Abstract: A method and a system for coreference resolution are provided. The method includes receiving a set of document clusters, each cluster in the set of document clusters including a set of text documents. Instances of each of a set of candidate named entities are identified in the document clusters. For a pairs of the candidate named entities, at least one socio-temporal feature is computed that is based on the similarity of the distributions of identified instances of the respective candidate name entities among the document clusters. A decision for merging for the candidate named entities into a common real named entity is based on the socio-temporal features.

Abstract: A method for computing similarity between paths includes extracting corpus statistics for triples from a corpus of text documents, each triple comprising a predicate and respective first and second arguments of the predicate. Documents in the corpus are clustered to form a set of clusters based on textual similarity and temporal similarity. An event-based path similarity is computed between first and second paths, the first path comprising a first predicate and first and second argument slots, the second path comprising a second predicate and first and second argument slots, the event-based path similarity being computed as a function of a corpus statistics-based similarity score which is a function of the corpus statistics for the extracted triples which are instances of the first and second paths, and a cluster-based similarity score which is a function of occurrences of the first and second predicates in the clusters.

Abstract: A method for extraction of events includes performing linguistic processing on a collection of text documents to identify predicates and respective arguments of the predicates and performing temporal processing on the collection of documents to normalize referential dates. A query is received which includes a topic and date information which defines a date range. A collection of excerpts from the collection of documents is identified, each excerpt including an argument which is based on the topic and a normalized reference to a date which matches the defined date range. A plurality of sets of events in the collection of excerpts is identified, each set of events including a plurality of the excerpts in the collection that are linked together by entailment relationships.

Abstract: A method and a system for coreference resolution are provided. The method includes receiving a set of document clusters, each cluster in the set of document clusters including a set of text documents. Instances of each of a set of candidate named entities are identified in the document clusters. For a pairs of the candidate named entities, at least one socio-temporal feature is computed that is based on the similarity of the distributions of identified instances of the respective candidate name entities among the document clusters. A decision for merging for the candidate named entities into a common real named entity is based on the socio-temporal features.

Abstract: A system and method are provided for processing an input document which enable assessment of the coherence of an abstract of the document. The method includes storing the document in memory and, for each sentence of the abstract, comparing the sentence with sentences of a main body of the document using textual entailment techniques to identify whether the sentence of the abstract entails a sentence in the main body of the document. Links can then be generated between the entailing sentences of the abstract and the corresponding entailed sentences of the document. The document and generated links are output. The links enable the coherence of the abstract to be evaluated, either manually or automatically, using an evaluation component of the system.

Abstract: A method for named entity resolution includes parsing an input text string to identify a context in which an identified named entity of the input text string is used. The identified context is compared with at least one stored context in which the named entity in the stored context is associated with a class of named entity, the named entity class being selected from a plurality of classes, at least one of the plurality of classes corresponding to a metonymic use of a respective named entity. A named entity class is assigned to the identified named entity from the plurality of named entity classes, based on at least one of the identified context and the comparison.

Abstract: A soft clustering method comprises (i) grouping items into non-exclusive cliques based on features associated with the items, and (ii) clustering the non-exclusive cliques using a hard clustering algorithm to generate item groups on the basis of mutual similarity of the features of the items constituting the cliques. In some named entity recognition embodiments illustrated herein as examples, named entities together with contexts are grouped into cliques based on mutual context similarity. Each clique includes a plurality of different named entities having mutual context similarity. The cliques are clustered to generate named entity groups on the basis of mutual similarity of the contexts of the named entities constituting the cliques.

Abstract: Aspects of the exemplary embodiment relate to a system and method for processing a document which enables assessment of the coherence of an abstract of the document. The method includes storing the document in memory and, for each sentence of the abstract, comparing the sentence with sentences of a main body of the document using textual entailment techniques to identify whether the sentence of the abstract entails a sentence in the main body of the document. Links can then be generated between the entailing sentences of the abstract and the corresponding entailed sentences of the document. The document and generated links are output. The links enable the coherence of the abstract to be evaluated, either manually or automatically, using an evaluation component of the system.

Abstract: A soft clustering method comprises (i) grouping items into non-exclusive cliques based on features associated with the items, and (ii) clustering the non-exclusive cliques using a hard clustering algorithm to generate item groups on the basis of mutual similarity of the features of the items constituting the cliques. In some named entity recognition embodiments illustrated herein as examples, named entities together with contexts are grouped into cliques based on mutual context similarity. Each clique includes a plurality of different named entities having mutual context similarity. The cliques are clustered to generate named entity groups on the basis of mutual similarity of the contexts of the named entities constituting the cliques.

Abstract: A method for named entity resolution includes parsing an input text string to identify a context in which an identified named entity of the input text string is used. The identified context is compared with at least one stored context in which the named entity in the stored context is associated with a class of named entity, the named entity class being selected from a plurality of classes, at least one of the plurality of classes corresponding to a metonymic use of a respective named entity. A named entity class is assigned to the identified named entity from the plurality of named entity classes, based on at least one of the identified context and the comparison.