Several recent efforts in statistical natural language understanding (NLU) have focused on generating clumps of English words from semantic meaning concepts (Miller et al., 1995; Levin and Pieraccini, 1995; Epstein et al., 1996; Epstein, 1996). This paper extends the IBM Machine Translation Group's concept of fertility (Brown et al., 1993) to the generation of clumps for natural language unders...

Fertility rate is one of the most important global indexes. Past researchers found models which fit to age-specific fertility rates. For example, mixture probability density functions have been proposed for situations with bi-modal fertility patterns. This model is less useful for unimodal age-specific fertility rate patterns, so a model based on skew-symmetric (skew-normal) pdf was proposed by...

In this paper, we describe the system jointly developed by IBM Research and Columbia University for video copy detection and multimedia event detection applied to the TRECVID-2010 video retrieval benchmark. A. Content-Based Copy Detection: The focus of our copy detection system this year was fusing three types of complementary fingerprints: a keyframe-based color correlogram, SIFTogram (bag of ...

In this paper, we attempt to generalize the ideal binary mask (IBM) estimation to the ideal ratio mask (IRM) estimation. Under binary masking, the error in IBM estimation may greatly distort the original speech spectrum. The main purpose of this paper is using ratio mask to smooth this negative impact. Since the key issue is the noise tracking, we firstly use exponential distributions to model ...

In this paper, we present a Bayesian Learning based method to train word dependent transition models for HMM based word alignment. We present word alignment results on the Canadian Hansards corpus as compared to the conventional HMM and IBM model 4. We show that this method gives consistent and significant alignment error rate (AER) reduction. We also conducted machine translation (MT) experime...

In this paper, we explore the IBM Model with a `0-norm prior to the semantic parsing which parses a sentence to its corresponding meaning representation, and compare two supervised probabilistic Combinatory Categorial Grammar (PCCG) online learning approaches that are Unification-Based Learning (UBL) method and Factored Unification-Based Learning (FUBL) one. Specially, we extend manually GeoQue...

I consider mathematical modelling and simulation to be both a legitimate intellectual exercise, and a practical aid to policy analysis and decision-making. However to the extent that there is an issue revolving around understanding models versus understanding systems, then I am fully committed to the systems camp. I think it is a cop out to produce models which may exhibit all kinds of interest...

Most statistical translation systems are based on phrase translation pairs, or “blocks”, which are obtained mainly from word alignment. We use blocks to infer better word alignment and improved word alignment which, in turn, leads to better inference of blocks. We propose two new probabilistic models based on the innerouter segmentations and use EM algorithms for estimating the models’ paramete...

The aim of this work is to develop and study a fully continuous individual-based model (IBM) for cancer tumor invasion into a spatial environment of surrounding tissue. The IBM improves previous spatially discrete models, because it is continuous in all variables (including spatial variables), and thus not constrained to lattice frameworks. The IBM includes four types of individual elements: tu...

Recently, discriminative word alignment methods have achieved state-of-the-art accuracies by extending the range of information sources that can be easily incorporated into aligners. The chief advantage of a discriminative framework is the ability to score alignments based on arbitrary features of the matching word tokens, including orthographic form, predictions of other models, lexical contex...