International Business Machines Corporation

Nicolas Rodolfo Fauceglia

Alfio Massimiliano Gliozzo

Robert G Farrell

Md Faisal Mahbub Chowdhury

Gaetano Rossiello

Nandana Sampath Mihindukulasooriya

Ruchi Mahindru

Yu Deng

Nidhi Rajshree

Biying Kong

Anish Mathur

Soumitra Sarkar

Mustafa Canim

Michael Robert Glass

Sarthak Dash

Aaron Chavez

Mudita Singhal

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

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

One embodiment of the invention provides a method for terminology ranking for use in natural language processing. The method comprises receiving a list of terms extracted from a corpus, where the list comprises a ranking of the terms based on frequencies of the terms across the corpus. The method further comprises accessing a domain ontology associated with the corpus, and re-ranking the list based on the domain ontology. The resulting re-ranked list comprises a different ranking of the terms based on relevance of the terms using knowledge from the domain ontology. The method further comprises generating clusters of terms via a trained model adapted to the corpus, and boosting a rank of at least one term of the re-ranked list based on the clusters to increase a relevance of the at least one term using knowledge from the trained model.

Discovering ranked domain relevant terms using knowledge

A method, system, and computer program product for automated evaluation of information retrieval systems are provided. The method accesses a natural language query from a set of natural language queries. The natural language query is associated with a query difficulty level. The method generates one or more natural language responses to the natural language natural language query. Each natural language response is associated with at least one facet of the plurality of facets. The method generates a set of feedback cues. A set of search results for the natural language query are returned. The set of search results include a highest ranked natural language response of the one or more natural language responses. The method generates an evaluation result for the HCIR system for the query difficulty level based on the one or more natural language responses, the set of search results, and the set of feedback cues.

Automated evaluation of information retrieval

A query-focused faceted structure generation method, system, and computer program product for generating a query-focused faceted structure from a taxonomy for searching a document collection, including ingesting a document corpus, generating a vector space representation of a query and instances from a taxonomy of the document corpus, and producing a dynamic structure of a relevant facet categories and facet values using a two-vector space representation from the generated vector space representation.

Dynamic faceted search on a document corpus

A query-focused faceted structure generation method, system, and computer program product for generating a query-focused faceted structure from a taxonomy for searching a document corpus, including augmenting taxonomy types with new instances where the instances comprise entities within a proximity of existing instances of taxonomy types in a local embedding of entities parsed from the document corpus, ranking each instance in the augmented taxonomy with respect to its type as a function of both a distance from an instance to a query in a global embedding vector space of the entities trained from the document corpus and a distance of an instance to a type in the local embedding, and ranking the taxonomy types using expanded instances in the document corpus for each type.

A spreadsheet population method, system, and computer program product include associating text with a spreadsheet, the text including candidate data items for populating the spreadsheet, building a multi-dimensional analogy model where each dimension comprises a unique pair of data items where the data items co-occur within a same context window, accepting example data items in the spreadsheet where the data items form tuples in a same implicit relationship according to a spatial configuration, and performing an assistance operation on the spreadsheet using the data item tuples retrieved using the analogy model from the example data items.

Populating spreadsheets using relational information from documents

An input entity pair including a first entity and a second entity is received. The first entity and the second entity are associated by a particular relation. A first set of statements containing the first entity and a second set of statements containing the second entity is are received from a corpus. A first set of discriminative words in the first set of statements and a second set of discriminative words in the second set of statements are identified. Perceptions in the first set of discriminative words and the second set of discriminative words are identified. A predetermined number of ranked statements is retrieved from the corpus using the identified perceptions as a query. Candidate entity pairs are extracted from the predetermined number statements. The candidate entity pairs have a relation therebetween analogous to the relation between the first entity and the second entity.

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New York, NY, United States of America

Nicolas Rodolfo Fauceglia