We propose a novel reordering model for phrase-based statistical machine translation (SMT) that uses a maximum entropy (MaxEnt) model to predicate reorderings of neighbor blocks (phrase pairs). The model provides content-dependent, hierarchical phrasal reordering with generalization based on features automatically learned from a real-world bitext. We present an algorithm to extract all reordering events of neighbor blocks from bilingual data. In our experiments on Chineseto-English translation, this MaxEnt-based reordering model obtains significant improvements in BLEU score on the NIST MT-05 and IWSLT-04 tasks. 1

This paper proposes a new term weighting method for summarizing documents retrieved by IR systems. Unlike query-biased summarization methods, our method utilizes not the information of query, but the similarity information among original documents by hierarchical clustering. In order to map the similarity structure of the clusters into the weight of each word, we adopt the information gain ratio (IGR) of probabilistic distribution of each word as a term weight. If the amount of information of a word in a cluster increases after the cluster is partitioned into sub-clusters, we may consider that the word contributes to determine the structure of the sub-clusters. The IGR is a measure to express the degree of such contribution. We will show the effectiveness of our method based on the IGR by comparison with other systems. 1