International Business Machines Corporation

Avinesh Polisetty Venkata Sai

Lalit Agarwalla

Ankur Parikh

Chinnappa Guggilla

Praveen K Midde

Prabhakar R Majjiga

Scott Robert Carrier

Joy Mustafi

Anil Manohar Omanwar

Scoring candidate evidence passages for criteria validation. Evidence data associated with a criteria, such that the evidence data entries include a decision indicator indicating that the criteria is either met or not met by the evidence data, is collected. Candidate evidences, making up a corpus of data associated with the criteria, against which the criteria is to be validated, are generated. Each candidate evidence is evaluated against the evidence data. A score indicating the validity of the criteria with respect to the candidate evidence is generated, based on the decision indicators associated with the evidence data entries.

Scoring candidate evidence passages for criteria validation using historical evidence data

According to one exemplary embodiment, a method for finding an answer to a query from a table is provided. The method may include receiving the query and the table. The method may also include generating a hierarchical representation based on the received table, whereby the hierarchical representation comprises a primary tree and a secondary tree. The method may then include mapping the primary tree to the secondary tree. The method may further include generating a plurality of predicate triples in a semantic relationship form based on the primary tree and the secondary tree. The method may also include determining if a predicate triple within the plurality of predicate triples matches the query using query-side and table-side context and semantics. The method may then include adding the predicate triple within the plurality of predicate triples to a plurality of candidate answers based on determining that the predicate triple matches the query.

Answering natural language table queries through semantic table representation

A processor determines a set of terms frequently occurring within unstructured data associated with criteria of a query. The processor analyzes the unstructured data to match a term of the set of terms, to criteria of the query. Matching terms are retained and unmatched terms are checked for semantic similarity to terms of the criteria to determine an inferred match of the term of the unstructured data to terms of the criteria of the query. In response to determining an inferred match, the inferred match term is added to the second set of terms, and the processor compares the second set of terms to the terms of the criteria of the query, and removes matching terms, resulting in a third set of terms added to the set of criteria terms of the query. The additional query terms will enhance the recall without diluting the precision.

Terms for query expansion using unstructured data

Machine logic that automatically detects natural language processing (NLP) system annotation errors and correspondingly updates NLP annotators to prevent future erroneous annotations by performing the following steps: (i) determining that a first annotation error has occurred in an annotation of a corpus by the natural language processing system; (ii) generating a candidate set of annotation correction actions, where each annotation correction action of the set is adapted to prevent an occurrence of an error similar to the first annotation error by the natural language processing system; (iii) selecting an annotation correction action from the candidate set of annotation correction actions, based, at least in part, on a set of annotation correction confidence characteristics; and (iv) automatically applying the selected annotation correction action to the natural language processing system.

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Kakinada, India

Avinesh Polisetty Venkata Sai