[PDF] Top 20 Name Origin Recognition Using Maximum Entropy Model and Diverse Features
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Name Origin Recognition Using Maximum Entropy Model and Diverse Features
... Name origin recognition is to identify the source language of a personal or location ...the name origin recognition as a multi-class classification problem and approach the ... See full document
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Improving Name Origin Recognition with Context Features and Unlabelled Data
... the name lan- guage of origin, we use the discrimi- native classification maximum entropy model and view the task as a classifica- tion ...learning using list of names out of ... See full document
7
Identification of Basic Phrases for Kazakh Language using Maximum Entropy Model
... statistical model recognition need to select a high correlation, and the Kazakh lan- guage features to train with good ...Establish model based on rule of the language, this work se- lected ... See full document
8
Exploiting Acoustic and Syntactic Features for Prosody Labeling in a Maximum Entropy Framework
... speech recognition (Hasegawa-Johnson et ...speech recognition (ASR) system is noisy and hence, the acoustic features are more useful in pre- dicting prosody than the hypothesized lexical tran- script ... See full document
8
Enhance Chinese Medical Name Entity Recognition with Etymon Features
... for name entity ...include Maximum Entropy (ME) [2], Hidden Markov Mode (HMM) [3] and Conditional Random Fields (CRF) [4] and so ...entity recognition. The network can extract features ... See full document
5
Named Entity Recognition: A Maximum Entropy Approach Using Global Information
... at using global information can be found in (Borthwick, ...additional maximum entropy classifier that tries to correct mistakes by using reference resolu- ...error-correction model, he ... See full document
7
Maximum Entropy for Chinese Comma Classification with Rich Linguistic Features
... with maximum entropy, on the basis of utilizing a set of context features, lexical features and dependency tree features extracted from the corpus of Chinese discourse built by ...The ... See full document
7
Maximum Entropy Approach based Named Entity Recognition in Punjabi Language
... Markov Model (HMM) [1], Maximum Entropy Model [2], Decision Tree [19], Conditional Random Field (CRF) [13] and Support Vector Machine (SVM) ...a Maximum Entropy Markov ... See full document
5
Using a maximum entropy model to build segmentation lattices for MT
... sufficiently diverse source segmen- tation ...a maximum entropy model of compound word splitting that relies on a few general features that can be used to generate segmentation lat- ... See full document
9
Grammatical Error Detection and Correction using a Single Maximum Entropy Model
... In contrast, we follow the work in (Jia et al., 2013a; Zhao et al., 2009a), integrating everything into one model. This integrated system holds a merit that a one-way feature selection benefits the whole system ... See full document
9
Chinese Tagging Based on Maximum Entropy Model
... It can be seen that our results were unexpectedly low in accuracy. After releasing the results, we found that the problem was due to the encoding problem of our submitted result files. The problem probably occurred after ... See full document
5
Maximum Entropy Model Learning of the Translation Rules
... Maximum Entropy Model Learning of the Translation Rules M a x i m u m E n t r o p y M o d e l Learning o f t h e T r a n s l a t i o n R u l e s K e n g o S a t o and M a s a k a z u N a k a n i s h i[.] ... See full document
5
Recurrent neural network models for disease name recognition using domain invariant features
... based model such as window based network to recognize named entity in Portuguese and Spanish ...shape features of words in character level word embed- ding, and used it as feature with concatenation of word ... See full document
10
A Maximum Entropy/Minimum Divergence Translation Model
... test corpus perplexity... test corpus perplexity.[r] ... See full document
8
Modeling Joint Entity and Relation Extraction with Table Representation
... dependency features like traditional sequential labeling for ...our model can include other non-local fea- tures between entities (Ratinov and Roth, 2009), we do not include them expecting that global fea- ... See full document
12
Maximum Entropy Model Learning of the Translation Rules
... We have described an approach to learn the translation rules from parallel corpora based on the maximum entropy method.. As feature functions, we have defined two models, one with co-occ[r] ... See full document
5
A Maximum Entropy Model for Prepositional Phrase Attachment
... A Maximum Entropy Model for Prepositional Phrase Attachment A Maximum Entropy Model for Prepositional Phrase Attachment A d w a i t R a t n a p a r k h i , J e f f Reynar,* a n d S a l i m R o u k o s[.] ... See full document
6
A Maximum Entropy Model for Part Of Speech Tagging
... The model with specialized features does not perform much better t h a n the baseline model, and further discovery or refinement of word-based fea- tures is difficult given the inconsist[r] ... See full document
10
Structuring E Commerce Inventory
... Structuring inventory in the e-commerce domain raises several challenges. First, one needs to iden- tify and extract the names and the values used by individual sellers from unstructured textual descrip- tions. Second, ... See full document
10
Speech/Non-Speech Segmentation Based on Phoneme Recognition Features
... phoneme recognition features suitable for speech/non-speech discrimination ...previous model-based approaches, where speech and non-speech classes were usually modeled by several models, we de- velop ... See full document
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