Abstract: Techniques for generated regular expressions are disclosed. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence. A negative example may be used to generate the regular expression. Context from the negative example may be determined in order to generate the regular expression.

Abstract: Disclosed herein are techniques related to automated generation of regular expressions. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence.

Abstract: Disclosed herein are techniques related to automated generation of regular expressions. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence.

Abstract: Techniques for generated regular expressions are disclosed. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence. Alignment of span data structures may be performed when generating the regular expression.

Abstract: Techniques are disclosed for providing adaptive recommendations for a data set. A data set can include one or more columns of data. The data set can be profiled in order to identify actions that can be applied to the data in order to enrich the data. The data set and actions that were applied to the data set can be stored. Actions that are applied to subsequent data sets can take into account the actions that were applied to prior data sets having similar profiles.

Abstract: Disclosed herein are techniques related to automated generation of regular expressions. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence.

Abstract: Techniques are provided for performing sentiment analysis on words in a first data set. An example embodiment includes generating a word embedding model including a first plurality of features. A value indicating sentiment for the words in the first data set can be determined using a convolutional neural network (CNN). A second plurality of features are generated based on bigrams identified in the data set. The bigrams can be generated using a co-occurrence graph. The model is updated to include the second plurality of features, and sentiment analysis can be performed on a second data set using the updated model.

Abstract: Techniques for generated regular expressions are disclosed. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence. Generation of the regular expressions can be implemented on an interactive user interface. Commands can be applied to the one or more character sequences and regular expressions are generated based on the applied commands.

Abstract: Disclosed herein are techniques related to automated generation of regular expressions. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence.

Abstract: Disclosed herein are techniques related to automated generation of regular expressions. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence.

Abstract: Disclosed herein are techniques related to automated generation of regular expressions. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence.

Abstract: Disclosed herein are techniques related to automated generation of regular expressions. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence.

Abstract: Techniques are disclosed for toponym disambiguation. Toponym disambiguation can be performed for a set of geographic location data, such as placenames. A subset of the data and additional location information associated with the subset of the data can be initially determined. The remaining geographic location data in the set of geographic location data can be scored in order to determined additional location information for the remaining geographic location data. Additional location information for the remaining geographic location data can be determined based on calculated scores.

Abstract: Techniques are disclosed for providing adaptive recommendations for a data set. A data set can include one or more columns of data. The data set can be profiled in order to identify actions that can be applied to the data in order to enrich the data. The data set and actions that were applied to the data set can be stored. Actions that are applied to subsequent data sets can take into account the actions that were applied to prior data sets having similar profiles.

Abstract: Techniques are disclosed for providing adaptive recommendations for a data set. A data set can include one or more columns of data. The data set can be profiled in order to identify actions that can be applied to the data in order to enrich the data. The data set and actions that were applied to the data set can be stored. Actions that are applied to subsequent data sets can take into account the actions that were applied to prior data sets having similar profiles.

Abstract: Techniques are disclosed for standardization of data. According to a first technique, standard representation terms are determined for to-be-standardized data using the to-be-standardized data itself and without using any external reference data. According to a second technique, a combination of the to-be-standardized data and an external reference is used to determine standard representation terms for the to-be-standardized data.

Abstract: Techniques are provided for performing sentiment analysis on words in a first data set. An example embodiment includes generating a word embedding model including a first plurality of features. A value indicating sentiment for the words in the first data set can be determined using a convolutional neural network (CNN). A second plurality of features are generated based on bigrams identified in the data set. The bigrams can be generated using a co-occurrence graph. The model is updated to include the second plurality of features, and sentiment analysis can be performed on a second data set using the updated model.

Abstract: Techniques are provided for performing sentiment analysis on words in a first data set. An example embodiment includes generating a word embedding model including a first plurality of features. A value indicating sentiment for the words in the first data set can be determined using a convolutional neural network (CNN). A second plurality of features are generated based on bigrams identified in the data set. The bigrams can be generated using a co-occurrence graph. The model is updated to include the second plurality of features, and sentiment analysis can be performed on a second data set using the updated model.

Abstract: Techniques for generated regular expressions are disclosed. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence. Generation of the regular expressions can be implemented on an interactive user interface. Commands can be applied to the one or more character sequences and regular expressions are generated based on the applied commands.

Abstract: Techniques for generated regular expressions are disclosed. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence. Alignment of span data structures may be performed when generating the regular expression.