EM Algorithm

This paper snggests a method to align Korean-English parallel corpus. '1?he structural dissimilarity between Korean and Indo-European languages requires more flexible measures to evaluate the alignment candidates between the bilin- …...

This paper describes an experimental com- parison of three unsupervised learning al- gorithms that distinguish the sense of an ambiguous word in untagged text. The methods described in this paper, …...

We propose a new method of classifying documents into categories. We define for each category a finite mixture model based on soft clustering of words. We treat the problem of …...

We present a syntax-based statistical translation model. Our model trans- forms a source-language parse tree into a target-language string by apply- ing stochastic operations at each node. These operations capture …...

We present a generative distributional model for the unsupervised induction of natural language syntax which explicitly models constituent yields and con- texts. Parameter search with EM produces higher quality analyses …...

Combining a naive Bayes classifier with the EM algorithm is one of the promising ap- proaches for making use of unlabeled data for disambiguation tasks when using local con- text …...

Collocation translation is important for machine translation and many other NLP tasks. Unlike previous methods using bilingual parallel corpora, this paper presents a new method for acquiring collocation translations by …...

. Document clustering has many uses in natural language tools and applications. For instance, summarizing sets of documents that all describe the same event requires first identifying and grouping those …...

. We build a class-based selection preference sub-model to in- corporate external semantic knowledge from two Chinese electronic se- mantic dictionaries. This sub-model is combined with modifier-head gen- eration sub-model. …...

We first show how a structural locality bias can improve the accuracy of state-of-the-art dependency grammar induction models trained by EM from unannotated examples (Klein and Manning, 2004). Next, by …...

Mixture modelling is a standard pattern classification technique. However, in statistical machine translation, the use of mixture modelling is still un- explored. Two main advantages of the mixture approach are …...

This paper demonstrates how unsupervised tech- niques can be used to learn models of deep linguis- tic structure. Determining the semantic roles of a verb’s dependents is an important step …...

We investigate why weights from generative mod- els underperform heuristic estimates in phrase- based machine translation. We first propose a sim- ple generative, phrase-based model and verify that its estimates …...

We show that phrase structures in Penn Tree- bank style parses are not optimal for syntax- based machine translation. We exploit a se- ries of binarization methods to restructure the …...

Traditional wisdom holds that once docu- ments are turned into bag-of-words (unigram count) vectors, word orders are completely lost. We introduce an approach that, perhaps surprisingly, is able to learn …...

We examine the problem of overcoming noisy word-level alignments when learn- ing tree-to-string translation rules. Our approach introduces new rules, and re- estimates rule probabilities using EM. The major obstacles …...

The (batch) EM algorithm plays an important role in unsupervised induction, but it some- times suffers from slow convergence. In this paper, we show that online variants (1) provide significant …...

This paper proposes a novel semi- supervised word alignment technique called EMDC that integrates discrimina- tive and generative methods. A discrim- inative aligner is used to find high preci- sion …...

In opinion mining of product reviews, one of- ten wants to produce a summary of opinions based on product features/attributes. Howev- er, for the same feature, people can express it …...

We present a novel approach to distributional- only, fully unsupervised, POS tagging, based on an adaptation of the EM algorithm for the esti- mation of a Gaussian mixture. In this …...

Letter-phoneme alignment is usually gen- erated by a straightforward application of the EM algorithm. We explore several al- ternative alignment methods that employ phonetics, integer programming, and sets of constraints, …...

We show how features can easily be added to standard generative models for unsuper- vised learning, without requiring complex new training methods. In particular, each component multinomial of a generative …...

We propose semantic role features for a Tree-to-String transducer to model the re- ordering/deletion of source-side semantic roles. These semantic features, as well as the Tree-to-String templates, are trained based …...

Automated grammar correction techniques have seen improvement over the years, but there is still much room for increased perfor- mance. Current correction techniques mainly focus on identifying and correcting a …...

Parse trees are indispensable to the existing tree-based translation models. However, there exist two major challenges in utilizing parse trees: 1) For most language pairs, it is hard to get …...

This paper presents a collapsed variational Bayesian inference algorithm for PCFGs that has the advantages of two dominant Bayesian training algorithms for PCFGs, namely variational Bayesian inference and Markov chain …...

Latent-variable PCFGs (L-PCFGs) are highly successful model for natural languagea parsing. Recent work (Cohen et al., 2012) has introduced a spectral algorithm for param- eter estimation of L-PCFGs, which—unlike the …...

Current word alignment models for statisti- cal machine translation do not address mor- phology beyond merely splitting words. We present a two-level alignment model that dis- tinguishes between words and …...

We introduce a provably correct learning algorithm for latent-variable PCFGs. The algorithm relies on two steps: first, the use of a matrix-decomposition algorithm ap- plied to a co-occurrence matrix estimated …...

We address the problem of discovering topical phrases or “aspects” from microblogging sites like Twitter, that correspond to key talking points or buzz around a particular topic or entity of …...

Data-driven refinement of non-terminal categories has been demonstrated to be a reliable technique for improving mono- lingual parsing with PCFGs. In this pa- per, we extend these techniques to learn …...

In regard to document classification, semi- supervised learning using the Naive Bayes method and EM algorithm was a great suc- cess, and we refer to this method as NBEM in …...

Cross-domain sentiment classification (CSC) aims at learning a sentiment classifier for unlabeled data in the target domain based on the labeled data from a different source domain. Due to the …...

Non-compositionality of multiword ex- pressions is an intriguing problem that can be the source of error in a variety of NLP tasks such as language genera- tion, machine translation and …...

AbstractWe propose a structured generative latent variable model that integrates information from multiple contextual representations for Word Sense Induction. Our approach jointly models global lexical, local lexical and dependency syntactic …...