Abstract: A method for extracting a relations graph uses an ontology graph in which nodes represent entity classes or concepts and edges represent properties of the classes. A property is associated with a constraint which defines a range of values that can be taken without incurring a cost. Input text in which entity and concept mentions are identified is received. An optimal set of alignments between a subgraph of the ontology graph and the identified mentions is identified by optimizing a function of constraint costs incurred by the alignments and a distance measure computed over the set of alignments. The relations graph is generated, based on the optimal set of alignments. The relations graph represents a linked set of relations instantiating a subgraph of the ontology. The relations graph can include relations involving implicit mentions corresponding to subgraph nodes that are not aligned to any of the concept or entity mentions.

Abstract: A method for extracting a relations graph uses an ontology graph in which nodes represent entity classes or concepts and edges represent properties of the classes. A property is associated with a constraint which defines a range of values that can be taken without incurring a cost. Input text in which entity and concept mentions are identified is received. An optimal set of alignments between a subgraph of the ontology graph and the identified mentions is identified by optimizing a function of the costs of the constraints incurred by the alignments and a distance measure computed over the set of alignments. A relations graph is generated, based on the optimal set of alignments. The relations graph represents a linked set of relations instantiating a subgraph of the ontology. The relations graph can include relations involving implicit mentions corresponding to subgraph nodes that are not aligned to any of the concept or entity mentions.

Abstract: A system and a method for phrase-based translation are disclosed. The method includes receiving source language text to be translated into target language text. One or more dynamic bi-phrases are generated, based on the source text and the application of one or more rules, which may be based on user descriptions. A dynamic feature value is associated with each of the dynamic bi-phrases. For a sentence of the source text, static bi-phrases are retrieved from a bi-phrase table, each of the static bi-phrases being associated with one or more values of static features. Any of the dynamic bi-phrases which each cover at least one word of the source text are also retrieved, which together form a set of active bi-phrases. Translation hypotheses are generated using active bi-phrases from the set and scored with a translation scoring model which takes into account the static and dynamic feature values of the bi-phrases used in the respective hypothesis. A translation, based on the hypothesis scores, is then output.

Abstract: A translation method is adapted to a domain of interest. The method includes receiving a source text string comprising a sequence of source words in a source language and generating a set of candidate translations of the source text string, each candidate translation comprising a sequence of target words in a target language. An optimal translation is identified from the set of candidate translations as a function of at least one domain-adapted feature computed based on bilingual probabilities and monolingual probabilities. Each bilingual probability is for a source text fragment and a target text fragment of the source text string and candidate translation respectively. The bilingual probabilities are estimated on an out-of-domain parallel corpus that includes source and target strings. The monolingual probabilities for text fragments of one of the source text string and candidate translation are estimated on an in-domain monolingual corpus.

Abstract: A system and method for automated validation of a translation provided by a generic machine translation engine comprises a generic machine translation (GMT) engine, a domain-specific multilingual terminology dictionary (DS) and a translation processor. The translation processor effects the first call to the GMT engine to obtain a first translation of an input text to a first translation text; a verification of a presence of a domain-specific terminology item in the first translation text; correctness assessment of the first translation text using the DS wherein upon an assessment of incorrect translation, a replacement of the domain-specific terminology item with a domain-specific translation text item from the DS.

Abstract: A system and method for natural language processing of queries are provided. A lexicon includes text elements that are recognized as being a proper noun when capitalized. A natural language query includes a sequence of text elements including words. The query is processed. The processing includes a preprocessing step, in which part of speech features are assigned to the text elements in the query. This includes identifying, from a lexicon, a text element in the query which starts with a lowercase letter and assigning recapitalization information to the text element in the query, based on the lexicon. This information includes a part of speech feature of the capitalized form of the text element. Then parts of speech for the text elements in the query are disambiguated, which includes applying rules for recapitalizing text elements based on the recapitalization information.

Abstract: A machine translation method includes receiving a source text string and identifying any named entities. The identified named entities may be processed to exclude common nouns and function words. Features are extracted from the source text string relating to the identified named entities. Based on the extracted features, a protocol is selected for translating the source text string. A first translation protocol includes forming a reduced source string from the source text string in which the named entity is replaced by a placeholder, translating the reduced source string by machine translation to generate a translated reduced target string, while processing the named entity separately to be incorporated into the translated reduced target string. A second translation protocol includes translating the source text string by machine translation, without replacing the named entity with the placeholder. The target text string produced by the selected protocol is output.

Abstract: A system and method suited to translation of queries are disclosed. The method includes receiving an input query in a source language and outputting a target query, based on a translation of the input query into a target language, different from the source language. The translation is performed with a machine translation system which has been trained on representations of features of translated queries that have been generated by translation of an original query, in the source language, into the target language and a measure of information retrieval performance of each the translated queries, for each of a set of original queries.

Abstract: A translation method is adapted to a domain of interest. The method includes receiving a source text string comprising a sequence of source words in a source language and generating a set of candidate translations of the source text string, each candidate translation comprising a sequence of target words in a target language. An optimal translation is identified from the set of candidate translations as a function of at least one domain-adapted feature computed based on bilingual probabilities and monolingual probabilities. Each bilingual probability is for a source text fragment and a target text fragment of the source text string and candidate translation respectively. The bilingual probabilities are estimated on an out-of-domain parallel corpus that includes source and target strings. The monolingual probabilities for text fragments of one of the source text string and candidate translation are estimated on an in-domain monolingual corpus.

Abstract: A translation system and method suited to use in Cross Language Information Retrieval employ a retrieval-based scoring function for reranking candidate translations. The method includes translating an input source language query to generate a set of the candidate translations in a target language. The candidate translations are scored with the scoring function, which allows them to be reranked, and an optimal one or more selected for use in querying a domain-specific collection of documents in the target language. The scoring function applies weights to features extracted from the candidate translations. The weights have been learned on features extracted from translated queries, each generated by translation of an original query into the target language, and a measure of information retrieval performance of each the translated queries. One or more of the features used is a domain-specific feature which relies on a corpus of documents in the domain of interest.

Abstract: A system and method for natural language processing of queries are provided. A lexicon includes text elements that are recognized as being a proper noun when capitalized. A natural language query includes a sequence of text elements including words. The query is processed. The processing includes a preprocessing step, in which part of speech features are assigned to the text elements in the query. This includes identifying, from a lexicon, a text element in the query which starts with a lowercase letter and assigning recapitalization information to the text element in the query, based on the lexicon. This information includes a part of speech feature of the capitalized form of the text element. Then parts of speech for the text elements in the query are disambiguated, which includes applying rules for recapitalizing text elements based on the recapitalization information.

Abstract: A system and method suited to translation of queries are disclosed. The method includes receiving an input query in a source language and outputting a target query, based on a translation of the input query into a target language, different from the source language. The translation is performed with a machine translation system which has been trained on representations of features of translated queries that have been generated by translation of an original query, in the source language, into the target language and a measure of information retrieval performance of each the translated queries, for each of a set of original queries.

Abstract: A system and a method for phrase-based translation are disclosed. The method includes receiving source language text to be translated into target language text. One or more dynamic bi-phrases are generated, based on the source text and the application of one or more rules, which may be based on user descriptions. A dynamic feature value is associated with each of the dynamic bi-phrases. For a sentence of the source text, static bi-phrases are retrieved from a bi-phrase table, each of the static bi-phrases being associated with one or more values of static features. Any of the dynamic bi-phrases which each cover at least one word of the source text are also retrieved, which together form a set of active bi-phrases. Translation hypotheses are generated using active bi-phrases from the set and scored with a translation scoring model which takes into account the static and dynamic feature values of the bi-phrases used in the respective hypothesis. A translation, based on the hypothesis scores, is then output.