International Business Machines Corporation

Rajasekar Krishnamurthy

Shivakumar Vaithyanathan

Yunyao Li

Prithviraj Sen

Sriram Raghavan

Huaiyu Zhu

Lucian Popa

Laura Chiticariu

Douglas Ronald Burdick

Ting Chen

Rahul Gupta

Hao Wang

Georgia Koutrika

Mauricio Antonio Hernandez-Sherrington

Marina Danilevsky Hailpern

Krishna Prasad Chitrapura

Armageddon Rhabdizo Brown

Jennifer Lee Beckmann

Suresh Thalamati

Sang Don Han

Ryan Wisnesky

Dulce Beatriz Ponceleon

Diptikalyan Saha

Eugene Jon Shekita

Jeffrey Thomas Kreulen

Berthold Reinwald

Karthik Sankaranarayanan

Yuanyuan Tian

Edwin Peter Dawson Pednault

Hima Prasad Karanam

Vikas Sindhwani

Shirish Tatikonda

Huahai Yang

Shajith I Mohamed

Alexandre Valentinovich Evfimievski

Jayavel Shanmugasundaram

Praduemn K Goyal

Amol N Ghoting

Ioannis Katsis

Mauricio A Hernandez

Christine T Wolf

Siddhartha Brahma

Bogdan Alexe

Prasad M Deshpande

Wei Cheng

George A Cypher

A computer-implemented method according to one embodiment includes receiving, at a deep neural network (DNN), a plurality of sentences each having an associated label; training the DNN, utilizing the plurality of sentences and associated labels; and producing a linguistic expression (LE) utilizing the trained DNN.

Producing explainable rules via deep learning

One embodiment provides for a method for evaluation of an artificial intelligence (AI) service, the method includes partitioning, by a processor, data into in-domain data and out-of-domain data. The processor defines held-out data from both of the in-domain data and the out-of-domain data for evaluation by each of domain and sub-domain based on building a taxonomy of both domains and sub-domains for the AI service. The held-out data is excluded from training data used for training the AI service. The processor further determines distribution underlying performance metrics for the held-out data using bootstrap validation processing. The processor also determines performance guarantees for multiple settings conditioned on multiple characteristics of an application scenario for the held-out data of the taxonomy based on the underlying performance metrics.

Workflow for evaluating quality of artificial intelligence (AI) services using held-out data

One embodiment provides for a method for evaluation of an artificial intelligence (AI) service, the method includes partitioning, by a processor, data into in-domain data and out-of-domain data. The processor defines held-out data from the in-domain data and the out-of-domain data for evaluation by domain and sub-domain based on building a taxonomy of domains and sub-domains for the AI service. The processor further determines distribution underlying performance metrics for the held-out data using statistical processing. The processor also determines performance guarantees for multiple settings conditioned on multiple characteristics of an application scenario for the held-out data of the taxonomy based on the underlying performance metrics. The processor further provides confidence intervals based on the performance guarantees.

Embodiments are provided for analysis of errors of a predictive model. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include an overview component that causes a client device to present first data identifying an error corresponding to a cell of a confusion matrix for a classification model, the error representing a mismatch between a first label generated by the classification model and a second label corresponding to a ground-truth observation. The computer-executable components also can include an element view component that receives second data defining a root cause of the error. The computer-executable components can further include an error annotation component that can embed the second data into a first data structure containing the first data, resulting in a first annotated data structure.

Error analysis of a predictive model

Methods, systems, and computer program products for table recognition in PDF documents are provided herein. A computer-implemented method includes discretizing one or more contiguous areas of a PDF document; identifying one or more white-space separator lines within the one or more discretized contiguous areas of the PDF document; detecting one or more candidate table regions within the one or more discretized contiguous areas of the PDF document by clustering the one or more white-space separator lines into one or more grids; and outputting at least one of the candidate table regions as a finalized table in accordance with scores assigned to each of the one or more candidate table regions based on (i) border information and (ii) cell structure information.

Table recognition in portable document format documents

Methods, systems, and computer program products for user-initiated dynamic data application programming interface creation are provided herein. A computer-implemented method includes automatically transforming a user-provided query into one or more parameterized queries, generating a data application programming interface based on at least one of the parameterized queries, outputting the generated data application programming interface to the user providing the user-provided query, and storing the generated data application programming interface in a database accessible to one or more additional users.

User-initiated dynamic data application programming interface creation

One embodiment provides a method for developing a text analytics program for extracting at least one target concept including: utilizing at least one processor to execute computer code that performs the steps of: initiating a development tool that accepts user input to develop rules for extraction of features of the at least one target concept within a dataset comprising textual information; developing, using the rules for feature extraction, an evaluation dataset comprising at least one document annotated with the at least one target concept to be extracted by the text analytics program; creating, using the rules for feature extraction, a rule-based annotator to extract the at least one target concept; training, using the evaluation dataset, a machine-learning annotator to extract the at least one target concept within the dataset; combining the rule-based annotator and the machine learning annotator to form a combined annotator; evaluating, using the evaluation dataset, extraction performance of the combined annotator against a predetermined threshold; and publishing, when the extraction performance of the combined annotator exceeds the predetermined threshold, the combined annotator for use in an application that extracts the at least one target concept from a plurality of datasets.

Unified text analytics annotator development life cycle combining rule-based and machine learning based techniques

Embodiments relate to facilitating construction of concepts from a task specification. A method includes receiving, from a user via a user interface, a task specification in natural language form. The method also includes parsing the task specification into a plurality of components, and searching a database for an existing concept having a pattern that approximates at least a portion of the plurality of components. The concept includes semantic meanings that are representable by textual patterns. The method further includes identifying any components of the plurality of components that are not included in the existing concept, and building a new concept that combines the existing concept and the components of the plurality of components that are not included in the existing concept.

Constructing concepts from a task specification

Described herein are methods, systems and computer program products for entity resolution. Entity resolution, also known as entity matching or record linkage, seeks to identify equivalent data objects between or among datasets. An example method includes creating a deterministic model by defining an entity to be resolved, selecting two datasets for comparison, defining matching predicates for attributes of the datasets to select a set of candidate matches, and defining a precedence rule for the candidate matches to select a subset of the candidate matches. The method includes running the deterministic model on the two datasets. Running the deterministic model includes applying the matching predicates and the precedence rule to data in the datasets that correspond to the attributes. The method also includes applying a cardinality rule to results of the running, and outputting the matching candidates for which the cardinality rule is satisfied.

Entity resolution between datasets

Embodiments of the present invention relate to a new method of entity integration using high-level scripting languages. In one embodiment, a method of and computer product for entity integration is provided. An entity declaration is read from a machine readable medium. The entity declaration describes an entity including at least one nested entity. An index declaration is read from a machine readable medium. The index declaration describes an index of nested entities. An entity population rule is read from a machine readable medium. The entity population rule describes a mapping from an input schema to an output schema. The output schema conforms to the entity declaration. A plurality of input records is read from a first data store. The input records conform to the input schema. The entity population rule applies to the plurality of records to create a plurality of output records complying with the output schema. An index of nested entities is populated. The index complies with the index declaration.

Entity integration using high-level scripting languages

Systems and methods for processing Machine Learning (ML) algorithms in a MapReduce environment are described. In one embodiment of a method, the method includes receiving a ML algorithm to be executed in the MapReduce environment. The method further includes parsing the ML algorithm into a plurality of statement blocks in a sequence, wherein each statement block comprises a plurality of basic operations (hops). The method also includes automatically determining an execution plan for each statement block, wherein at least one of the execution plans comprises one or more low-level operations (lops). The method further includes implementing the execution plans in the sequence of the plurality of the statement blocks.

Systems and methods for processing machine learning algorithms in a MapReduce environment

A method, system, and article are provided for evaluating regular expressions over large data collections. A general purpose index is built to handle complex regular expressions at the character level. Characters, character classes, and associated metadata are identified and stored in an index of a collection of documents. Given a regular expression, a query is generated based on the contents of the index. This query is executed over the index to identify a set of documents in the collection of documents over which the regular expression can be evaluated. Based upon the query execution, the identified set of documents is returned for evaluation by the regular expression responsive to execution of the query over the index.

Indexing for regular expressions in text-centric applications

A text annotation structured storage system stores text annotations with associated type information in a structured data store. The present system persists or stores annotations in a structured data store in an indexable and queryable format. Exemplary structured data stores comprise XML databases and relational databases. The system exploits type information in a type system to develop corresponding schemas in a structured data model. The system comprises techniques for mapping annotations to an XML data model and a relational data model. The system captures various features of the type system, such as complex types and inheritance, in the schema for the persistent store. In particular, the repository provides support for path navigation over the hierarchical type system starting at any type.

System and method for storing text annotations with associated type information in a structured data store

Growing community of inventors

Campbell, CA, United States of America

Rajasekar Krishnamurthy