[PDF] Top 20 Log Linear Models for Word Alignment
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Log Linear Models for Word Alignment
... for word align- ment based on log-linear ...ables. Log-linear models allow statis- tical alignment models to be easily ex- tended by incorporating syntactic infor- ... See full document
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Unsupervised Morphological Segmentation with Log Linear Models
... We evaluated our system on two datasets. Our main evaluation is on a multi-lingual dataset constructed by Snyder & Barzilay (2008a; 2008b). It consists of 6192 short parallel phrases in Hebrew, Arabic, Ara- maic (a ... See full document
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Log-linear Models for Uyghur Segmentation in Spoken Language Translation
... in log-linear ...monoCoOccur models learn features unsurpervis- ...proposed log-linear based morphological seg- mentation model achieved best translation results among seven ... See full document
9
Discriminative Word Alignment by Linear Modeling
... For Models 3 and 4, maximization over all alignments cannot be efficiently carried out as the corresponding search problem is ...Viterbi alignment of a simple model such as Model 2 or HMM. This ... See full document
38
Using Log-linear Models for Tuning Machine Translation Output
... A number of feature functions have been explored in various system implementations (e.g. Oepen et al. 2007, Liu et al. 2007, Quirk 2007), separating the features roughly into source features, channel features and target ... See full document
8
Multi Word Expression Sensitive Word Alignment
... standard log-linear phrase-based MT system based on ...for word alignment, which we compare to our modified ...refined alignment from bidirectional ...IBM Models 1-5, and the ... See full document
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When and why are log linear models self normalizing?
... skip-gram models that treat context words inde- pendently will have sample efficiency multiplicative, rather than exponential, in the size of the condition- ing ... See full document
6
Minimum Risk Annealing for Training Log Linear Models
... Our risk minimization most resembles the work of Rao and Rose (2001), who trained an isolated- word speech recognition system for expected word-error rate. Deterministic annealing has also been used to ... See full document
8
Parsing the WSJ Using CCG and Log Linear Models
... The feature set for the dependency model con- sists of the following types of features: dependency features (with and without distance measures), rule instantiation features (with and without a lexical head), lexical ... See full document
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Contrastive Estimation: Training Log Linear Models on Unlabeled Data
... all models perform worse as knowledge is ...(tag, word) features): the learner can construct a good class-based unigram model of the text (where classes are ... See full document
9
Log Linear Models for Wide Coverage CCG Parsing
... The second step of data generation involves using the parser to create a feature forest for each sentence, using the feature set extracted from CCGbank. The parser is interrupted if a sentence takes longer than 60 ... See full document
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Structured Penalties for Log Linear Language Models
... In this section, we demonstrate empirically the prop- erties of the algorithms summarized in Table 1. We consider four distinct subsets of the Associated Press News (AP-news) text corpus with train-test sizes of 100K-20K ... See full document
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BiTAM: Bilingual Topic AdMixture Models for Word Alignment
... translation models concern mainly explicit logical representations of semantics for machine ...using word-disambiguation schemes (Carpua and Wu, 2005) and non-overlapping bilin- gual word-clusters ... See full document
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Multiple Word Alignment with Profile Hidden Markov Models
... a word to the correct cognate set from a list of cognate sets with the same meaning as the given word, similar to the evaluation of a biologi- cal sequence for membership in a ...one word at a time ... See full document
6
An Unsupervised Method for Uncovering Morphological Chains
... the word formation process and show how to employ the flexibility of log-linear models to incorporate both morpheme and word- level features, while handling transformations of parent ... See full document
12
Extentions to HMM based Statistical Word Alignment Models
... tential of part of speech information to better model translation probabilities and permutation probabili- ties. Melamed (2000) uses a very broad classifica- tion of words (content, function and several punctu- ation ... See full document
8
Computationally Efficient M Estimation of Log Linear Structure Models
... We have presented a new loss function for genera- tively estimating the parameters of log-linear mod- els. The M-estimator is fast to train, requiring no repeated, expensive calculation of normalization ... See full document
8
Normalized Log Linear Interpolation of Backoff Language Models is Efficient
... lated models (Klakow, 1998; Gutkin, 2000), but experiments and adoption were limited due the im- practically high cost of ...the log-linearly interpolated model can be normalized offline in advance and ... See full document
11
Power of edge exclusion tests for graphical log linear models
... Asymptotic multivariate normal approximations to the joint distributions of edge exclusion test statistics for saturated graphical log-linear models, with all variables binary, are derived. ... See full document
15
Analyzing The Suitability Of The Spline Models And Other Forecasting Models In The Estimation Of Cassava Production In Nigeria.
... Grafted polynomial (spline) models are used in econometrics to embark on economic analysis involving time series. It was assumed that different functional forms may fit different segments of a time series or ... See full document
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