Patent Publication Number: US-2022237381-A1

Title: Visually Correlating Individual Terms in Natural Language Input to Respective Structured Phrases Representing the Natural Language Input

Description:
RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 17/063,663, filed Oct. 5, 2020, entitled “Visually Correlating Individual Terms in Natural Language Input to Respective Structured Phrases Representing the Natural Language Input,” which is hereby incorporated by reference herein in its entirety. 
     This application is related to the following applications, each of which is incorporated by reference herein in its entirety:
         (i) U.S. patent application Ser. No. 15/486,265, filed Apr. 12, 2017, entitled “Systems and Methods of Using Natural Language Processing for Visual Analysis of a Data Set,” now U.S. Pat. No. 10,515,121;   (ii) U.S. patent application Ser. No. 15/804,991, filed Nov. 6, 2017, entitled “Systems and Methods of Using Natural Language Processing for Visual Analysis of a Data Set”;   (iii) U.S. patent application Ser. No. 15/978,062, filed May 11, 2018, entitled “Applying Natural Language Pragmatics in a Data Visualization User Interface”;   (iv) U.S. patent application Ser. No. 15/978,066, filed May 11, 2018, entitled “Data Visualization User Interface Using Cohesion of Sequential Natural Language Commands”;   (v) U.S. patent application Ser. No. 15/978,067, filed May 11, 2018, entitled “Updating Displayed Data Visualizations According to Identified Conversation Centers in Natural Language Commands”;   (vi) U.S. patent application Ser. No. 16/219,406, filed Dec. 13, 2018, entitled “Identifying Intent in Visual Analytical Conversations”;   (vii) U.S. patent application Ser. No. 16/134,892, filed Sep. 18, 2018, entitled “Analyzing Natural Language Expressions in a Data Visualization User Interface”;   (viii) U.S. patent application Ser. No. 16/134,907, filed Sep. 18, 2018, entitled “Natural Language Interface for Building Data Visualizations, Including Cascading Edits to Filter Expressions”;   (ix) U.S. patent application Ser. No. 16/166,125, filed Oct. 21, 2018, entitled “Determining Levels of Detail for Data Visualizations Using Natural Language Constructs”;   (x) U.S. patent application Ser. No. 16/234,470, filed Dec. 27, 2018, entitled “Analyzing Underspecified Natural Language Utterances in a Data Visualization User Interface”;   (xi) U.S. patent application Ser. No. 16/601,437, filed Oct. 14, 2019, entitled “Incremental Updates to Natural Language Expressions in a Data Visualization User Interface”;   (xii) U.S. patent application Ser. No. 16/680,431, filed Nov. 11, 2019, entitled “Using Refinement Widgets for Data Fields Referenced by Natural Language Expressions in a Data Visualization User Interface”;   (xiii) U.S. patent application Ser. No. 14/801,750, filed Jul. 16, 2015, entitled “Systems and Methods for using Multiple Aggregation Levels in a Single Data Visualization”;   (xiv) U.S. patent application Ser. No. 16/681,754, filed Nov. 12, 2019, entitled “Using Natural Language Expressions to Define Data Visualization Calculations that Span Across Multiple Rows of Data from a Database”; and   (xv) U.S. patent application Ser. No. 17/026,113, filed Sep. 18, 2020, entitled “Using Dynamic Entity Search during Entry of Natural Language Commands for Visual Data Analysis.”       

    
    
     TECHNICAL FIELD 
     The disclosed implementations relate generally to data visualization and more specifically to systems, methods, and user interfaces that enable users to interact with data visualizations and analyze data using natural language expressions. 
     BACKGROUND 
     Data visualization applications enable a user to understand a data set visually. Visual analyses of data sets, including distribution, trends, outliers, and other factors are important to making business decisions. Some data sets are very large or complex, and include many data fields. Various tools can be used to help understand and analyze the data, including dashboards that have multiple data visualizations and natural language interfaces that help with visual analytical tasks. 
     SUMMARY 
     The use of natural language expressions to generate data visualizations provides a user with greater accessibility to data visualization features, including updating the fields and changing how the data is filtered. A natural language interface enables a user to develop valuable data visualizations with little or no training. 
     There is a need for improved systems and methods that support and refine natural language interactions with visual analytical systems. The present disclosure describes data visualization platforms that improve the effectiveness of natural language interfaces by resolving natural language utterances as they are being input by a user of the data visualization platform. Unlike existing interfaces that require natural language inputs to be composed of complete words and/or phrases, the present disclosure describes a natural language interface that provides feedback (e.g., generates interpretations, analytical expressions, and/or correlates interpretations and input query terms) in response to each term that is input by the user. 
     The disclosed natural language interface automatically annotates a term in a natural language utterance when the interface determines with certain confidence that the term should be interpreted as a particular entity in the data source. The disclosed natural language interface also resolves ambiguities in natural language utterances by visually correlating how a term in a natural language input maps to respective analytical expressions or phrases corresponding to the interpretation. Once a term is automatically annotated, the data visualization platform displays analytical expressions or phrases corresponding to the interpretation. The data visualization platform also visually emphasizes a term and its corresponding phrases (e.g., by simultaneously pulsing the term and its corresponding phrases). The data visualization platform also visually de-emphasizes other terms in the natural language input that are not recognized by the platform, thereby informing the user that these other terms are not required in the natural language command. Accordingly, such methods and interfaces reduce the cognitive burden on a user and produce a more efficient human-machine interface. For battery-operated devices, such methods and interfaces conserve power and increase the time between battery charges. Such methods and interfaces may complement or replace conventional methods for visualizing data. Other implementations and advantages may be apparent to those skilled in the art in light of the descriptions and drawings in this specification. 
     In accordance with some implementations, a method is performed at a computing device. The computing device has a display, one or more processors, and memory. The memory stores one or more programs configured for execution by the one or more processors. The computing device receives from a user a partial natural language input related to a data source. The partial natural language input includes a most recently entered first term. In response to receiving the first term, the computing device generates a first token that includes the first term. The computing device maps the first token to one or more analytical concepts in a lexicon of the data source. The computing device determines a first interpretation corresponding to the first token. The computing device also displays a first phrase corresponding to the first interpretation. The first phrase includes the first term. 
     In some implementations, generating the first token includes concatenating the first term with another term that immediately precedes the first term in the partial natural language input. 
     In some implementations, generating the first token includes concatenating the first term with another term that immediately follows the first term in the partial natural language input. 
     In some implementations, the first phrase includes an analytical concept corresponding to the first term. 
     In some implementations, displaying the first phrase further comprises simultaneously visually emphasizing the first term and the first phrase. 
     In some implementations, simultaneously visually emphasizing the first term and the first phrase includes simultaneously pulsing the first term and the first phrase. 
     In some instances, the partial natural language input consists of a plurality of terms including the first term. In some implementations, visually emphasizing the first term further comprises visually de-emphasizing one or more other terms in the plurality of terms. 
     In some instances, the visually de-emphasized one or more other terms include a second term. The computing device receives user selection of the second term. The computing device also receives user input specifying a data field name of a data field from the data source. In response to the user input, the computing device stores the second term as a synonym of the data field name. 
     In some instances, after the storing, the computing device receives from the user a second partial natural language command. The second partial natural language command includes the second term. In response to receiving the second term, the computing device displays a second phrase corresponding to the second term. The second phrase includes the data fieldname stored as a synonym of the second term. 
     In some implementations, the partial natural language input is received in a first region of a graphical user interface. The first phrase is displayed in a second region of the graphical user interface, distinct from the first region. 
     In some implementations, after determining the first interpretation corresponding to the first token, the computing device assigns a first index to the first token. The computing device also generates a data structure that includes the first index and the first token. 
     In some instances, the computing device receives additional user input to the partial natural language input. In some implementations, the additional user input includes a third term. In response to receiving the third term, the computing device generates a third token that includes the third term. The computing device maps the third token to one or more analytical concepts in the lexicon. The computing device determines a third interpretation corresponding to the third token. The computing device also displays in the graphical user interface a third phrase corresponding to the third interpretation. 
     In some instances, after the computing device determines the first interpretation, the computing device persists the first interpretation. The third interpretation includes the first interpretation. 
     In some instances, the computing device receives a user interaction with the first term. In some implementations, in response to the user interaction, the computing device maintains visual display of the first phrase while visually de-emphasizing the third phrase. 
     In some implementations, a computing device includes one or more processors, memory, and one or more programs stored in the memory. The programs are configured for execution by the one or more processors. The one or more programs include instructions for performing any of the methods described herein. 
     In some implementations, a non-transitory computer-readable storage medium stores one or more programs configured for execution by a computing device having one or more processors and memory. The one or more programs include instructions for performing any of the methods described herein. 
     Thus methods, systems, and graphical user interfaces are disclosed that enable users to easily interact with data visualizations and analyze data using natural language expressions. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a better understanding of the aforementioned systems, methods, and graphical user interfaces, as well as additional systems, methods, and graphical user interfaces that provide data visualization analytics, reference should be made to the Description of Implementations below, in conjunction with the following drawings in which like reference numerals refer to corresponding parts throughout the figures. 
         FIG. 1  illustrates a graphical user interface used in some implementations. 
         FIGS. 2A-2D  are block diagrams of a computing device according to some implementations. 
         FIG. 3  provides a screenshot for a graphical user interface according to some implementations. 
         FIGS. 4A-4O  provide a series of screen shots for interactions with a graphical user interface according to some implementations. 
         FIGS. 5A-5K  provide a series of screen shots for interactions with a graphical user interface according to some implementations. 
         FIGS. 6A-6D  provide a flowchart of a method performed at a computing device according to some implementations. 
     
    
    
     Reference will now be made to implementations, examples of which are illustrated in the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that the present invention may be practiced without requiring these specific details. 
     DESCRIPTION OF IMPLEMENTATIONS 
     Some methods and devices disclosed in the present specification improve upon data visualization methods by generating and displaying interpretations in response to each subsequent additional keystroke of a partial natural language command (e.g., a command that contains incomplete words, phrases, and/or sentences). In response to a determination that a term in the natural language utterance matches a particular entity (e.g., with a certain level of confidence), the data visualization application automatically annotates a term in the natural language command, which in turn instructs a data visualization application to interpret the term as the particular entity in the data source. Such methods and devices improve user interaction with the natural language interface by providing quicker and easier incremental updates to natural language expressions related to a data visualization. Database “entities” include data field names, table names, and data values. Entities also include natural language keywords and keywords that express database semantics. As used herein, “annotating” a term entails assigning a specific meaning to the term. Terms that are not annotated can have their interpretations updated as a user enters more terms or modifies existing terms. 
     Some methods and devices disclosed in the present specification improve upon data visualization methods by generating and displaying how a term in a natural language input maps to respective analytical expressions or phrases corresponding to the interpretation. 
       FIG. 1  illustrates a graphical user interface  100  for interactive data analysis. The user interface  100  includes a Data tab  114  and an Analytics tab  116  in accordance with some implementations. When the Data tab  114  is selected, the user interface  100  displays a schema information region  110 , which is also referred to as a data pane. The schema information region  110  provides named data elements (e.g., field names) that may be selected and used to build a data visualization. In some implementations, the list of field names is separated into a group of dimensions (e.g., categorical data) and a group of measures (e.g., numeric quantities). Some implementations also include a list of parameters. When the Analytics tab  116  is selected, the user interface displays a list of analytic functions instead of data elements (not shown). 
     The graphical user interface  100  also includes a data visualization region  112 . The data visualization region  112  includes a plurality of shelf regions, such as a columns shelf region  120  and a rows shelf region  122 . These are also referred to as the column shelf  120  and the row shelf  122 . As illustrated here, the data visualization region  112  also has a large space for displaying a visual graphic (also referred to herein as a data visualization). Because no data elements have been selected yet, the space initially has no visual graphic. In some implementations, the data visualization region  112  has multiple layers that are referred to as sheets. In some implementations, the data visualization region  112  includes a region  126  for data visualization filters. 
     In some implementations, the graphical user interface  100  also includes a natural language input box  124  (also referred to as a command box) for receiving natural language commands. A user may interact with the command box to provide commands. For example, the user may provide a natural language command by typing in the box  124 . In addition, the user may indirectly interact with the command box by speaking into a microphone  220  to provide commands. In some implementations, data elements are initially associated with the column shelf  120  and the row shelf  122  (e.g., using drag and drop operations from the schema information region  110  to the column shelf  120  and/or the row shelf  122 ). After the initial association, the user may use natural language commands (e.g., in the natural language input box  124 ) to further explore the displayed data visualization. In some instances, a user creates the initial association using the natural language input box  124 , which results in one or more data elements being placed on the column shelf  120  and on the row shelf  122 . For example, the user may provide a command to create a relationship between a data element X and a data element Y. In response to receiving the command, the column shelf  120  and the row shelf  122  may be populated with the data elements (e.g., the column shelf  120  may be populated with the data element X and the row shelf  122  may be populated with the data element Y, or vice versa). 
       FIG. 2A  is a block diagram illustrating a computing device  200  that can display the graphical user interface  100  in accordance with some implementations. Various examples of the computing device  200  include a desktop computer, a laptop computer, a tablet computer, and other computing devices that have a display and a processor capable of running a data visualization application  230 . The computing device  200  typically includes one or more processing units (processors or cores)  202 , one or more network or other communication interfaces  204 , memory  206 , and one or more communication buses  208  for interconnecting these components. In some implementations, the communication buses  208  include circuitry (sometimes called a chipset) that interconnects and controls communications between system components. 
     The computing device  200  includes a user interface  210 . The user interface  210  typically includes a display device  212 . In some implementations, the computing device  200  includes input devices such as a keyboard, mouse, and/or other input buttons  216 . Alternatively or in addition, in some implementations, the display device  212  includes a touch-sensitive surface  214 , in which case the display device  212  is a touch-sensitive display. In some implementations, the touch-sensitive surface  214  is configured to detect various swipe gestures (e.g., continuous gestures in vertical and/or horizontal directions) and/or other gestures (e.g., single/double tap). In computing devices that have a touch-sensitive display  214 , a physical keyboard is optional (e.g., a soft keyboard may be displayed when keyboard entry is needed). The user interface  210  also includes an audio output device  218 , such as speakers or an audio output connection connected to speakers, earphones, or headphones. Furthermore, some computing devices  200  use a microphone  220  and voice recognition to supplement or replace the keyboard. In some implementations, the computing device  200  includes an audio input device  220  (e.g., a microphone) to capture audio (e.g., speech from a user). 
     In some implementations, the memory  206  includes high-speed random-access memory, such as DRAM, SRAM, DDR RAM, or other random-access solid-state memory devices. In some implementations, the memory  206  includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. In some implementations, the memory  206  includes one or more storage devices remotely located from the processor(s)  202 . The memory  206 , or alternatively the non-volatile memory device(s) within the memory  206 , includes a non-transitory computer-readable storage medium. In some implementations, the memory  206  or the computer-readable storage medium of the memory  206  stores the following programs, modules, and data structures, or a subset or superset thereof:
         an operating system  222 , which includes procedures for handling various basic system services and for performing hardware dependent tasks;   a communications module  224 , which is used for connecting the computing device  200  to other computers and devices via the one or more communication interfaces  204  (wired or wireless), such as the Internet, other wide area networks, local area networks, metropolitan area networks, and so on;   a web browser  226  (or other application capable of displaying web pages), which enables a user to communicate over a network with remote computers or devices;   an audio input module  228  (e.g., a microphone module) for processing audio captured by the audio input device  220 . The captured audio may be sent to a remote server and/or processed by an application executing on the computing device  200  (e.g., the data visualization application  230  or the natural language system  236 );   a data visualization application  230 , which generates data visualizations and related features. In some implementations, the data visualization application  230  includes:
           a graphical user interface  100  for a user to construct visual graphics. In some implementations, the graphical user interface includes a user input module  232  for receiving user input through the natural language box  124 . For example, a user inputs a natural language command or expression into the natural language box  124  identifying one or more data sources  258  (which may be stored on the computing device  200  or stored remotely) and/or data fields from the data source(s). In some implementations, the natural language expression is a voice utterance captured by the audio input device  220 . The selected fields are used to define a visual graphic. The data visualization application  230  then displays the generated visual graphic in the user interface  100 . In some implementations, the data visualization application  230  executes as a standalone application (e.g., a desktop application). In some implementations, the data visualization application  230  executes within the web browser  226  or another application using web pages provided by a web server;   a data visualization generator  234 , which automatically generates and displays a corresponding visual graphic (also referred to as a “data visualization” or a “data viz”) using the user input (e.g., the natural language input);   a natural language system  236 , which receives and parses the natural language input provided by the user. The natural language system  236  may identify analytical expressions  238 , which are described in  FIG. 2B .   the natural language system  236  may also include a dependency calculator  250 , which looks up dependencies in a database  258  to determine how particular terms and/or phrases are related (e.g., dependent);   in some implementations, the natural language system  236  includes a filter generator  252 , which determines if one or more filters are related to a field that has been modified by a user. The filter generator  252  generates the one or more filters based on user selections;   a widget generator  254 , which generates widgets that include user-selectable options. For example, a “sort” widget is generated in response to a user selecting (e.g., hovering) over a sort field (e.g., a natural language term identified to be a sort field). The sort widget includes user-selectable options such as “ascending,” “descending,” and/or “alphabetical,” so that the user can easily select, from the widget, how to sort the selected field; and   visual specifications  256 , which are used to define characteristics of a desired data visualization. In some implementations, the information the user provides (e.g., user input) is stored as a visual specification. In some implementations, the visual specifications  256  include previous natural language commands received from a user or properties specified by the user through natural language commands. In some instances, a visual specification  256  includes two or more aggregations based on different levels of detail. Further information about levels of detail can be found in U.S. patent application Ser. No. 14/801,750, filed Jul. 16, 2015, titled “Systems and Methods for using Multiple Aggregation Levels in a Single Data Visualization,” and U.S. patent application Ser. No. 16/166,125, filed Oct. 21, 2018, titled “Determining Levels of Detail for Data Visualizations Using Natural Language Constructs,” each of which is incorporated by reference herein in its entirety; and   
           zero or more databases or data sources  258  (e.g., a first data source  258 - 1 ), which are used by the data visualization application  230 . In some implementations, the data sources are stored as spreadsheet files, CSV files, XML files, flat files, or JSON files, or stored in a relational database. For example, a user selects one or more databases or data sources  258  (which may be stored on the computing device  200  or stored remotely), selects data fields from the data source(s), and uses the selected fields to define a visual graphic.   zero or more semantic models  260  (e.g., a first semantic model  260 - 1 ), each of which is derived directly from a respective database or data source  258 . The semantic model  260  represents the database schema and contains metadata about attributes (e.g., data fields). In some implementations, the semantic model  260  also includes metadata of alternative labels or synonyms of the attributes. The semantic model  260  includes data types (e.g., “text,” “date,” “geospatial,” “Boolean,” and “numeric”), attributes (e.g., a currency type such as the United States Dollar), and a semantic role (e.g., the “City” role for a geospatial attribute) for data fields of the respective database or data source  258 . In some implementations, the semantic model  260  also captures statistical values (e.g., data distribution, range limits, average, and cardinality) for each attribute. In some implementations, the semantic model  260  is augmented with a grammar lexicon  262 , which contains a set of analytical concepts  266  found in many query languages (e.g., average, filter, and sort). In some implementations, the semantic model  260  also distinguishes between attributes that are measures (e.g., attributes that can be measured, aggregated, or used for mathematical operations) and dimensions (e.g., fields that cannot be aggregated except by counting). Thus, the semantic model  260  helps with inferencing and choosing salient attributes and values;   a grammar lexicon  262 , which includes analytical concepts  266  (see  FIG. 2C ) that are used to support the analytical expressions  238  for forming intermediate expressions; and   zero or more data source lexicons  264  (e.g., a first data source lexicon  264 - 1 ), each of which is associated with a respective database or data source  258 . Details of the components of a data source lexicon are described in  FIG. 2D .       

     In some implementations the computing device  200  further includes an inferencing module (not shown), which is used to resolve underspecified (e.g., omitted information) or ambiguous (e.g., vague) natural language commands (e.g., expressions or utterances) directed to the databases or data sources  258 , using one or more inferencing rules. Further information about the inferencing module can be found in U.S. patent application Ser. No. 16/234,470, filed Dec. 27, 2018, titled “Analyzing Underspecified Natural Language Utterances in a Data Visualization User Interface,” which is incorporated by reference herein in its entirety. 
     In some implementations, canonical representations are assigned to the analytical expressions  238  (e.g., by the natural language system  236 ) to address the problem of proliferation of ambiguous syntactic parses inherent to natural language querying. The canonical structures are unambiguous from the point of view of the parser and the natural language system  236  is able to choose quickly between multiple syntactic parses to form intermediate expressions. Further information about the canonical representations can be found in U.S. patent application Ser. No. 16/234,470, filed Dec. 27, 2018, titled “Analyzing Underspecified Natural Language Utterances in a Data Visualization User Interface,” which is incorporated by reference herein in its entirety. 
     Although  FIG. 2A  shows a computing device  200 ,  FIG. 2A  is intended more as a functional description of the various features that may be present rather than as a structural schematic of the implementations described herein. In practice, and as recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. 
       FIG. 2B  is block diagram illustrating the analytical expressions  238  of the natural language system  236 , in accordance with some implementations. The analytical expressions  238  include:
         aggregation expressions  240 . For example, “average Sales” is an aggregate expression that includes an aggregate term “average” and an attribute “Sales.” In some implementations, the aggregation expressions  240  are in the canonical form [agg att], where agg ∈ Aggregations and att is an Attribute (i.e., a data field);   group expressions  242 . For example, “by Region” is a group expression that includes a group term “by” and an attribute “Region.” In some implementations, the group expressions  242  are in the canonical form [grp att], where grp ∈ Groups and att is an attribute;   filter expressions  244 . For example, “Customer Name starts with John” is a filter expression that contains an attribute “Customer Name,” a filter “starts with,” and a value “John.” In some implementations, the filter expressions  244  are in the canonical form [att filter val], where att is an attribute, filter ∈ Filters, and val ∈ Values;   limit expressions  246 . For example, “top 5 Wineries by sum of Sales” is a limit expression that contains a limit term “top”, a value “5”, a group by attribute “Wineries,” and an aggregation expression “sum of Sales.” In some implementations, the limit expressions  246  are in the canonical form [limit val ge ae], where limit ∈ Limits, val ∈ Values, ge ∈ group expressions, and ae ∈ aggregation expressions; and   sort expressions  248 . For example, in “sort Products in ascending order by sum of Profit,” the phrase “ascending order” is the sort term, “Products” is the attribute to group by, and “sum of Profit” is the aggregation expression. In some implementations, the sort expressions  248  are in the canonical form [sort ge ae], where sort ∈ Sorts, ge ∈ group expressions, and ae ∈ aggregation expressions.       

       FIG. 2C  is a block diagram illustrating components of a grammar lexicon  262  according to some implementations. In some implementations, the grammar lexicon comprises analytical concepts  266  that support the formation of analytical expressions  238 . The analytical concepts  266  include:
         data fields  268 , which are database fields. Examples of field concepts include “Sales,” and “Product Category”;   data values  270 , which are data values for database fields. Examples of value concepts include the value  10 , 500 , 000 . 00  for a Sales data field and the value “Chairs” for a Product Category data field;   aggregation operators  272 , which are operators that aggregate the values of multiple rows to form a single value based on a mathematical operation. Examples of aggregation concepts include “sum,” “average,” “median,” “count,” and “distinct count”;   group operators  274 , which are operators that partition the data into categories. An example of a group concept is the “by” key value;   filter operators  276 , which are operators that return a subset of rows from the database. Examples of filter concepts include “filter to,” “at least,” “between,” and “at most”;   limit operators  278 , which are operators (akin to the filters  276 ) that return a subset of rows from the database, restricting to n rows, where 1≤n≤N, and N is the total number of rows in the domain. Examples of limit concepts include “top” and “bottom”; and   sort operators  280 , which are operators that arrange data rows in a specific order. Examples of sort concepts include “ascending,” “descending,” and “alphabetical.”       

       FIG. 2D  is a block diagram illustrating components of a first data source lexicon  264 - 1 , in accordance with some implementations. The first data source lexicon  264 - 1  includes table names  282  corresponding to names of one or more tables of the first data source  258 - 1 , a plurality of data fields  284  of the first data source  258 - 1 , and other database objects  296 . Each data field  284  includes:
         a data field name  285 , which identifies the data field;   a data type  286 , such as integer, string, date, or floating point numeric;   one or more concepts  288 , which are used to interpret the data field. For example, a data value “Michael” may be interpreted using the concepts such as a “string,” “name,” “gender (e.g., male),” “singer,” “basketball player,” and/or “chef.” In some implementations, the one or more concepts are derived from elastic searches;   zero or more synonyms  290 , which are defined by the system. For example, a data field “average” may include synonyms such as “mean” and “avg”;   zero or more aliases  292 , which are defined by the user. For example, a data field “goods” may include aliases such as “widgets,” “bananas,” and “my favorite field”; and   data values  294 , which are some or all of the distinct values for a data field. This is particularly useful for low cardinality string data fields. In some instances, the set of stored data values  294  for a data field  284  in a lexicon  264  is limited to data values with threshold usage in the data field  284  (e.g., include a data value  294  in the lexicon when the data value appears in at least a threshold number of rows for the data field  284  or appears in a threshold percentage of the rows of the data field).       

     In some implementations, a data source lexicon  264  includes other database objects  296  as well. 
     In some implementations, the computing device  200  also includes other modules such as an autocomplete module, which displays a dropdown menu with a plurality of candidate options when the user starts typing into the input box  124 , and an ambiguity module to resolve syntactic and semantic ambiguities between the natural language commands and data fields (not shown). Details of these sub-modules are described in U.S. patent application Ser. No. 16/134,892, titled “Analyzing Natural Language Expressions in a Data Visualization User Interface, filed Sep. 18, 2018, which is incorporated by reference herein in its entirety. 
     Each of the above identified executable modules, applications, or sets of procedures may be stored in one or more of the memory devices, and corresponds to a set of instructions for performing a function described above. The above identified modules or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various implementations. In some implementations, the memory  206  stores a subset of the modules and data structures identified above. Furthermore, the memory  206  may store additional modules or data structures not described above 
       FIG. 3  is a screen shot for a graphical user interface  100  according to some implementations. In some implementations, as illustrated in  FIG. 3 , the data visualization region  112  displays suggestions  302  (e.g., tips or pointers) to assist the user in interacting with the data source. Further details about the suggestions  302  are described in U.S. patent application Ser. No. 16/601,437, filed Oct. 14, 2019, entitled “Incremental Updates to Natural Language Expressions in a Data Visualization User Interface,” which is incorporated by reference herein in its entirely. 
     In the example of  FIG. 3 , a user is interacting with a data source  258 . The schema information region  110  provides named data elements (e.g., field names) from the data source  258 , which may be selected and used to build a data visualization. 
       FIG. 3  illustrates a user interaction with the graphical user interface  100 . In this example, the user inputs (e.g., enters or types) a natural language expression (e.g., a natural language command)  304  “year over year sales” in the command box  124 . The user may also input the natural language expression by speech, which is then captured using an audio input device  220  (e.g. a microphone) coupled to the computing device  200 . Typically, the natural language expression includes one or more terms that identify data fields from the data source  258 . A term may be a dimension (e.g., categorical data) or a measure (e.g., a numerical quantity). As illustrated by the example, the natural language input typically includes one or more terms that correspond to database fields (e.g., the term “sales” identifies a data field from the data source). 
     In some implementations, parsing of the natural language expression is triggered in response to the user input. In this example, the natural language command  304  includes the terms “year over year,” which specifies a table calculation type. 
     In response to the natural language command  304 , the graphical user interface  100  displays an interpretation  308  (also referred to as a proposed action) in an interpretation box  310 . In some implementations, as illustrated in  FIG. 3 , the field names “Sales” and “Order Date” are displayed in a visually distinctive manner (e.g., in boldface) relative to other words included in the interpretation  308 . 
       FIGS. 4A-4O  provide a series of screen shots for interactions with a graphical user interface  100  according to some implementations. 
       FIG. 4A  provides a screen shot for a partial view of a graphical user interface  100  according to some implementations. 
     In some implementations, as illustrated in  FIG. 4A , the graphical user interface  100  comprises a natural language input box  124  for receiving natural language commands from a user. The natural language input box  124  includes a graphical control element  401  (e.g., a “Submit” affordance) that, when selected by a user, causes a natural language command in the input box  124  to be transmitted to the computing system  200  (e.g., the natural language system  236 ) for analysis and/or interpretation. In some implementations, the computing system  200  generates a visualization (e.g., a data visualization) in response to the analysis and/or interpretation and returns the visualization for display on the graphical user interface  100 . In this example, the graphical control element  401  is deactivated (e.g., grayed out) because the graphical user interface  100  has yet to receive a natural language command. 
     In some implementations, the graphical user interface  100  also comprises a data field interpretation region  402  and a filter interpretation region  404 , which are located adjacent to (e.g., above) the natural language input box  124 . The data field interpretation region  402  displays how the natural language system  236  interprets the natural language input from a user in light of the selected data source. The filter interpretation region  404  displays the filters (if any) that are applied to data fields of the data source  258  in response to the natural language input from the user. In this example, no interpretation is displayed in the regions  402  and  404  because the graphical user interface  100  has yet to receive a natural language command. In some implementations, the data field interpretation region  402  and the filter interpretation region  404  are collectively referred to as an interpretation region (or the natural language command interpretation region). 
       FIG. 4B  illustrates a user interaction with the graphical user interface  100 . In this example, the user inputs (e.g., enters or types) a partial natural language expression  406  (e.g., “what are furni”) into the command box  124 . In general, the expression can be a command, an input, or an utterance that includes partial or incomplete words (e.g., the partial word “furni”), phrases, and/or sentences. Typically, a natural language expression includes one or more terms that identify entities (e.g., a data field, a data value of a data field, an analytical operation, and/or a data visualization type) from the data source  258 . The user may also input the natural language expression by speech, which is then captured using an audio input device  220  (e.g. a microphone) coupled to the computing device  200 . 
       FIG. 4B  also illustrates in response to the user interaction, the graphical user interface  100  displays interpretations  410  (e.g., search results, entity search results, or suggestions) in a dropdown menu  408 . Each of the interpretations  410  is generated by the natural language system  236  and includes an entity from the data source  258 . In the example of  FIG. 4B , each of the interpretations  410  identifies a distinct data value  412  in the data source  258  and its corresponding data field  414 . For instance, the first interpretation  410 - 1  identifies a data value  412 - 1  (“Furnishings”) of a data field  414 - 1  (“Sub-category”). The second interpretation  410 - 2  identifies a data value  412 - 2  (“Furniture”) of a data field  414 - 2  (“Category”). In the example of  FIG. 4B , each of the data values  412  contains the letters “Furni,” corresponding to the keystrokes of the most recently entered term “furni”. In this example, the most recently entered term “furni” is a partial word (e.g., an incomplete word). In some implementations, the most recently entered term may be one word (e.g., “sales”), two words separated by a space (e.g., “San Francisco”), three or more words (e.g., “same day shipping”), a phrase, numbers, and/or a combination of letters and numbers. 
     As also illustrated in  FIG. 4B , the letters “Furn” in the interpretations  410  are displayed in a visually distinctive manner (e.g., boldface) from the remaining characters of the data value. 
     In some implementations, the user may select an interpretation (e.g., one of the interpretations  410 ) in the dropdown menu  410 . In some implementations, user selection of an interpretation corresponding to a term in the partial language command causes the term to be annotated by the natural language system  236 . Annotation in this context means that the user has instructed the natural language system  236  to interpret a term (e.g., a word or a phrase) in the input (e.g., natural language utterance, command, and/or question) as a reference to a particular entity in the data source  258  (e.g., as a data field, a data value, and/or an analytical concept). Further details about annotation are described in U.S. patent application Ser. No. 17/026,113, filed Sep. 18, 2020, titled “Using Dynamic Entity Search during Entry of Natural Language Commands for Visual Data Analysis,” which is incorporated by reference herein in its entity. 
       FIG. 4C  illustrates another user interaction with the graphical user interface  100 . In this example, additional keystrokes  416  (e.g., the letters “ture”) are appended to the end of the partial natural language input  406  “what are furni” (e.g., the interpretations  410  in  FIG. 4B  are not selected). In some implementations, the user may click on any portion of the partial natural language input and input additional keystrokes and/or modify an existing term of the partial natural language input. 
     In some implementations, the natural language system  236  generates one or more tokens from the character string. A token may include one letter (e.g., a letter of a word, such as the letter “r”), two letters, three or more letters (e.g., letters of a word), one word (e.g., the word “paper”), two or more words, and/or one or more phrases that is formed by combining two or more consecutive words. 
     Referring again to  FIG. 4C , the natural language system  236  concatenates the partial word “furni” and the additional keystrokes  416  to form a token “furniture” (e.g., a word or a term), and interprets the token according to the data source  258  and/or a lexicon  264  for the data source  258 . The process of tokenization is also described in U.S. patent application Ser. No. 16/166,125, filed Oct. 21, 2018, titled “Determining Levels of Detail for Data Visualizations Using Natural Language Constructs,” which incorporated by reference herein in its entirety. In this example, the natural language processing system  236  recognizes the word (e.g., term or token) “furniture” that has been formed. 
       FIG. 4C  also illustrates that in response to the formation of the token “furniture,” the graphical user interface  100  displays updated interpretations in the dropdown menu  408 . In this example, only the interpretation  410 - 2  corresponding to the data value  412 - 2  (“Furniture”) is displayed. The interpretation  410 - 1  is not displayed because the data value  412 - 1  “Furnishings” no longer matches the term “Furniture.” 
     Notably, the implementations described in  FIGS. 4B and 4C  distinguish over the implementation that is described in  FIG. 3 . In the example of  FIG. 3 , a user has to input a complete word or phrase (e.g., the phrase  304  “year over year sales”) before the graphical user interface  100  displays one or more interpretations  308 . On the other hand,  FIGS. 4B and 4C  illustrate that the interpretations  410  are constantly updated in response to each keystroke entered by the user. 
     In some implementations, the data visualization application  230  may determine with a high degree of confidence (e.g., with 80%, 85%, 90%, or 95% confidence) that a term in a natural language utterance should be interpreted as a particular entity in the data source. In accordance with the determination, the data visualization application  230  may automatically (i.e., without user invention) annotate the term so that it is interpreted as the particular entity. In some implementations, when a term is automatically annotated by the data visualization system, the data visualization application further determines analytical concepts corresponding to the interpretation. The data visualization application  230  also generates analytical expressions (e.g., phrases) corresponding to the interpretation (e.g., the analytical concepts). 
     Referring to  FIG. 4D , in some implementations, in response to an identification (e.g., recognition) of the token “furniture” by the data visualization application  230 , the data visualization application  230  may determine with high confidence that the term “furniture” corresponds to the data value “Furniture” of the data field “Category” in the data source.  FIG. 4D  illustrates a phrase  420  “filter Category to Furniture” is displayed in the filter interpretation region  404 . The phrase  420  is an analytical expression corresponding to the interpretation  410 - 2 . In this example, the natural language processing system  236  suggests a proposed action to add a filter operation that filters rows of the data source to include only those whose “Category” data field have the value “Furniture.”  FIG. 4D  also illustrates display of a default data field  418  (e.g., a phrase “count of Migrated Data (Count)”) in the data field interpretation region  402 . 
     In some implementations, the phrases that are displayed in the data field interpretation region  402  and/or in the filter interpretation region  404  are also known as full interpretations or complete interpretations. For example, in  FIG. 4D , in response to the partial natural language input “what are furniture”, the natural language processing system  236  generates a full interpretation (an ArkLang level of detail expression) consisting of two phrases (e.g., ArkLang analytical expressions) “count of Migrated Data (Count)” and “filter Category to furniture.” In some implementations, a phrase may comprise one or more proposed actions on entities of a data source. For example, as described above, the phrase  420  comprises a proposed action to add a filter. In some implementations, a phrase may be an analytical expression. The analytical expression may include one or more analytical operations. For example, the phrase  418  includes a count operation. The phrase  420  includes a filter operation. In some implementations, the interpretations that are displayed in the dropdown menu  408  (e.g., interpretations  410  in  FIGS. 4B, 4C, and 4D ) are also known as partial interpretations. 
     In some implementations, when a term in the partial natural language input is interpreted (e.g., by the natural language processing system  236 ), the term (in the input command box  124 ) and its corresponding interpretations (e.g., the phrases in the data field interpretation region  402  and/or in the filter interpretation region  404 ) are visually emphasized. 
     In the example of  FIG. 4D , the natural language processing system  236  interprets the term  422  (e.g., token) “furniture” in the input command box  124  as a data value  412 - 2  of the data field  414 - 2  “Category”. Accordingly, the natural language processing system  236  suggests a filter operation that filters rows of the data source to include only those whose “Category” data field have the value “Furniture”. When the graphical user interface  100  displays the phrase  420  “filter Category to Furniture” corresponding to the suggested filter operation, the term  422  and the phrase  420  are simultaneously pulsed. The  FIG. 4D  inset shows a pulse  424  (e.g., a beam, a wave, a shadow) emanating from the term  422  “Furniture.”  FIG. 4D  inset also shows a pulse  426  (e.g., a beam, a wave, a shadow) emanating from the phrase  420  “filter Category to Furniture.” In some implementations, the visual emphasis (e.g., pulsing) occurs in real time as the user inputs the natural language command. The visual emphasis (e.g., pulsing) draws attention to the user that a term in the natural language command has been interpreted a certain way. The user can also decide whether the interpretation by the data visualization application  230  is accurate (e.g., consistent with what the user has in mind) or if the interpretation requires further modification. 
     In some implementations, the term that has been automatically annotated and interpreted, as well as its corresponding phrases, may also be visually emphasized in some other manner in addition to (or as an alternative to) pulsing. For example, the term may be displayed in a manner (e.g., boldface, italicized, using a different font type, using a different font size, using a different font color, changing the size of the font as it is emphasized, highlighting, or adding a frame around the term) that is visually distinctive from other terms in the input command box  124 . The phrases corresponding to the term may be displayed in a manner (e.g., boldface, italicized, using a different font type, using a different font size, using a different font color, changing the size of the font as it is emphasized, highlighting, or adding a frame around the phrase) that are visually distinctive from other phrases in the data field interpretation region  402  and/or the filter interpretation region  404 , which do not map to the interpreted term. 
     In some implementations, the data visualization application  230  visually de-emphasizes one of more terms (e.g., tokens) in the partial natural language command that have not been mapped to a phrase. For example, in  FIG. 4D , the terms “what are” in the natural language expression are visually de-emphasized (e.g., grayed out) because they are not related to the default count  418  or the default filter  420 . Stated another way, visually de-emphasized terms do not map to a phrase (an analytical expression) in the interpretation region. In some implementations, the presence of a visually de-emphasized term informs the user that the natural language processing system  236  does not use the term when generating interpretations for the natural language command. Accordingly, in this example, instead of the natural language command “what are furniture,” a user can simply input “furniture” to obtain the interpretation  410 - 2 , the phrase  418 , and the phrase  420 . 
     In some implementations, when one or more terms in the partial natural language command include one or more terms that are recognized by the natural language system  236  (e.g., after the graphical user interface  100  displays interpretations such as the interpretations  410 - 2 , the default count  418  and/or the default filter  420 ), the graphical control element  401  becomes activated. A user may at this point select the graphical control element  401  to cause the partial natural language command in the input box  124  to be transmitted to the data visualization application  230  for analysis (e.g., using the full interpretation as defined by the default count  418  and/or the default filter  420 ). Alternatively, the user may continue to modify the partial language command, such as input additional keystrokes, modify or delete existing terms, and/or select an alternative interpretation. 
       FIG. 4E  illustrates a user interaction with the graphical user interface  100 . In this example, the user inputs an additional term  428  (“sales”) after the partial natural language input “what are furniture”. In response to the user interaction, the dropdown menu displays one interpretation  430  “Sales,” which is a measure. In this example, the data visualization application  230  automatically annotates the term  428  “Sales,” which instructs the data visualization application  230  to interpret the term  428  as the measure “Sales” in the data source. 
     As also illustrated in  FIG. 4E , in response to the annotation, the graphical user interface  100  replaces the default count  418  in the data field interpretation region  402  with an updated data field interpretation  432  (e.g., a phrase) “sum of Sales”, corresponding to an aggregation of the measure “Sales” (the interpretation  430 ). In some implementations, when the graphical user interface  100  displays the phrase  432 , the data visualization application  230  also simultaneously visually emphasizes the phrase  432  and its corresponding term  428  (e.g., using the various implementations that are discussed in  FIG. 4D ). 
     Referring again to  FIG. 4E , in some implementations, when a user inputs a query (e.g., “what are furniture sales”), the data visualization application  230  (e.g., the natural language system  236 ) interprets the query (or more specifically, interprets the non-grayed out terms “furniture sales”). In some implementations, the data visualization application  230  maps the terms to one or more concepts in a lexicon (e.g., the grammar lexicon  262  and/or the data source lexicon  264 ). The natural language system  236  determines a respective interpretation for the each of the tokens (e.g., the tokens “furniture” and “sales”). In this example, the token furniture is interpreted as a data value of the data field “Category.” The token “sales” is interpreted as a data field. The natural language system  236  also generates a full interpretation that comprises a combination of the respective interpretations. In  FIG. 4E , the full interpretation is represented by the phrase  432  “sum of Sales” and the phrase  420  “filter Category to Furniture.” The phrase  420  is a proposed action to filter data rows of the data source to include only those whose “Category” data field have the value “Furniture.” The phrase  432  is a proposed action to aggregate data for the data field “Sales.” 
     In some implementations, as illustrated in  FIGS. 4D and 4E , the position of the dropdown menu  408  changes based on the position of a most recently entered term (e.g., word or keystrokes) in the natural language input box  124 . In  FIG. 4D , the dropdown menu  408  is located immediately below the term “furniture.” In  FIG. 4E , the dropdown menu  408  is displaced further to the right compared to that in  FIG. 4D , and is located immediately below the most recently entered term “sales”. Stated another way, the dropdown menu  408  is located immediately below the substring of the input text that corresponds to the displayed search results (e.g., the input text “furniture” in  FIG. 4D  and the input text “sales” in  FIG. 4E ). 
       FIG. 4F  illustrates a user interaction with the graphical user interface  100 . In this example, the user inputs additional terms  434  “from California” to the partial natural language command “what are furniture sales”.  FIG. 4F  also illustrates in response to the user input of the most recently entered term “California,” the dropdown menu  408  displays an interpretation  436 , which suggests that the term “California” corresponds to a data value  438  “California” in the data field  440  “State.” 
     In some implementations, the natural language processing system  236  maps the term “California” to concepts in a lexicon (e.g., the grammar lexicon  262  and/or the data source lexicon  264 ). The natural language processing system  236  determines with high confidence that the term “California” is a value of the data filed “State” and automatically annotates the term so that it is interpreted as such. In some implementations, and illustrated in  FIG. 4G , in response to the determination (and automatic annotation), the graphical user interface  100  displays a second phrase  442  (“with State in California”) corresponding to the interpretation  436 . In this example, the phrase  442  includes a proposed action to filter values of “State” to the value “California.” 
     In some implementations, when the phrase  442  is displayed, the phrase  442  and its corresponding term (in the user input box  124 ) are visually emphasized. As illustrated in the  FIG. 4G  inset, the term “California” in the user input box  124  is visually emphasized by including a frame  444  around the term “California” and pulsing the term. The shadow  448  surrounding the frame  444  (surrounding the term “California”) illustrates the pulsing effect on the term. In some implementations, the pulsing effect comprises a pulse that originates from the center of the frame  444  (the center of the term “California”) and travels toward the two ends of the frame  444 . In some implementations, the pulsing effect comprises a pulse that originates from one end of the frame  444  and travels along the length of the frame  444  to the other end. 
     The  FIG. 4G  inset also illustrates visual emphasis of the phrase  442 . In this example, the visual emphasis of the phrase  442  comprises a pulsing effect, as illustrated by the shadow  446  surrounding the phrase  442 . In some implementations, the graphical user interface  100  visually emphasizes both the term and its corresponding phrase simultaneously. In some implementations, visual emphasis of the term and its corresponding phrase occur sequentially. For example, the graphical user interface  100  may first display the phrase in a visually emphasized manner, and then proceed to display the term corresponding to the phrase in a visually emphasized manner. Alternatively, the graphical user interface  100  may first display the term in a visually emphasized manner, and then proceed to display the corresponding phrase in a visually emphasized manner. In some implementations, visually emphasizing the term and the corresponding phrase includes applying the same visual effect to both the term and the phrase. For example, both the term and the phrase are pulsed. In some implementations, the term and the corresponding phrase may be visually emphasized using different visual effects. For example, the term in the input command box  124  may be highlighted while its corresponding phrase may be simultaneously pulsed. 
       FIG. 4G  also illustrates that the term “from” in the natural language input query is visually de-emphasized (e.g., grayed out). This suggests that the term “from” is not mapped to any of the phrases  432  (“sum of Sales”),  442  (“with State in California”), or  420  (“filter Category to Furniture”). 
       FIG. 4H  illustrates another user interaction with the graphical user interface  100 . In this example, the user appends additional terms  450  (“in 2018”) to the partial natural input query (e.g., “what are furniture sales from California”).  FIG. 4H  also illustrates the dropdown menu  408  displaying interpretations  452  in response to a most recently entered term (the term “ 2018 ”) of the additional terms  450  (“in 2018”). In this example, the interpretations  452  include a first interpretation  452 - 1  corresponding to the number 2,018 (a numerical value). The interpretations  452  also include a second interpretation  452 - 2  corresponding to the date value “2018”. 
       FIG. 4I  illustrates the display of a phrase  454  (“with Order Date in 2018”) in response to a determination (and automatic annotation) that the most recently entered term “2018” corresponds to a date data value (the second interpretation  452 - 2 ). The inset of  FIG. 4I  also shows the phrase  454  and the term  458  (“2018”) corresponding to the phrase  454  are simultaneously visually emphasized (e.g., with a pulsing effect). In this example, the pulsing effects on the phrase  454  and the term  458  corresponding to the phrase  454  are illustrated by a shadow  456  surrounding the phrase  454  and by a shadow  460  surrounding the term  458 . 
     In some implementations, a phrase includes a term and an analytical concept for the term. For example, in  FIG. 4I , the phrase  454  “with Order Date in 2018” is a filter expression  244  that includes the term “2018” and includes a concept to filter values of the Order Date field to the year 2018. As discussed earlier with respect to  FIG. 2B , in some implementations, the filter expressions  244  have the canonical form [att filter val], where att is an attribute, filter ∈ Filters, and val ∈ Values. In some implementations, when a natural language input appears underspecified, the natural language system  236  assigns one or more default fields to the underspecified input to generate a phrase. In this example, the natural language system  236  maps “in 2018” to “filter val” and selects the data field “Order Date” as the default attribute. 
     In some implementations, when a user interacts with (e.g., hovers over and/or clicks on) a term in the natural language input box  124 , the graphical user interface  100  maintains display of the phrases corresponding to the term while visually de-emphasizing other phrases that do not correspond to the term.  FIGS. 4J, 4K, 4L, and 4M  illustrate this behavior. 
       FIG. 4J  shows a user hovering over ( 462 ) (and/or clicking on) the term  422  (“furniture”) in the natural language input box  124 . In response to the user interaction, display of the phrase  420  (e.g., “filter Category to Furniture”) corresponding to the term  422  “furniture” is maintained. Other phrases (the phrase  432  “sum of sales,” the phrase  454  “with Order Date in 2018,” and the phrase  442  “State in California”) in the interpretation region that do not correspond to (e.g., not mapped to or not related to) the term  422  are visually de-emphasized (e.g., grayed out or faded). 
     In  FIG. 4K , in response to a user hovering ( 464 ) over the term  428  “sales” in the natural language input box  124 , display of the phrase  432  “sum of sales” corresponding to the term  428  “sales” is maintained. Other phrases (the phrase  454  “with Order Date in 2018,” the phrase  442  “State in California,” and the phrase  420  “filter Category to Furniture”) in the interpretation region that are not related to (e.g., not mapped to) the term  420  are visually de-emphasized. 
       FIGS. 4L and 4M  illustrate effects that are similar to those described in  FIGS. 4J and 4K . For example,  FIG. 4L  illustrates in response to a user hovering ( 466 ) over the term  444  “California,” display of the phrase  442  corresponding to the term “California” is maintained whereas the phrases  432 ,  454 , and  420  not related to the term “California” are visually de-emphasized. 
     In some implementations, and as illustrated in  FIG. 4M  and the inset, in response to user hovering ( 468 ) over a term  458  (“2018”), the graphical user interface  100  displays a frame  470  around the terms “in 2018.” That is to say, the data visualization application  230  groups the term  458  “2018” and the adjacent term “in” because these two terms collectively map to the phrase  454  “with Order Date in 2018.” The graphical user interface  100  also maintains display of the phrase  454  “with Order Date in 2018” while visually de-emphasizing the phrases  432 ,  442 , and  420 . 
       FIGS. 4J to 4M  illustrate how a user can interact with (e.g., hover over) a term (a token) in a natural language command and visually correlate the term and the phrases to which the term has been mapped. Any unexpected or incorrect mapping between a term and the phrases will be immediately visible to the user, thus enabling prompt correction (e.g., the user can delete an erroneous phrase and/or modify the natural language input). 
       FIGS. 4J to 4M  also illustrate the phrases in the interpretation region may be ordered differently from the order of the terms in the natural language command. For example, the phrase  454  “with Order Date in 2018” appears as the first phrase in the filter interpretation region  404  whereas the terms “in 2018” that are mapped to the phrase are the last two terms in the natural language command. 
       FIG. 4N  illustrates user selection ( 472 ) of the graphical control element  401 . As discussed earlier, user selection of the graphical control element causes the natural language command (e.g., “what are furniture sales from California in 2018”) to be transmitted to the data visualization application  230 . In this example, the data visualization application  230  has already interpreted the terms “furniture,” “sales,” “California,” and “in 2018” as respective particular entities in the data source  258 . The data visualization application  230  has also generated analytical expressions (phrases)  432 ,  454 ,  442 , and  420  corresponding to the interpretation. The data visualization application  230  will use the assigned meanings for these terms and does not have to identify other meanings for these terms as it is synthesizing a viable meaning for the entire phrase. 
     In some implementations, after determining an interpretation for a term (e.g., a token), the computing device  200  assigns an index to the term (e.g., the phrase to which it applies) and stores the term, the interpretation, and/or one or more analytical expressions (phrases) corresponding to the interpretation.  FIG. 4O  illustrates a representative table (data structure)  480  for the natural language query “what are furniture sales from California in 2018” that may be stored by the computing device  200 . In this example, the table  480  only includes terms that are understood (have been interpreted) by the data visualization application. The “Term (Token) Number” heading refers to the order of the term in the natural language query. For example, the term “furniture” is the third word in the query and therefore its term (token) number is 3. The index identifies a phrase. In this example, both “in” and “2018” apply to the same phrase, so they both have the same index value  4 . Some implementations also store a position number within each phrase (e.g., “in” is at position  3  in the phrase “with Order Date in 2018”, whereas “2018” is at position  4 ; because “Order Date” is one field name, it counts as just one position). 
       FIGS. 5A-5K  provide a series of screen shots for interactions with a graphical user interface  100  according to some implementations. 
       FIG. 5A  illustrates a user interaction with the graphical user interface  100 . In this example, the user inputs a natural language command  501  (e.g., “which industry has the most sales”). In response to the natural language command  501 , the graphical user interface  100  displays analytical expressions (phrases)  502  (“Count of Orders”),  504  (“by City”), and  506  (“top City by Sum of Sales”). 
       FIG. 5A  also illustrates that the natural language command  501  includes terms (e.g., “which industry has the”) that are visually de-emphasized (grayed out). As described earlier with reference to  FIG. 4 , visually de-emphasized terms in a natural language command do not map to a phrase (an analytical expression) in the interpretation region. Accordingly, the data visualization application  230  is interpreting just the terms “most sales” in the natural language command  501  and the phrases  502 ,  504 , and  506  are phrases corresponding to the terms “most sales.” The visually de-emphasized terms informs the user that the query is not being interpreted the way it is supposed to be because the word “industry” has not been recognized. 
       FIG. 5B  illustrates another user interaction with the graphical user interface  100 . In this example, the user selects ( 505 ) a visually de-emphasized term  509  “industry.” In response to the user selection, the graphical user interface  100  displays a dropdown menu  507  that includes an indication  508  that there is no entity corresponding to term  509  (e.g., “No results”). The dropdown menu  507  also displays a prompt  510  (e.g., “Teach Ask Data what ‘industry’ means”) requesting the user to teach (e.g., train) the data visualization application  230  to interpret terms that are unknown to the data visualization application  230 . 
       FIG. 5C  illustrates user selection ( 512 ) of the prompt  510 . 
       FIG. 5D  illustrates in response to the user selection, the dropdown menu  507  displays an input box  514 , and a button  515  to save the user input. 
       FIG. 5E  illustrates user interaction with the input box  514 . In this example, the user inputs keystrokes  516  (e.g., the keystrokes “se”) into the input box  514 . In response to the user interaction, the graphical user interface  100  displays suggested interpretations  518  (e.g., entities in the data source). Each of the suggested interpretations includes the letters “se” of the keystrokes  516 . 
       FIG. 5F  illustrates a user interaction ( 520 ) (e.g., hovering over) with a first suggested interpretation  518 - 1  (the data field “Segment”). In response to the user interaction, the graphical user interface  100  also displays a window  522  that contains details about the data field “Segment,” including the number of distinct data values of the data field  524 , the data values  526  of the data field “Segment,” and a respective frequency  528  of each of the data values  526 . 
       FIG. 5G  illustrates in response to user selection of the first suggested interpretation  518 - 1  (e.g., in response to user selection of the “Apply”  515  affordance), the data visualization application  230  replaces the term  509  “industry” that was in the natural language command  501  with the user specified term  530  “Segment.”  FIG. 5G  also illustrates that the interpretation region displays updated phrases in response to the user selection. In this example, the phrase  504  “by City” and the phrase  506  “top City by Sum of Sales” are replaced with a first updated phrase  532  “by Segment” and a second updated phrase  534  “top Segment by Sum of Sales.” 
     In some implementations, user selection of a suggested interpretation causes the data visualization application  230  to store the selected entity as a synonym of the unknown term. For example, in  FIG. 5G , user selection of the interpretation  518 - 1  “Segment” causes the data visualization application  230  to store the data field “Segment” as a synonym of the previously unknown term “industry.” 
       FIG. 5H  illustrates a subsequent user interaction with the graphical user interface  100 . In this example, the user enters in the input box  124  a new query  536  (a natural language command), which includes the terms “which industry.” In response to the query  536  (e.g., to the most recently entered term “industry”), the graphical user interface  100  displays an interpretation  538  (“Segment”), which corresponds to the data field “Segment” that the user had previously defined in  FIGS. 5F and 5G . The graphical user interface  100  also displays an interpretation for the query  536 , which is defined by a first phrase  540  (“count of Orders”) and a second phrase  542  (“by Segment”). 
       FIG. 5I  illustrates continued user interaction with the graphical user interface  100 . In this example, the user appends additional terms  544  (“has the most sales”) to the query  536 . Of the additional terms  544 , the terms “has the” are grayed out, which suggests that they are not recognized (or understood) by the natural language processing system  236  and do not contribute to the system&#39;s interpretation of the natural input query. On the other hand, the terms “most sales” of the additional terms  544  are not grayed out. This suggests that the natural language processing system  236  understands (recognizes) the terms “most sales” and generates interpretations for the terms.  FIG. 5I  also illustrates display of an additional phrase  546  (e.g., “top Segment by sum of Sales”) in response to an interpretation of the terms “most sales” by the data visualization application  230 . 
       FIG. 5J  illustrates a user interaction with the natural language command. In this example, the user hovers ( 548 ) over the term “industry.” In response to the hovering over, the graphical user interface  100  displays a frame  550  around the terms “which industry.” The graphical user interface  100  also maintains display of the phrase  542  “by segment” and the phrase  546  “top Segment by sum of Sales.” The graphical user interface  100  also visually de-emphasizes the phrase  540  “count of Orders.” This suggests that the terms “which industry” in the natural language command collectively map to (e.g., correspond to) the phrases  546  and  542  and are not related to the phrase  540 . 
       FIG. 5K  illustrates another user interaction with the natural language command. In this example, the user hovers ( 552 ) over the term “sales.” In response to the user interaction, the graphical user interface  100  displays a frame  554  around the terms “most sales” and displays the phrase  546  “top Segment by sum of Sales” corresponding to the terms “most sales.” 
       FIGS. 6A-6D  provide a flowchart of a method  600 . The method  600  is also called a process. 
     The method  600  is performed ( 602 ) at a computing device  200  that has a display  212 , one or more processors  202 , and memory  206 . The memory  206  stores ( 604 ) one or more programs configured for execution by the one or more processors  202 . In some implementations, the operations shown in  FIGS. 4A to 4O  and  FIGS. 5A to 5K  correspond to instructions stored in the memory  206  or other non-transitory computer-readable storage medium. The computer-readable storage medium may include a magnetic or optical disk storage device, solid state storage devices such as Flash memory, or other non-volatile memory device or devices. The instructions stored on the computer-readable storage medium may include one or more of: source code, assembly language code, object code, or other instruction format that is interpreted by one or more processors. Some operations in the method  500  may be combined and/or the order of some operations may be changed. 
     The computing device  200  receives ( 606 ) from a user a partial natural language input (e.g., a query) related to a data source  258 . The partial natural language input includes ( 608 ) a most recently entered first term. 
     For example, in  FIG. 4C , the computing device  200  receives from a user a partial natural language input “what are furniture” related to a data source  258 . The partial natural language input includes the most recently entered first term “furniture.” 
     In some implementations, the first term may consist of one word (e.g., “furniture”). The first term may also consist of two words separated by a space (e.g., “San Francisco”). The first term may also consist of three or more words (e.g., “same day shipping”). The first term may also comprise a phrase (e.g., “furniture sales in California”), numbers (e.g., 2018), and/or a combination of letters and numbers (e.g., Just4U). The first term may also consist of a partial word (e.g., letters “furni”). 
     Referring back to  FIG. 6A , in response ( 610 ) to receiving the first term, the computing device  200  generates ( 612 ) a first token that includes the first term. 
     For example, in  FIG. 4C , the computing device  200  generates a first token that includes the first term “furniture.” 
     In some implementations, generating the first token includes concatenating ( 614 ) the first term with another term that immediately precedes the first term in the partial natural language input. 
     In some implementations, generating the first token includes concatenating ( 616 ) the first term with another term that immediately follows the first term in the partial natural language input. 
     As one example, a partial natural language input may include two terms such as “San” and “Francisco” that are adjacent to each other. In this scenario, the computing device  200  generates a token “San Francisco” by concatenating both the terms “San” and “Francisco.” As another example, the partial natural language input may also include three consecutive terms such as “Napa,” “Valley,” and “wineries.” In this scenario, the computing device  200  generates a token “Napa Valley wineries” by concatenating the three terms “Napa,” “Valley,” and “wineries.” In some implementations, the multiple terms in a token are treated as one indivisible token term. 
     Referring back to  FIG. 6A , the computing device  200  maps ( 618 ) the first token to one or more analytical concepts  266  in a lexicon of the data source (e.g., a grammar lexicon  262  and/or a data source lexicon  264 ). 
     In some instances, the first token consists of one a single word such as “Sales” or “furniture.” The computing device  200  analyzes each keystroke as it is being input by the user, concatenates the keystrokes that form the word, and maps the word to an analytical concept. For example, in  FIG. 4C , the computing device  200  concatenates the keystrokes “furni” in the partial natural language input  406  and keystrokes  416  “ture” to form the word “furniture.” The computing device  200  also maps (interprets) the word “furniture” as a data value of the data field “Category” from the data source  258 . 
     The computing device  200  determines ( 622 ) a first interpretation corresponding to the first token. 
     The computing device  200  displays ( 624 ) a first phrase corresponding to the first interpretation. The first phrase includes ( 626 ) the first term. 
     For example, in  FIG. 4C , the computing device  200  displays a first phrase  420  “filter Category to furniture” corresponding to the first interpretation. The first phrase includes ( 626 ) the first term “furniture.” 
     In some implementations, the first phrase includes ( 628 ) an analytical concept corresponding to the first term. 
     In some implementations, the analytical concept includes an aggregation, a group, a filter, a limit, a sort, a field (e.g., a data field), or a value (e.g., a data value of a data field). 
     For example, in  FIG. 4E , the phrase  432  “sum of Sales” corresponding to the term  428  “sales” includes an aggregation of the data field “Sales.” 
     In some implementations, displaying the first phrase further comprises simultaneously ( 630 ) visually emphasizing the first term and the first phrase. 
     For example, in  FIG. 4G , displaying the phrase  442  “with State in California” corresponding to the term  444  “California” comprises simultaneously visually emphasizing the term  444  and the phrase  442 . 
     In some implementations, visually emphasizing the first term and the phrase comprises displaying the first term and the first interpretation in a visually distinctive manner (e.g., boldface, italicized, using a font type, font size, and/or font color that is different from other terms and/or phrases in the graphical user interface  100 ). In some implementations, visually emphasizing the term and the phrase includes highlighting the term and/or the phrase, or adding a frame around the term (e.g., frame  470 ,  FIG. 4M ). 
     In some implementations, simultaneously visually emphasizing the first term and the first phrase includes simultaneously pulsing ( 632 ) the first term and the first phrase. This is illustrated in  FIGS. 4D, 4G, and 4I . 
     In some instances, the partial natural language input consists ( 634 ) of a plurality of terms including the first term. In some implementations, visually emphasizing the first term further comprises ( 636 ) visually de-emphasizing one or more other terms in the plurality of terms. 
     For example, in  FIG. 4D , the partial natural language input consists of the terms “what,” “are,” and “furniture.” The plurality of terms includes the term “furniture.” Visually emphasizing the term furniture” further comprises visually de-emphasizing the other terms “what” and “are” in the plurality of terms. 
     In some implementations, and as illustrated in  FIGS. 4D to 4I , visually de-emphasizing one or more other terms includes graying out the other terms. In some implementations, the visually de-emphasized terms correspond to terms in the partial natural language command that have not been mapped to a phrase in the interpretation region. In some implementations, the visually de-emphasized terms are terms that are not interpreted (e.g., not understood or not recognized) by the data visualization application  230 . 
     In some implementations, the partial natural language input is received ( 638 ) in a first region of a graphical user interface  100 . The first phrase is displayed ( 640 ) in a second region of the graphical user interface, distinct from the first region. 
     For example, as illustrated in  FIGS. 4D to 4O , the partial natural language input is received in a command box  124  of the graphical user interface  100 . The phrases are displayed in a fields interpretation region  402  and/or a filters interpretation region  404  (or collectively referred to as a field interpretation region), which is distinct from the command box  124 . 
     In some instances, the visually de-emphasized one or more other terms include ( 642 ) a second term. The computing device  200  receives ( 644 ) user selection of the second term. The computing device  200  receives ( 646 ) user input specifying a data field of the data source. In some implementations, in response to the user input, the computing device stores ( 648 ) the second term as a synonym of the data field. 
     For example, in  FIG. 5A , the visually de-emphasized terms “which industry has the” include the term “industry.” In  FIG. 5B , the computing device  200  receives user selection of the term “industry.” In  FIG. 5F , the computing device  200  receives user input specifying the data field “Segment” of the data source. In response to the user input, the computing device stores ( 648 ) the term “industry” as a synonym of the data field “Segment.” 
     In some implementations, storing the second item as a synonym of the data field includes updating the lexicon to associate the second term with the specified data field. 
     In some instances, after the storing, the computing device  200  receives ( 650 ) from the user a second partial natural language command including the second term. In response to receiving the second term, the computing device  200  displays ( 652 ) a second phrase corresponding to the second term. The second phrase includes ( 654 ) the data field. 
     For example, in  FIG. 5H , the computing device receives from the user a partial natural language command  536  “which industry” that includes the term “industry.” In response to receiving the term, the computing device  200  displays a phrase  542  “by Segment” corresponding to the term “industry.” The phrase  542  includes the data field “Segment.” 
     In some implementations, after determining the first interpretation corresponding to the first token, the computing device  200  assigns ( 656 ) a first index to the first token. The computing device  200  updates ( 658 ) a data structure that includes the first index and the first token. This is illustrated in  FIG. 4O . 
     In some implementations, the computing device  200  receives ( 660 ) additional user input to the partial natural language input. The additional user input includes ( 662 ) a third term. In response ( 664 ) to receiving the third term, the computing device  200  generates ( 666 ) a third token that includes the third term. The computing device  200  maps ( 668 ) the third token to one or more analytical concepts in the lexicon. The computing device  200  determines ( 670 ) a third interpretation corresponding to the third token. The computing device  200  also displays ( 672 ) in a graphical user interface a third phrase corresponding to the third interpretation. 
     For example, in  FIG. 4H , the computing device  200  receives additional user input “in 2018” to the partial natural language input “what are furniture sales from california.” The additional user input includes the term “2018.” In response to receiving the term “2018,” the computing device  200  generates a third token (“2018”) that includes the term “2018.” The computing device  200  maps the third token to one or more analytical concepts in the lexicon (e.g., maps the token “2018” to a year that can be applied to a date data field). The computing device  200  determines an interpretation corresponding to the token. The computing device  200  also displays ( 672 ) in a graphical user interface a phrase  454  “with Order Date in 2018” corresponding to the interpretation. 
     In some implementations, the additional user input is appended to the partial natural language input. For example, in  FIG. 4H , the additional user input “in 2018” is appended to an existing partial natural language input “what are furniture sales from California.” In some implementations, the additional user input comprises a modification of an existing term of the partial natural language input. For example, the partial natural language input may comprise “sales of furniture in California” and the additional user input may comprise replacing the term “furniture” with “chairs,” and hence the modified natural language input query becomes “sales of chairs in California.” In this scenario, the natural language processing system  236  (e.g., a parser of the system  236 ) is still going to parse the entire query, but it is going to skip the tokenization process for tokens that have already been annotated (e.g., tokens “sales” and “California”). In some implementations, the natural processing system  236  will process the modified, tokenized query through a grammar parser (e.g., ArkLang), which then determines how to combine the tokens in expressions that are grammatically sound and fully specified. 
     In some instances, the computing device  200  receives ( 674 ) a user interaction with the first term. In response to the user interaction, the computing device  200  maintains ( 676 ) visual display of the first phrase while visually de-emphasizing the third phrase. 
     For example, in  FIG. 4L , the computing device  200  receives a user interaction comprises a hovering over ( 466 ) the term “California.” In response to the user interaction, the computing device  200  maintains visual display of the phrase  442  “State in California” corresponding to the term “California” while visually de-emphasizing the phrases  432 ,  454 , and  420 . 
     In some instances, after determining the first interpretation, the computing device  200  persists ( 678 ) the first interpretation. The third interpretation includes ( 680 ) the first interpretation. 
     For example, in  FIG. 5H , the computing device determines a first interpretation (the data field “Segment”) corresponding to the term “industry.” The computing device  200  persists the first interpretation. The computing device  200  determines a third interpretation corresponding to the term “top sales.” The phrase  546  “top Segment by sum of sales” corresponding to the third interpretation includes the first interpretation “Segment.” 
     Each of the above identified executable modules, applications, or sets of procedures may be stored in one or more of the previously mentioned memory devices, and corresponds to a set of instructions for performing a function described above. The above identified modules or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various implementations. In some implementations, the memory  206  stores a subset of the modules and data structures identified above. Furthermore, the memory  206  may store additional modules or data structures not described above. 
     The terminology used in the description of the invention herein is for the purpose of describing particular implementations only and is not intended to be limiting of the invention. As used in the description of the invention and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. 
     The foregoing description, for purpose of explanation, has been described with reference to specific implementations. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The implementations were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various implementations with various modifications as are suited to the particular use contemplated.