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A Maximum Entropy Approach for Semantic Language Modeling
... ME language model outperforms Wu’s ME language model [Wu and Khudanpur 2002] and the ME language model outperforms the LI language ...and semantic language models based on LI and ... See full document
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Semi Supervised Maximum Entropy Based Approach to Acronym and Abbreviation Normalization in Medical Texts
... The approach I am investigating falls into the hybrid category of bootstrapping or semi-supervised approaches to training classifiers; however, it uses a different notion of bootstrapping from that of Hearst ... See full document
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Latent Semantic Language Modeling and Smoothing
... Language modeling plays a critical role for automatic speech ...n-gram language models suffer from the lack of a good representation of historical words and an inability to estimate unseen parameters ... See full document
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A Fast Algorithm for Feature Selection in Conditional Maximum Entropy Modeling
... distribution and the count from the real training data. It is a simple and probably effective tech- nique for language modeling tasks. Since ME models are optimized using their likelihood or likelihood ... See full document
7
A Joint Syntactic and Semantic Dependency Parsing System based on Maximum Entropy Models
... the semantic parser label- ing the semantic roles of nouns and verbs for each predicate are both recognized as classification prob- lem, and the Maximum Entropy Models (MEs) are used for them ... See full document
5
CRC Press Utility Based Learning from Data 2010 RETAiL EBook pdf
... the language of information theory and solved via maximum entropy (ME), mini- mum relative entropy (MRE), or minimum mutual information (MMI) meth- ...as entropy, relative ... See full document
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Semantic pattern learning through maximum entropy based WSD technique
... Natural Language Processing (NLP) tasks. To obtain these semantic patterns, it is necessary to count on different ...the semantic behaviour of each ... See full document
7
Identification of Basic Phrases for Kazakh Language using Maximum Entropy Model
... There are a variety of techniques used for phrase recognition, which include rule-based technique, sta- tistical technique, and a combination of them. Church's (1988) approach used manual or semi- automatic ... See full document
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Parsing Syntactic and Semantic Dependencies with Two Single Stage Maximum Entropy Models
... This paper describes our system to carry out the joint parsing of syntactic and se- mantic dependencies for our participation in the shared task of CoNLL-2008. We il- lustrate that both syntactic parsing and se- mantic ... See full document
5
A Maximum Entropy Approach to Combining Word Alignments
... new approach to combining outputs of existing word align- ment ...a maximum entropy ...three language pairs, yielding significant improvements over input alignments and three heuristic ... See full document
8
Incremental Feature Selection and l1 Regularization for Relaxed Maximum Entropy Modeling
... this approach, an approximate gain in likelihood for adding a feature to the model is used as feature selection criterion, and thresholds on this gain are used as stopping ... See full document
8
A Maximum-Entropy approach for accurate document annotation in the biomedical domain
... the semantic level and significantly improve the search of relevant documents, as it has been shown by recent studies in the case of the search in the life sciences literature ... See full document
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LMSim : Computing Domain specific Semantic Word Similarities Using a Language Modeling Approach
... domain-specific semantic similarity be- tween ...our approach, domain-specific word similarity is cap- tured through language ...based approach to compute it ef- ...our approach on ... See full document
6
Improvement of a Whole Sentence Maximum Entropy Language Model Using Grammatical Features
... Over the last decade, there has been an increas- ing interest in Stochastic Context-Free Grammars (SCFGs) for use in different tasks (K., 1979; Jelinek, 1991; Ney, 1992; Sakakibara, 1990). The reason for this can be ... See full document
8
Maximum Entropy Density Estimation with Generalized Regularization and an Application to Species Distribution Modeling
... Standard maxent algorithms such as iterative scaling (Darroch and Ratcliff, 1972; Della Pietra et al., 1997), gradient descent, Newton and quasi-Newton methods (Cesa-Bianchi et al., 1994; Mal- ouf, 2002; Salakhutdinov et ... See full document
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Maximum Entropy Approach based Named Entity Recognition in Punjabi Language
... Natural Language Learning (CONNL-2002) was held in Taiwan,2002 that concerns with language independent Named Entity Recognition and concentrated on two European languages ... See full document
5
Maximum Entropy Modeling in Sparse Semantic Tagging
... Since 96% of the headwords in Blind are also in SHD, headwords are the most effective indicators. Using head- words alone can get 9.47% classification error rate. Other indicators investigated include adjectives, which ... See full document
6
Domain Adaptation of Maximum Entropy Language Models
... Recently, a hierarchical Bayesian adaptation method was proposed that can be applied to a large family of discriminative learning tasks (such as ME models, SVMs) (Daume III, 2007; Finkel and Manning, 2009). In NLP ... See full document
6
Latent Semantic Information in Maximum Entropy Language Models for Conversational Speech Recognition
... [r] ... See full document
8
A Maximum Entropy Approach to Natural Language Processing
... The feature selection algorithm de- scribed in Section 4 calculated that if this constraint were imposed on the model, the log-likelihood w o u l d rise by approximately 0.019059 bits; s[r] ... See full document
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