Abstract: A system and method for a multi-stage approach for creating topic models is presented. The method includes applying a first stage topic model to textual data, wherein the first stage topic model is trained to discover a first plurality of topics and distributions of words in each topic of the first plurality of topics from the textual data; generating at least one seeded word for a subset of topics of the first plurality of topics, wherein the at least one seeded word is determined based on a plurality of selection rules and the distributions of words in the subset of topics discovered in the first stage topic model; and creating a second stage topic model by feeding the generated at least one seeded word to direct identification of the subset of topics of the first plurality of topics.

Abstract: Methods for generating a categorized, ranked, condensed summary of a transcript of a conversation, involving obtaining a diarized version of the transcript of the conversation, storing textual monologues from the transcript, determining classifications as to the textual monologues based on a classifier algorithm, associating the classifications with the textual monologues, creating textually-modified rephrasings of the textual monologues based on text and classification thereof, storing the textually-modified rephrasings, aggregating the textually-modified rephrasings based on associated clustering and scoring, and transmitting summary information pertaining to the aggregated textually-modified rephrasings to a user device.

Abstract: A system and method for generating a rephrasing model for rephrased actionable data extracted from conversations is presented. The method includes receiving a training dataset including a plurality of training samples, wherein each training sample includes a textual data extracted from recorded conversations and at least one action item, wherein the at least one action item is a portion of the textual data; associating a control signal to each training sample of the training dataset, wherein the control signal is added to the associated training sample; and training a rephrasing model using the training dataset, wherein the rephrasing model is trained to paraphrase the at least one action item to output at least one actionable data, wherein each training sample of the training dataset is iteratively fed into the machine learning algorithm of the rephrasing model.

Abstract: A system and method for generating a multi-label classifier of textual data is presented. The method includes training a plurality of single-label classifiers, wherein each of the plurality of single-label classifiers is trained to classify textual data to a single predefined revenue-based label; and training the multi-label classifier using labeled data output by the plurality of single-label classifiers, wherein the multi-label classifier is trained to classify textual data to a vector including revenue-based labels and their respective classification scores.

Abstract: A method for information processing includes computing, over a corpus of conversations, a conversation structure model including (i) a sequence of conversation parts having a defined order, and (ii) a probabilistic model defining each of the conversation parts. For a given conversation, a segmentation of the conversation is computed based on the computed conversation structure model. Action is taken on the given conversation according to the segmentation.

Abstract: A method and system for generating a tracker model for identification of trackers in textual data are provided. The method includes receiving an input query including at least an input sentence exemplifying a tracker of interest, wherein the tracker is at least one word with a specific context; generating a base results set including a set of sentences substantially matching the input sentence, wherein the sentences in the base results set are obtained from an index indexing textual data; deriving a first labeling set from the base results set, wherein includes samples of sentences from the base results set; receiving labels on each sentence in the first labeling set; and feeding the labels to a machine learning algorithm to train the tracker model, wherein the tracker model is generated and ready when enough labels have been processed by the machine learning algorithm.

Abstract: A method for information processing includes completing, over a corpus of conversations, a conversation structure model including (i) a sequence of conversation parts having a defined order, and (ii) a probabilistic model defining each of the conversation parts. For a given conversation, a segmentation of the conversation is computed based on the computed conversation structure model. Action is taken on the given conversation according to the segmentation.