International Business Machines Corporation

Shreeranjani Srirangamsridharan

Mudhakar Srivatsa

Raghu Kiran Ganti

Yeon-Sup Lim

Dakshi Agrawal

Jae-Wook Ahn

Swati Rallapalli

Michele Merler

Linsong Chu

Mahmood Saajan Ashek

Dean Steuer

Mohammad Siddiqui

Asif Sharif

Ramey Ghabros

Somesh Jha

Mojdeh Sayari Nejad

Yusuf Mai

Antara Palit

Embodiments for providing rare topic detection using hierarchical topic modeling by a processor. A hierarchical topic model may be learned from one or more data sources. One or more dominant words in a selected cluster may be iteratively removed using the hierarchical topic model. The dominant words may relate to one or more primary topics of the cluster. The learned hierarchical topic model may be seeded with one or more words, n-grams, phrases, text snippets, or a combination thereof to evolve the hierarchical topic model and the removed dominant words are reinstated upon completion of the seeding.

Rare topic detection using hierarchical clustering

Systems, methods and/or computer program products for controlling and visualizing topic modeling results using a topic modeling interface. The interface allows user directed exploration, understanding and control of topic modeling algorithms, while offering both semantic summaries and/or structure attribute explanations about results. Explanations and differentiations between results are presented using metrics such as cohesiveness and visual displays depicting hierarchical organization. Through user-manipulation of features of the interface, iterative changes are implemented through user-feedback, adjusting parameters, broadening or narrowing topic results, and/or reorganizing topics by splitting or merging topics. As users trigger visual changes to results being displayed, users can compare and contrast output from the topic modeling algorithm. With each change to parameters, users view different explanations informing the user why the changes being displayed occurred, providing users deeper understanding of the topic modeling process, how to manipulate parameters to achieve accurate topic results and adjust granularity of information presented.

Transparent and controllable topic modeling

A computer-implemented method for generating and deploying a reinforced learning model to train a chatbot. The method includes selecting a plurality of conversations, wherein each conversation includes an agent and a user. The method includes identifying, in each of the conversations, a set of turns and on or more topics. The method further includes associating one or more topics to each turn of the set of turns. The method includes, generating a conversation flow for each conversation, wherein the conversation flow identifies a sequence of the topics. The method includes applying an outcome score to each conversation. The method includes creating a reinforced learning (RL) model, wherein the RL model includes a Markov is based on the conversation flow of each conversation and the outcome score of each conversation. The method includes deploying the RL model, wherein the deploying includes sending the RL model to a chatbot.

Reinforcement learning for chatbots

A method for obtaining information and status about a monitored system by adaptively analyzing log messages is provided. A log analyzer receives log messages generated by a monitored system. The log analyzer identifies static and variable portions in the received log messages. The log analyzer generates a template based on the identified static and variable portions of the received log messages. The log analyzer computes a metric for the generated template based on a number of log messages that fall within the template. The log analyzer reports a status in the monitored system based on the computed metric.

Adaptive log analysis

A method and system for extracting information from a drawing. The method includes classifying nodes in the drawing, extracting attributes from the nodes, determining whether there are errors in the node attributes, and removing the nodes from the drawing. The method also includes identifying edges in the drawing, extracting attributes from the edges, and determining whether there are errors in the edge attributes. The system includes at least one processing component, at least one memory component, an identification component, an extraction component, and a correction component. The identification component is configured to classify nodes in the drawing, remove the nodes from the drawing, and identify edges in the drawing. The extraction component is configured to extract attributes from the nodes and edges. The correction component is configured to determine whether there are errors in the extracted attributes.

Extracting and analyzing information from engineering drawings

Embodiments of the invention include method, systems and computer program products for document vectorization. Aspects include receiving, by a processor, a plurality of documents each having a plurality of word. The processor utilizing a vector embeddings engine generates a vector to represent each of the plurality of words in the plurality of documents. An image representation for each document in the plurality of documents is created and a word probability for each of the plurality of words in the plurality of documents is generated. A position for each word probability is determined in the image based on the vector associated with each word and a compression operation on the images is performed to produce a compact representation for the plurality of documents.

Vectorization of documents

An input dataset for training a new machine learning model is received by a processor. For each of a plurality of trained machine learning models, a hash function and a sketch of a training dataset used to train the machine learning model is retrieved. A sketch of the input dataset is computed based on the hash function and the input dataset, along with a distance between the sketch of the training dataset and the sketch of the input dataset. The computed distances of the trained machine learning models are ranked from smallest to largest, and a seed machine learning model for the input dataset is selected from the trained machine learning models based at least in part on the ranking. A training process of the new machine learning model using the selected seed machine learning model and the input dataset is initiated.

Ranking and updating machine learning models based on data inputs at edge nodes

A parse tree corresponding to a portion of narrative text is constructed. The parse tree includes a data structure representing a syntactic structure of the portion of narrative text as a set of tokens according to a grammar. Using a token in the parse tree as a focus word, a context window comprising a set of words within a specified distance from the focus word is generated, the distance determined according to a number of links of the parse tree separating the focus word and a context word in the set of words. A weight is generated for the focus word and the context word. Using the weight, a first vector representation of a first word is generated, the first word being within a second portion of narrative text.

Parse tree based vectorization for natural language processing

Systems, methods, and computer-readable media are described for automatically identifying potential errors in the text output of a domain-agnostic speech-to-text engine and generating text snippets that contain words representative of the potential errors and other words in the neighborhoods of such words for context. In this manner, a substantially reduced amount of text (i.e., the text snippets) can be reviewed for errors in the speech-to-text conversion rather than the entire text output, thereby significantly reducing the burden associated with error identification in the text output.

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White Plains, NY, United States of America

Shreeranjani Srirangamsridharan