Abstract: A method of identifying and using side information available to statistical machine translation systems within an enterprise setting, the method including extracting user-specific interaction and non-interaction-based information from at least one corresponding database within the enterprise for each of a plurality of users, aggregating the user-specific interaction and non-interaction based information from a plurality of users, by using a processor on a computer, to tune and adapt background translation and language models, and updating all relevant models within the enterprise after user activity based on the tuned and adapted translation and language models.

Abstract: A method of identifying and using side information available to statistical machine translation systems within an enterprise setting, the method including extracting user-specific interaction and non-interaction-based information from at least one corresponding database within the enterprise for each of a plurality of users, aggregating the user-specific interaction and non-interaction based information from a plurality of users, by using a processor on a computer, to tune and adapt background translation and language models, and updating all relevant models within the enterprise after user activity based on the tuned and adapted translation and language models.

Abstract: Methods and apparatus are provided for annotating documents with one or more of entities, events and relations. Documents are annotated by presenting the document to a user; presenting the user with a list of possible entity types, wherein the list of possible entity types is configurable; and obtaining at least one mention annotation that associates a selected phrase in the document with one of the possible entity types. The selected phrase can be presented to the user, for example, based on one or more presentation rules associated with the associated entity type. The method can be implemented, for example, in a client-server configuration where a browser communicates with a remote server.

Abstract: A fast vocabulary independent method for spotting words in speech utilizes a preprocessing step and a coarse-to-detailed search strategy for spotting a word/phone sequence in speech. The preprocessing includes a Viterbi-beam phone level decoding using a tree-based phone language model. The coarse search matches phone-ngrams to identify regions of speech as putative word hits, and the detailed search performs an acoustic match at the putative hits with a model of the given word included in the vocabulary of the recognizer.

Abstract: A system for translating a first word set in a source language into a second word set in a target language, the system comprising: input means for inputting the first word set into the system; tagging means for tagging the first word set input to the system so as to at least substantially reduce non-essential variability in the first word set; translation means including a single a posteriori conditional probability model and a target candidate store for storing target language candidate word sets, wherein the translation means employs the single model to evaluate the target language candidate word sets in order to select the target language candidate word set having a best score with respect to the first word set; and output means for outputting the best scoring target language candidate word set as the second word set in the target language.

Abstract: The invention proposes using statistical methods to do natural language understanding. The key notion is that there are "strings" of words in the natural language, that correspond to a single semantic concept. One can then define an alignment between an entire semantic meaning (consisting of a set of semantic concepts), and the English. This is modeled using P(E,A.vertline.S). One can model p(S) separately. This allows each parameter to be modeled using many different statistical models.

Abstract: A method of gender dependent speech recognition includes the steps of identifying phone state models common to both genders, identifying gender specific phone state models, identifying a gender of a speaker and recognizing acoustic data from the speaker.

Abstract: A statistical language model for inflected languages, having very large vocabularies, is generated by splitting words into stems, prefixes and endings, and deriving trigrams for the stems, ending and prefixes. The statistical dependence of endings and prefixes from each stem is also obtained, and the resulting language model is a weighted sum of these scores.