The present disclosure is directed to a spreadsheet application with an enhanced understanding of mathematical equations using a mathematical engine and a calculation engine. The disclosed spreadsheet application has many enhanced features, such as, but not limited to: analyzing complex equations, identifying dependencies, converting mathematical notation to spreadsheet notation, connecting variables in an equation to other cells of the spreadsheet, solving or simplifying equations, recommending different display representations of data, and a providing a math display mode.

A computer-implemented method according to one embodiment includes identifying a textual document, determining chemical data within the textual document, performing an analysis of the chemical data to identify a chemical pathway, and calculating a probability score for the chemical pathway.

A computer-implemented method according to one embodiment includes identifying a textual document, determining chemical data within the textual document, performing an analysis of the chemical data to identify a chemical pathway, and calculating a probability score for the chemical pathway.

An information processing method includes displaying a graph on a display screen of a display, in response to a user operation of specifying at least part of the graph, displaying an icon corresponding to a numerical value which is associated with the at least part of the graph on the display screen, in response to a user operation of selecting the icon, as at least part of a mathematical expression to execute calculation using the numerical value which is associated with the icon selected, displaying the numerical value or a variable indicating the numerical value which is associated with the icon on the display screen.

An information processing method includes displaying a graph on a display screen of a display, in response to a user operation of specifying at least part of the graph, displaying an icon corresponding to a numerical value which is associated with the at least part of the graph on the display screen, in response to a user operation of selecting the icon, as at least part of a mathematical expression to execute calculation using the numerical value which is associated with the icon selected, displaying the numerical value or a variable indicating the numerical value which is associated with the icon on the display screen.

Techniques described herein provide intelligent context-based testing. One or more implementations receive an image that includes content. In turn, some implementations process the image to extract test information from the content, such as questions, answers, learning material, and so forth. By analyzing the test information, various implementations determine one or more characteristics associated with the test information, and dynamically generate new test information based on the determined one or more characteristics. As one example, some implementations obtain new content by searching for content that includes the one or more characteristics and generate new test information based on the new content and the extracted test information.

The present disclosure is directed to using subcells to display both text and mathematically evaluable information within a cell of a spreadsheet. An algorithm can be applied to user-specified cell data to determine if the data are mathematically evaluable or not. If the user-specified cell data are not mathematically evaluable, such as the case when the data are text characters, the data are displayed within the cell as input. If the user-specified cell data are mathematically evaluable, such as the case when the data are a mathematical expression, the data can be evaluated and the resulting solution displayed within the cell. The cell can be used as a portion of a mathematical expression associated with a different cell, wherein the text data may be ignored and the solution to the mathematical expression used to solve the mathematical expression associated with the different cell.

The present disclosure is directed to using subcells to display both text and mathematically evaluable information within a cell of a spreadsheet. An algorithm can be applied to user-specified cell data to determine if the data are mathematically evaluable or not. If the user-specified cell data are not mathematically evaluable, such as the case when the data are text characters, the data are displayed within the cell as input. If the user-specified cell data are mathematically evaluable, such as the case when the data are a mathematical expression, the data can be evaluated and the resulting solution displayed within the cell. The cell can be used as a portion of a mathematical expression associated with a different cell, wherein the text data may be ignored and the solution to the mathematical expression used to solve the mathematical expression associated with the different cell.

A text preparation apparatus is configured to in the decoding processing: perform first-layer recurrent neural network processing for phrase types to be used in the text and second-layer recurrent neural network processing for words appropriate for each of the phrase types; determine a phrase appropriate for each of the phrase types based on outputs of the second-layer recurrent neural network processing; generate a first vector set from a state vector of a previous step in the first-layer recurrent neural network processing and the feature vector sets, each vector of the first vector set being generated based on similarity degrees between individual vectors in one of the feature vector sets and the state vector; generate a second vector based on similarity degrees between individual vectors in the first vector set and the state vector; and input the second vector to a given step in the first-layer recurrent neural network processing.

A text preparation apparatus is configured to in the decoding processing: perform first-layer recurrent neural network processing for phrase types to be used in the text and second-layer recurrent neural network processing for words appropriate for each of the phrase types; determine a phrase appropriate for each of the phrase types based on outputs of the second-layer recurrent neural network processing; generate a first vector set from a state vector of a previous step in the first-layer recurrent neural network processing and the feature vector sets, each vector of the first vector set being generated based on similarity degrees between individual vectors in one of the feature vector sets and the state vector; generate a second vector based on similarity degrees between individual vectors in the first vector set and the state vector; and input the second vector to a given step in the first-layer recurrent neural network processing.