[PDF] Top 20 Maximum Entropy Models for FrameNet Classification
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Maximum Entropy Models for FrameNet Classification
... We extend Gildea and Jurafsky (2002)’s initial effort in three ways. First, we adopt a maximum entropy (ME) framework in order to learn a more accurate classification model. Second, we include ... See full document
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Parsing Syntactic and Semantic Dependencies with Two Single Stage Maximum Entropy Models
... We implement a left-to-right arc-eager parsing model in a way that the parser scan through an in- put sequence from left to right and the right depen- dents are attached to their heads as soon as possible (Hall et al., ... See full document
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Using Maximum Entropy Models to Discriminate between Similar Languages and Varieties
... In a multilingual setting, the effect of adding Galician to group D could be investigated. Focusing on Spanish language, we plan to geographically expand the classifier to deal with all national varieties, a much harder ... See full document
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Cross lingual Pronoun Prediction for English, French and German with Maximum Entropy Classification
... explores models for CLPP with the focus on using only simple ...for classification are reported, which makes predictions based on previous clas- sification ... See full document
7
The Integration of Dependency Relation Classification and Semantic Role Labeling Using Bilayer Maximum Entropy Markov Models
... relation classification and semantic role ...relations. Maximum Entropy Markov Models are implemented to estimate con- ditional probability distribution and to do ... See full document
5
Chinese Microblogs Sentiment Classification using Maximum Entropy
... sentiment classification (CMSC) system in the Topic-Based Chinese Message Polarity Classification task of SIGHAN-8 ...polarity classification of Chinese microblogs is still an open problem ...system, ... See full document
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A Joint Syntactic and Semantic Dependency Parsing System based on Maximum Entropy Models
... 2009 shared task is presented. The system is com- posed of three components: a syntactic dependency parser, a predicate classifier and a semantic parser. The first-order MSTParser is used as our syntactic dependency ... See full document
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Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models
... We consider LBFGS as Malouf (2002) reports that it is better than other approaches including GIS and IIS . Lin et al. (2008) show that TRON is faster than LBFGS for document classification, so we include TRON for ... See full document
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Decision Aggregation in Distributed Classification by a Transductive Extension of Maximum Entropy/Improved Iterative Scaling
... ensemble classification systems, ...feature models, the classifier’s (parametric) structure, the training set, the training objective function, parameter initialization, and the learning algorithm for ... See full document
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FrameNet based Semantic Parsing using Maximum Entropy Models
... We train the ME models using the GIS algorithm (Darroch and Ratcliff, 1972) as implemented in the YASMET ME package (Och, 2002). For testing, we use the YASMET MEtagger (Bender et al. 2003) to perform the Viterbi ... See full document
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A Combined Entropy/Phase-Field Approach to Gravity
... early models of van der Waals (1893), Korteweg (1901), Ginzburg-Landau (1950), Cahn-Hilliard (1958), Allen-Cahn (1960), Halperin, Hohenberg & Ma ...early models did not include any spatial resolution, ... See full document
13
Maximum entropy production: can it be used to constrain conceptual hydrological models?
... Another example was illustrated by Porada et al. (2011), who used MEP to constrain parameters for a physically based model based on multiple 1-D columns to simulate the wa- ter balance of the largest 35 catchments on ... See full document
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A Linear Observed Time Statistical Parser Based on Maximum Entropy Models
... The maximum entropy parser presented here achieves a parsing accuracy which exceeds the best previously published results, and parses a test sen- tence in linear observed time, with resp[r] ... See full document
10
Refined Lexicon Models for Statistical Machine Translation using a Maximum Entropy Approach
... the maximum en- tropy ...dependent models, compute automatic sen- tence splitting and to improve word reordering in ...translation models instead of those proposed in (Brown et ...a maximum ... See full document
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Mathematical and computational modeling in biology at multiple scales
... Our first example of applying entropy for inductive inference is in silico drug discovery. Virtual screening has attracted much attention in the pharmaceutical industry [12,13]. It provides a more economical way ... See full document
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Latent Semantic Information in Maximum Entropy Language Models for Conversational Speech Recognition
... [r] ... See full document
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A Maximum Entropy Inspired Parser
... We have presented a lexicalized Markov grammar parsing model that achieves using the now standard training/testing/development sections of the Penn treebank an average precision/recall o[r] ... See full document
8
Maximum entropy and learning theory
... Figure 4: Experimentally determined and predicted values of total error across the training density after competitive learning was performed using a 20-neuron network trained to various [r] ... See full document
19
Generalized Maximum Entropy Estimation
... the maximum entropy subject to finite moment constraints can be approximated by using duality of convex ...the maximum entropy principle with generalized regularization measures, that as a ... See full document
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Discriminative Training and Maximum Entropy Models for Statistical Machine Translation
... We present a framework for statistical machine translation of natural languages based on direct maximum entropy mod- els, which contains the widely used sour- ce-channel approach as a special case. All ... See full document
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