , a search query (e.g., using a search engine), etc. The one or more passages may be obtained from one or more documents, other natural language sources, such as emails, articles, etc.) based on elements in the question. In block  215 , a question type classifier uses natural language understanding (NLU) that classifies a type of question to provide information addressing whether the question is of a Boolean type or not. If the question type classifier determines the question has a traditional answer (e.g., Who is the president of the United States?) or is selected to have such an answer, the pipeline  200  proceeds to block  220  where an extractor (e.g., a conventional text-based extractor, etc.) is employed to extract portions from the supporting passage(s)  205  based on the type of question. In block  225 , pipeline  200  sets the answer span over the passage for the question text (if appropriate), and sets any other features in the extracted text. Note that there may be multiple instances of these. In block  230 , a score that was determined by the extractor in block  220  for the span of the supporting passage(s) is stored in a memory device. The extractor in block  220  may implement scoring via a simple term match score implementing executable instructions for counting the number of terms that match; textual alignment implementing executable instructions for determining if words appear in the same or similar order with a similar distance between them so they are not separated much ( aligned) that is advantageous to find a quotation to find quotes, for example, an alignment may be performed to obtain the best result; and, a deeper analysis implementing executable instructions for determining the meaning of the passages/question (i.e., lexical and/or semantic relations). Each of these analyses produces a score. In one or more embodiments, the span from block  225  and the score from block  230  are used in block  240  to determine whether a threshold is exceeded or not. If the threshold is not met, the result in block  235  is No-answer. If the threshold is met or exceeded, in block  245  a span from the passage supporting the non-boolean answer is output. 
     If the question type classifier in block  215  determines the question is Boolean (such as “Is Joe Biden the president of the United States?), or is selected to be a Boolean type question, the pipeline  200  proceeds to block  250  where an extractor (e.g., a conventional text-based extractor, etc.) is employed to extract portions from the supporting passage(s)  205  based on the type of question. In block  255 , pipeline  200  sets the span over the question text (if appropriate), and sets any other features in the extracted text. Note that there may be multiple instances of these. In block  260 , a score is determined for the span of the supporting passage(s) using a conventional scoring application or algorithm. In block  265 , the evidence span classifier determines the answer (e.g., weak No, weak Yes, No, Yes, Yes and No, etc.) to a Boolean type question. In block  280 , it is determined whether the answer has a value that meets (e.g., equal to or greater) a threshold or not (e.g., less than the threshold) for having an answer (i.e., Yes and No, weak-No, weak-Yes, scaled, etc.). If it is determined there is no answer, the pipeline  200  proceeds to block  285  where the output is no answer. Otherwise, the pipeline  200  proceeds to block  270  where the span of the passages for the answer is set. In some embodiments, the pipeline  200  provides a label  275  for fine-grained short answer(s) besides Yes  290  and No  291  are provided as output, which may include weak-No  292 , or others  293 , such as weak-Yes. Alternatively, a scale of 1-n can be used instead of short answers, where 1 may indicate a strong No and n would indicate a strong Yes. For each short answer, the pipeline  200  demarcates or highlights the evidence span  270  in the passage that explains the reasoning for the label generated in block  275  for that short answer, and each short answer’s evidence span may have a different label. In addition to highlighting the evidence span in block  270  co-reference resolution may be applied to highlight the appropriate entity. The output evidence span from block  270  is highlighted as justification for the output short answer(s). In some embodiments, the highlighting may include colors (e.g., fixed, or selectable), underlining, other markings (e.g., blurring portions of text while making evidence readable, larger font, bring to front, place behind, etc.). The Boolean questions and answers may be in one or more languages, and they need not be the same language. Multiple answers may be in one or more passages in a corpus, depository, etc. 
     Unlike conventional systems that may have the ability to only output some evidence for Boolean type questions without labeling an answer, those systems do not automatically highlight supporting passages of the evidence for multiple answers (e.g., Yes and No), they do not provide an answer with polarity that is fine-grained, and they do not even handle cases where there can be both Yes and No answers in a single passage or multiple passages. Further distinguishable from conventional systems, one or more embodiments classify questions as Boolean and non-Boolean, and display highlighted supporting evidence of for Boolean questions. Some embodiments capture use cases where Boolean questions may not have a straight-forward Yes or No answer; and these embodiments also work with one or more different languages. Using a question type classifier  215  to identify which questions are Boolean is more reliable than conventional systems and also avoids giving a YES/NO answer when the question is not Boolean. 
       FIG.  3    illustrates an example display  300  of evidence  320  in response to a proposed Boolean question  310 , according to one embodiment. As shown, the support evidence  330  and  340  in the contextual surrounding passages  320  are indicated in this example as highlighting, and have different highlighting from one another. Support evidence  330  is associated with the output answer No  360 , and support evidence  340  is associated with output answer Yes  350 . The contextual surrounding passage  320  is also displayed for context. The output answers Yes  350  and No  360  alerts the viewer that there are multiple answers to the Boolean question  310  labeled by block  275  ( FIG.  2   ) as Yes and No, there is supporting evidence  330  and  340  for answers Yes  350  and No  360 , which are each highlighted in the example display  300  the same as the respective supporting evidences  330  and  340 . 
       FIG.  4    illustrates another example display  400  of evidence  420  in response to a proposed Boolean question  410 , according to one embodiment. In this example display  400 , the supporting evidence  430  is highlighted and associated with the answer Weak-No  440 . The answer Weak-No  440  alerts the viewer that the answer to the Boolean question  410  is likely No, but that answer does not have enough weight over the threshold  280  ( FIG.  2   ) to be labeled in block  275  ( FIG.  2   ) as a No, so block  275  labeled the output as Weak-No  292  for the result output answer Weak-No  440 , which is highlighted the same in the example display  400  as the support evidence  430 . 
       FIG.  5    illustrates yet another example display  500  of evidence  520  in response to a proposed Boolean question  510 , according to one embodiment. In this example display  500 , the supporting evidence  530  is highlighted and associated with the answer Yes  540 . The answer Yes  540  alerts the viewer that the answer to the Boolean question had enough weight over the threshold  280  ( FIG.  2   ) to be labeled in block  275  ( FIG.  2   ) as a Yes  290 , so block  275  labeled the output as Yes  540  for the result output answer, which is highlighted the same in the example display  500  as the supporting evidence  530 . 
       FIG.  6    illustrates a process  600  for using natural language software to respond to a Boolean question and automatically providing evidence and providing more fine-grained answers to Boolean questions, according to an embodiment. In block  610 , process  600  provides receiving, by a computing device (e.g., from computing node  10 ,  FIG.  7   , hardware and software layer  60 ,  FIG.  8   , processing system  900 ,  FIG.  9   , system  1000 ,  FIG.  10   , system  1100 ,  FIG.  11   , etc.), a question (e.g., a simple question, a Boolean question, etc.) and at least one passage. In block  620 , process  600  provides classifying, by the computing device, the question as a Boolean type question. In block  630 , process  600  provides generating, by the computing device, evidence from the at least one passage. In block  640 , process  600  provides generating, by the computing device, a response (e.g., Yes, No, Yes and No, weak-Yes, weak-No, 1-n (n is an integer where the higher the n, the stronger the answer as Yes or No, depending on implementation) to the Boolean question using the generated evidence. In block  650 , process  600  includes providing, by the computing device, the generated evidence (e.g., textual passage(s)) and response that supports the response to the Boolean question. 
     In some embodiments, process  600  provides the feature that the classifying employs a question type classifier to determine the question type (e.g., Boolean, non-Boolean, etc.). In one or more embodiments, process  600  may include that the response is a single Boolean type of answer (e.g., Yes, No). 
     In some embodiments, process  600  provides the feature that the response is more than one Boolean type of answer (e.g., Yes and No). In one or more embodiments, process  600  may include that the response is a fine-grained Boolean type of answer, and the fine-grained Boolean type of answer is labeled as a weak-No answer, a weak-Yes answer, both Yes and No answer or a scaled type (e.g., 1-n) of answer. 
     In one or more embodiments, process  600  provides that the generated evidence that supports the response is indicated by demarcating (e.g., include colors (e.g., fixed, or selectable), underlining, other markings (e.g., blurring portions of text while making evidence readable, larger font, bring to front, place behind, etc.)), the generated evidence in the at least one passage. 
     In some embodiments, process  600  provides that the generated evidence comprises text-based evidence in one or more languages. 
     It is understood in advance that although this disclosure includes a detailed description of cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present embodiments are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines (VMs), and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed and automatically, without requiring human interaction with the service’s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous, thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider’s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or data center). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned and, in some cases, automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active consumer accounts). Resource usage can be monitored, controlled, and reported, thereby providing transparency for both the provider and consumer of the utilized service. 
     Service Models are as follows: 
     Software as a Service (SaaS): the capability provided to the consumer is the ability to use the provider’s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface, such as a web browser (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited consumer-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is the ability to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application-hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is the ability to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load balancing between clouds). 
     A cloud computing environment is a service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure comprising a network of interconnected nodes. 
     Referring now to  FIG.  7    an illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  comprises one or more cloud computing nodes  10  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as private, community, public, or hybrid clouds as described hereinabove, or a combination thereof. This allows the cloud computing environment  50  to offer infrastructure, platforms, and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  54 A-N shown in  FIG.  7    are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
     Referring now to  FIG.  8   , a set of functional abstraction layers provided by the cloud computing environment  50  ( FIG.  7   ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG.  8    are intended to be illustrative only and embodiments are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
     In one example, a management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and pricing  82  provide cost tracking as resources are utilized within the cloud computing environment and billing or invoicing for consumption of these resources. In one example, these resources may comprise application software licenses. Security provides identity verification for cloud consumers and tasks as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and for using natural language software to respond to a Boolean question and automatically providing evidence and providing more fine-grained answers to Boolean questions processing  96  (see, e.g., pipeline  200 ,  FIG.  2   , process  600 ,  FIG.  6   , system  900 ,  FIG.  9   , system  1000 ,  FIG.  10   , system  1100 ,  FIG.  11   , etc.). As mentioned above, all of the foregoing examples described with respect to  FIG.  8    are illustrative only, and the embodiments are not limited to these examples. 
     It is reiterated that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, the embodiments may be implemented with any type of clustered computing environment now known or later developed. 
       FIG.  9    is a network architecture of a system  900  for using natural language software to respond to a Boolean question and automatically providing evidence and providing more fine-grained answers to Boolean questions, according to an embodiment. As shown in  FIG.  9   , a plurality of remote networks  902  are provided, including a first remote network  904  and a second remote network  906 . A gateway  901  may be coupled between the remote networks  902  and a proximate network  908 . In the context of the present network architecture  900 , the networks  904 ,  906  may each take any form including, but not limited to, a LAN, a WAN, such as the Internet, public switched telephone network (PSTN), internal telephone network, etc. 
     In use, the gateway  901  serves as an entrance point from the remote networks  902  to the proximate network  908 . As such, the gateway  901  may function as a router, which is capable of directing a given packet of data that arrives at the gateway  901 , and a switch, which furnishes the actual path in and out of the gateway  901  for a given packet. 
     Further included is at least one data server  914  coupled to the proximate network  908 , which is accessible from the remote networks  902  via the gateway  901 . It should be noted that the data server(s)  914  may include any type of computing device/groupware. Coupled to each data server  914  is a plurality of user devices  916 . Such user devices  916  may include a desktop computer, laptop computer, handheld computer, printer, and/or any other type of logic-containing device. It should be noted that a user device  916  may also be directly coupled to any of the networks in some embodiments. 
     A peripheral  920  or series of peripherals  920 , e.g., facsimile machines, printers, scanners, hard disk drives, networked and/or local storage units or systems, etc., may be coupled to one or more of the networks  904 ,  906 ,  908 . It should be noted that databases and/or additional components may be utilized with, or integrated into, any type of network element coupled to the networks  904 ,  906 ,  908 . In the context of the present description, a network element may refer to any component of a network. 
     According to some approaches, methods and systems described herein may be implemented with and/or on virtual systems and/or systems, which emulate one or more other systems, such as a UNIX® system that emulates an IBM® z/OS environment, a UNIX® system that virtually hosts a MICROSOFT® WINDOWS® environment, a MICROSOFT® WINDOWS® system that emulates an IBM® z/OS environment, etc. This virtualization and/or emulation may be implemented through the use of VMWARE® software in some embodiments. 
       FIG.  10    shows a representative hardware system  1000  environment associated with a user device  916  and/or server  914  of  FIG.  9   , in accordance with one embodiment. In one example, a hardware configuration includes a workstation having a central processing unit  1010 , such as a microprocessor, and a number of other units interconnected via a system bus  1012 . The workstation shown in  FIG.  10    may include a Random Access Memory (RAM)  1014 , Read Only Memory (ROM)  1016 , an I/O adapter  1018  for connecting peripheral devices, such as disk storage units  1020  to the bus  1012 , a user interface adapter  1022  for connecting a keyboard  1024 , a mouse  1026 , a speaker  1028 , a microphone  1032 , and/or other user interface devices, such as a touch screen, a digital camera (not shown), etc., to the bus  1012 , communication adapter  1034  for connecting the workstation to a communication network  1035  (e.g., a data processing network) and a display adapter  1036  for connecting the bus  1012  to a display device  1038 . 
     In one example, the workstation may have resident thereon an operating system, such as the MICROSOFT® WINDOWS® Operating System (OS), a MAC OS®, a UNIX® OS, etc. In one embodiment, the system  1000  employs a POSIX® based file system. It will be appreciated that other examples may also be implemented on platforms and operating systems other than those mentioned. Such other examples may include operating systems written using JAVA®, XML, C, and/or C++ language, or other programming languages, along with an object oriented programming methodology. Object oriented programming (OOP), which has become increasingly used to develop complex applications, may also be used. 
       FIG.  11    is a block diagram illustrating a distributed system  1100  for using natural language software to respond to a Boolean question and automatically providing evidence and providing more fine-grained answers to Boolean questions, according to one embodiment. In one embodiment, the system  1100  includes client devices  1110  (e.g., mobile devices, smart devices, computing systems, etc.), a cloud or resource sharing environment  1120  (e.g., a public cloud computing environment, a private cloud computing environment, a data center, etc.), and servers  1130 . In one embodiment, the client devices  1110  are provided with cloud services from the servers  1130  through the cloud or resource sharing environment  1120 . 
     One or more embodiments may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present embodiments. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the embodiments may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user’s computer, partly on the user’s computer, as a stand-alone software package, partly on the user’s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user’s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present embodiments. 
     Aspects of the embodiments are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be accomplished as one step, executed concurrently, substantially concurrently, in a partially or wholly temporally overlapping manner, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     References in the claims to an element in the singular is not intended to mean “one and only” unless explicitly so stated, but rather “one or more.” All structural and functional equivalents to the elements of the above-described exemplary embodiment that are currently known or later come to be known to those of ordinary skill in the art are intended to be encompassed by the present claims. No claim element herein is to be construed under the provisions of 35 U.S.C. section 112, sixth paragraph, unless the element is expressly recited using the phrase “means for” or “step for.” 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the embodiments. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present embodiments has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the embodiments in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the embodiments. The embodiment was chosen and described in order to best explain the principles of the embodiments and the practical application, and to enable others of ordinary skill in the art to understand the embodiments for various embodiments with various modifications as are suited to the particular use contemplated.