Abstract: A method for resolving prepositional phrase attachments includes, for an input sequence of text, identifying a prepositional phrase and a set of candidate heads for the prepositional phrase. The prepositional phrase includes a preposition and a modifier. For each candidate head in the set of candidate heads, the candidate head is scored with a scoring function which outputs a score as a function of a tensor product of a word embedding of the candidate head, a product of word embeddings of the preposition and modifier of the preposition, and a matrix of learned parameters or a decomposition thereof. One of the candidate heads is identified as a predicted head for attachment to the prepositional phrase based on the scores for the candidate heads.

Abstract: A method for modeling a sparse function over sequences is described. The method includes inputting a set of sequences that support a function. A set of prefixes and a set of suffixes for the set of sequences are identified. A sub-block of a full matrix is identified which has the full structural rank as the full matrix. The full matrix includes an entry for each pair of a prefix and a suffix from the sets of prefixes and suffixes. A matrix for the sub-block is computed. A minimal non-deterministic weighted automaton which models the function is computed, based on the sub-block matrix. Information based on the identified minimal non-deterministic weighted automaton is output.

Abstract: A method for entity recognition employs document-level entity tags which correspond to mentions appearing in the document, without specifying their locations. A named entity recognition model is trained on features extracted from text samples tagged with document-level entity tags. A text document to be labeled is received, the text document being tagged with at least one document-level entity tag. A document-specific gazetteer is generated, based on the at least one document-level entity tag. The gazetteer includes a set of entries, one entry for each of a set of entity names. For a text sequence of the document, features for tokens of the text sequence are extracted. The features include document-specific features for tokens matching at least a part of the entity name of one of the gazetteer entries. Entity labels are predicted for the tokens in the text sequence with the named entity recognition model, based on the extracted features.

Abstract: A method for entity recognition employs document-level entity tags which correspond to mentions appearing in the document, without specifying their locations. A named entity recognition model is trained on features extracted from text samples tagged with document-level entity tags. A text document to be labeled is received, the text document being tagged with at least one document-level entity tag. A document-specific gazetteer is generated, based on the at least one document-level entity tag. The gazetteer includes a set of entries, one entry for each of a set of entity names. For a text sequence of the document, features for tokens of the text sequence are extracted. The features include document-specific features for tokens matching at least a part of the entity name of one of the gazetteer entries. Entity labels are predicted for the tokens in the text sequence with the named entity recognition model, based on the extracted features.