Abstract: A machine translation system may use non-parallel monolingual corpora to generate a translation lexicon. The system may identify identically spelled words in the two corpora, and use them as a seed lexicon. The system may use various clues, e.g., context and frequency, to identify and score other possible translation pairs, using the seed lexicon as a basis. An alternative system may use a small bilingual lexicon in addition to non-parallel corpora to learn translations of unknown words and to generate a parallel corpus.

Abstract: Translating named entities from a source language to a target language. In general, in one implementation, the technique includes: generating potential translations of a named entity from a source language to a target language using a pronunciation-based and spelling-based transliteration model, searching a monolingual resource in the target language for information relating to usage frequency, and providing output including at least one of the potential translations based on the usage frequency information.

Abstract: A statistical translation model (TM) may receive a parse tree in a source language as an input and separate output a string in a target language. The TM may perform channel operations on the parse tree using model parameters stored in probability tables. The channel operations may include reordering child nodes, inserting extra words at each node, e.g., NULL words, translating leaf words, and reading off leaf words to generate the string in the target language. The TM may assign a translation probability to the string in the target language.

Abstract: A machine translation (MT) decoder may transform a translation problem into an integer programming problem, such as a Traveling Salesman Problem (TSP). The decoder may invoke an integer program (IP) solver to solve the integer programming problem and output a likely decoding based on the solution.

Abstract: A discourse structure for an input text segment is determined by generating a set of one or more discourse parsing decision rules based on a training set, and determining a discourse structure for the input text segment by applying the generated set of discourse parsing decision rules to the input text segment. A tree structure is summarized by generating a set of one or more summarization decision rules based on a training set, and compressing the tree structure by applying the generated set of summarization decision rules to the tree structure. Alternatively, summarization is accomplished by parsing an input text segment to generate a parse tree for the input segment, generating a plurality of potential solutions, applying a statistical model to determine a probability of correctness for each of potential solution, and extracting one or more high- probability solutions based on the solutions' respective determined probabilities of correctness.