Abbreviated words carry critical information in the literature of many special domains. This paper reports our research in recognizing dotted abbreviations with MaxEnt model. The key points in our work include: (1) allowing the model to optimize with as many features as possible to capture the text characteristics of context words, and (2) utilizing simple lexical information such as sentence-i...

This paper presents our recent work on period disambiguation, the kernel problem in sentence boundary identification, with the maximum entropy (Maxent) model. A number of experiments are conducted on PTB-II WSJ corpus for the investigation of how context window, feature space and lexical information such as abbreviated and sentence-initial words affect the learning performance. Such lexical inf...

We develop a maximum entropy (maxent) approach to generating recommendations in the context of a user’s current navigation stream, suitable for environments where data is sparse, high-dimensional, and dynamic— conditions typical of many recommendation applications. We address sparsity and dimensionality reduction by first clustering items based on user access patterns so as to attempt to minimi...

Maximum Entropy (MaxEnt) models (Jaynes, 1957) are exponential models that implement the intuition that if there is no evidence to favour one alternative solution above another, both alternatives should be equally likely. In order to accomplish this, as much information as possible about the process you want to model must be collected. This information consists of frequencies of events relevant...

Citation: Bargain A, Marchese F, Savini A, Taviani M and Fabri M-C (2017) Santa Maria di Leuca Province (Mediterranean Sea): Identification of Suitable Mounds for Cold-Water Coral Settlement Using Geomorphometric Proxies and Maxent Methods. Front. Mar. Sci. 4:338. doi: 10.3389/fmars.2017.00338 Santa Maria di Leuca Province (Mediterranean Sea): Identification of Suitable Mounds for Cold-Water Co...

This paper proposes a novel lexicalized approach for rule selection for syntax-based statistical machine translation (SMT). We build maximum entropy (MaxEnt) models which combine rich context information for selecting translation rules during decoding. We successfully integrate the MaxEnt-based rule selection models into the state-of-the-art syntax-based SMT model. Experiments show that our lex...

In this paper, we address the problem of predicting one of three functions for the English pronoun ‘it’: anaphoric, event reference or pleonastic. This disambiguation is valuable in the context of machine translation and coreference resolution. We present experiments using a MAXENT classifier trained on gold-standard data and self-training experiments of an RNN trained on silver-standard data, ...