International Business Machines Corporation

University of Massachusetts

Nternational Business Machines Corporation

Alfio Massimiliano Gliozzo

Michael Robert Glass

Md Faisal Mahbub Chowdhury

Gaetano Rossiello

Sarthak Dash

Nicolas Rodolfo Fauceglia

Robert G Farrell

Nandana Sampath Mihindukulasooriya

Mustafa Canim

Oktie Hassanzadeh

Md Faisal M Chowdhury

Ruchi Mahindru

Aditya Anand Kalyanpur

James J Fan

Mihaela Ancuta Bornea

David Angelo Ferrucci

Yu Deng

Mohammad Sadoghi Hamedani

Sharon Mary Trewin

Sugato Bagchi

Christopher A Welty

Nidhi Rajshree

Mariano Rodriguez Muro

Biying Kong

Nicolas R Fauceglia

Thien H Nguyen

Piero Molino

Anish Mathur

Vittorio R Castelli

John Richard Smith

Bishwaranjan Bhattacharjee

Soumitra Sarkar

Achille Belly Fokoue-Nkoutche

Kavitha Srinivas

Jim Alain Laredo

Anastasios Kementsietsidis

Adam P Lally

Radu Florian

Michael James Ward

Avirup Sil

Michele Merler

Songyun Duan

Alessandro Morari

Cicero Nogueira Dos Santos

Pavan Kapanipathi Bangalore

Mauro Martino

Salim E Roukos

Bassem Makni

Aaron Chavez

Saurabh Pujar

Luca Buratti

Nicholas Brady Garvan Monath

Mudita Singhal

Chao Shang

Feifei Pan

Ankita Rajaram Naik

Xueqing Wu

Faisal Mahbub Chowdhury

Using a trained keyphrase generation model, a set of keyphrases corresponding to an input document is generated, a keyphrase in the set of keyphrases comprising a word summarizing a portion of a document. A relevance score measuring a similarity between the keyphrase and the document is calculated for a keyphrase in the set of keyphrases. The relevance score is adjusted according to a diversity balancing function.

Keyphrase generation

An embodiment includes generating a training dataset by aligning text from a document of a document database with a named entity from a knowledge base. The embodiment generates an enhanced training dataset by updating the training dataset to include a named entity type and a named entity label associated with the named entity. The embodiment trains a natural language processing (NLP) model using the enhanced training dataset resulting in a trained NLP model. The embodiment identifies, using the trained NLP model, the named entity in a block of unstructured text and generates, using the trained NLP model, a target sequence that includes a relationship between the named entity and a tail entity, as well as the named entity type and the named entity label of the named entity and a tail entity type and a tail entity label of the tail entity.

Generating semantic triplets from unstructured text using named entities

A system for ranking machine learning base models for transfer learning purposes is described. The system receives image data in the form an image or an image set and extracts image tags from the images. The image tags are expanded into a set of associated terms using a word embedding database and model. The associated terms are used to query a knowledge database for parent or categorical terms used to rank various matching machine learning base models that may be improved or trained by the image data.

Ranking image sources for transfer learning

Embodiments of the invention are directed to a computer system comprising a memory communicatively coupled to a processor system. The processor system is operable to perform processor system operations that include accessing query information associated with a to-be-augmented information set (TBAIS) having a TBAIS format. Query information sequence vectors (QISV) are generated that represent the query information and the TBAIS. Unannotated data repository information sequence vectors (UDRSV) are accessed that represent unannotated data repository information having a plurality of information formats. Matching UDRSV are identified, where the matching UDRSV include the UDRSV that match the QISV. A response to the query information is generated based at least in part on the matching UDRSV.

Retrieval-based, self-supervised augmentation using transformer models

Determining an initial rank and a probability of relevance of each of a retrieved plurality of electronic documents relevant to a query. For each of a plurality of candidate facets, determine a revised rank for each of the retrieved plurality of electronic documents relevant to the query. Selecting, for each of the retrieved plurality of electronic documents relevant to the query, a minimum rank from among the initial rank and the revised rank for each of the plurality of candidate facets. Determine an expected discounted cumulative gain based on the probability of relevance and the minimum rank for each of the retrieved plurality of electronic documents relevant to the query. Select a set of optimistic facets based on maximizing the expected discounted cumulative gain.

Optimistic facet set selection for dynamic faceted search

An embodiment for encoding permutation-invariant representations of linearized tabular data. The embodiment may receive input including tabular data and linearize a column or row within the received tabular data. The embodiment may automatically assign an increasing sequence of position identifiers to each non-delimiting tokenized cell in the linearized column or row until a header delimiter is reached. The embodiment may, in response to reaching the header delimiter, automatically assign a monotonically increasing sequence of position identifiers for each non-delimiting tokenized cell positioned after the header delimiter, restarting from an integer corresponding to 1 greater than the position identifier assigned to the header delimiter for each non-delimiting tokenized cell positioned after cell delimiters. The embodiment may automatically assign a static position identifier for each of the cell delimiters in the linearized column or row and output an encoded permutation-invariant representation of the linearized column or row.

Permutation invariance for representing linearized tabular data

An embodiment for improved linking of tabular columns to column types in an ontology unseen during training. The embodiment may for a target table, encode a target tabular query column, table headers, and target types independently to generate permutation invariant representations of tabular data associated with the target table. The embodiment may, for each of the target types, extract and further encode auxiliary information. The embodiment may process the encoded tabular data to obtain a first vector and a second vector. The embodiment may concatenate the first vector and the second vector to generate a final query vector. The embodiment may process the encoded target types through a third transformer to obtain a third vector. The embodiment may calculate a score to model interactions between the target tabular query column of the target table and the target types.

Linking tabular columns to unseen ontologies

A computer-implemented method according to one embodiment includes identifying a natural language query; translating the natural language query into an intermediate representation; converting the intermediate representation into one or more query triples; and performing relation linking between each of the one or more query triples and a plurality of knowledge base triples.

Implementing relation linking for knowledge bases

One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process for generating the classification of files to allow for file system organization and/or query augmentation. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a generating component that generates a keyphrase based on a context derived from evaluation of an input file, wherein the generating component employs a public repository of files annotated with a plurality of keyphrases, including the keyphrase, to generate the keyphrase based on the context, and an execution component that classifies the input file based on the keyphrase. In one or more embodiments, the input file can comprise a query, and classification of the input file can comprise augmenting the query based on the keyphrase.

Keyphrase generation leveraging public repository categories

A computer answers a question using a data table. The computer receives a user question and a target table containing a target cell corresponding to a target answer for the user question, with the target cell corresponding to a target column and a target row. The computer generates, a first classifier to provide column correlation values reflecting the probability that a given column is the target column. The computer generates a second classifier that provides row correlation values reflecting the probability that a given row is the target row. The computer applies the first classifier to the target table to determine a column correlation value for each column. The computer applies the second classifier to the target table to determine a row correlation value for each row. The computer suggests, as the target cell, a cell having elevated column and row correlation values relative to other target table cells.

Answering questions with artificial intelligence using tabular data

Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises analyzing a performed query by identifying a plurality of indicative markers based on a pre-stored classification database associated with the performed query; generating a plurality of facets based on the analysis of the performed query; selecting at least two facets within the generated plurality of facets by determining a quantitative similarity value between each respective facet and the plurality of identified indicative markers associated with the performed query; dynamically ranking the selected facets by prioritizing the selected facets based on a calculated overall score associated with assigned weighted values for each selected facet in the generated plurality of facets using a supervised machine learning algorithm; and displaying the dynamically ranked facets within a user interface of a computing device associated with a user.

Dynamic facet ranking

A system comprises a memory that stores computer-executable components; and a processor, operably coupled to the memory, that executes the computer-executable components. The system includes a receiving component that receives a corpus of data; a relation extraction component that generates noisy knowledge graphs from the corpus; and a training component that acquires global representations of entities and relation by training from output of the relation extraction component.

Deep symbolic validation of information extraction systems

Systems and techniques that facilitate spurious relationship filtration from external knowledge graphs based on distributional semantics of an input corpus are provided. In one or more embodiments, a context component can generate a context-based word embedding of one or more first terms in a document collection. The embedding can yield vector representations of the one or more first terms. The one or more first terms can correspond to knowledge terms in one or more first nodes of a knowledge graph. In one or more embodiments, a filtering component can filter out a relationship between the one or more first nodes and a second node of the knowledge graph based on a similarity value being less than a threshold. The similarity value can be a function of the vector representations of the one or more first terms. In various embodiments, cosine similarity can be used to compute the similarity value.

Filtering spurious knowledge graph relationships between labeled entities

A system, method and computer program product for disambiguating one or more entity mentions in one or more documents. The method facilitates the simultaneous linking entity mentions in a document based on convolution neural networks and recurrent neural networks that model both the local and global features for entity linking. The framework uses the capacity of convolution neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. The RNN functions to accumulate information about the previous entity mentions and/or target entities, and provide them as the global constraints for the linking process of a current entity mention.

Joint learning of local and global features for entity linking via neural networks

One embodiment of the present invention provides a method comprising receiving a text corpus, and generating a first list of triples based on the text corpus. Each triple of the first list comprises a first term representing a candidate hyponym, a second term representing a candidate hypernym, and a frequency value indicative of a number of times a hypernymy relation is observed between the candidate hyponym and the candidate hypernym in the text corpus. The method further comprises training a neural network for hypernym induction based on the first list. The trained neural network is a strict partial order network (SPON) model.

Hypernym detection using strict partial order networks

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Brooklyn, NY, United States of America

Alfio Massimiliano Gliozzo