International Business Machines Corporation

Nandana Sampath Mihindukulasooriya

Alfio Massimiliano Gliozzo

Michael Robert Glass

Gaetano Rossiello

Sugato Bagchi

Md Faisal Mahbub Chowdhury

Sarthak Dash

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

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

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Dublin, Ireland

Nandana Sampath Mihindukulasooriya