Latent variable models

Xem 1-20 trên 23 kết quả Latent variable models
  • The present paper describes a robust approach for abbreviating terms. First, in order to incorporate non-local information into abbreviation generation tasks, we present both implicit and explicit solutions: the latent variable model, or alternatively, the label encoding approach with global information. Although the two approaches compete with one another, we demonstrate that these approaches are also complementary. By combining these two approaches, experiments revealed that the proposed abbreviation generator achieved the best results for both the Chinese and English languages. ...

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  • We propose models for semantic orientations of phrases as well as classification methods based on the models. Although each phrase consists of multiple words, the semantic orientation of the phrase is not a mere sum of the orientations of the component words. Some words can invert the orientation. In order to capture the property of such phrases, we introduce latent variables into the models. Through experiments, we show that the proposed latent variable models work well in the classification of semantic orientations of phrases and achieved nearly 82% classification accuracy. ...

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  • We propose a latent variable model to enhance historical analysis of large corpora. This work extends prior work in topic modelling by incorporating metadata, and the interactions between the components in metadata, in a general way. To test this, we collect a corpus of slavery-related United States property law judgements sampled from the years 1730 to 1866. We study the language use in these legal cases, with a special focus on shifts in opinions on controversial topics across different regions....

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  • Large-scale discriminative machine translation promises to further the state-of-the-art, but has failed to deliver convincing gains over current heuristic frequency count systems. We argue that a principle reason for this failure is not dealing with multiple, equivalent translations. We present a translation model which models derivations as a latent variable, in both training and decoding, and is fully discriminative and globally optimised. Results show that accounting for multiple derivations does indeed improve performance.

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  • This paper describes the application of so-called topic models to selectional preference induction. Three models related to Latent Dirichlet Allocation, a proven method for modelling document-word cooccurrences, are presented and evaluated on datasets of human plausibility judgements. Compared to previously proposed techniques, these models perform very competitively, especially for infrequent predicate-argument combinations where they exceed the quality of Web-scale predictions while using relatively little data. ...

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  • We derive two variants of a semi-supervised model for fine-grained sentiment analysis. Both models leverage abundant natural supervision in the form of review ratings, as well as a small amount of manually crafted sentence labels, to learn sentence-level sentiment classifiers. The proposed model is a fusion of a fully supervised structured conditional model and its partially supervised counterpart. This allows for highly efficient estimation and inference algorithms with rich feature definitions. ...

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  • This paper presents a set of Bayesian methods for automatically extending the W ORD N ET ontology with new concepts and annotating existing concepts with generic property fields, or attributes. We base our approach on Latent Dirichlet Allocation and evaluate along two dimensions: (1) the precision of the ranked lists of attributes, and (2) the quality of the attribute assignments to W ORD N ET concepts. In all cases we find that the principled LDA-based approaches outperform previously proposed heuristic methods, greatly improving the specificity of attributes at each concept. ...

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  • We introduce a spectral learning algorithm for latent-variable PCFGs (Petrov et al., 2006). Under a separability (singular value) condition, we prove that the method provides consistent parameter estimates.1 Introduction Statistical models with hidden or latent variables are of great importance in natural language processing, speech, and many other fields. The EM algorithm is a remarkably successful method for parameter estimation within these models: it is simple, it is often relatively efficient, and it has well understood formal properties. ...

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  • Latent conditional models have become popular recently in both natural language processing and vision processing communities. However, establishing an effective and efficient inference method on latent conditional models remains a question. In this paper, we describe the latent-dynamic inference (LDI), which is able to produce the optimal label sequence on latent conditional models by using efficient search strategy and dynamic programming.

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  • Sentence Similarity is the process of computing a similarity score between two sentences. Previous sentence similarity work finds that latent semantics approaches to the problem do not perform well due to insufficient information in single sentences. In this paper, we show that by carefully handling words that are not in the sentences (missing words), we can train a reliable latent variable model on sentences.

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  • Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí hóa học quốc tế đề tài : Self-reported physical and mental health status and quality of life in adolescents: a latent variable mediation model

    pdf11p panasonic07 04-01-2012 35 3   Download

  • We analyse the mathematical structure of portfolio credit risk models with particular regard to the modelling of dependence between default events in these models. We explore the role of copulas in latent variable models (the approach that underlies KMV and CreditMetrics) and use non-Gaussian copulas to present extensions to standard industry models. We explore the role of the mixing distribution in Bernoulli mixture models (the approach underlying CreditRisk+) and derive large portfolio approximations for the loss distribution.

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  • We consider a semi-supervised setting for domain adaptation where only unlabeled data is available for the target domain. One way to tackle this problem is to train a generative model with latent variables on the mixture of data from the source and target domains. Such a model would cluster features in both domains and ensure that at least some of the latent variables are predictive of the label on the source domain.

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  • This paper defines a generative probabilistic model of parse trees, which we call PCFG-LA. This model is an extension of PCFG in which non-terminal symbols are augmented with latent variables. Finegrained CFG rules are automatically induced from a parsed corpus by training a PCFG-LA model using an EM-algorithm. Because exact parsing with a PCFG-LA is NP-hard, several approximations are described and empirically compared. In experiments using the Penn WSJ corpus, our automatically trained model gave a per40 formance of 86.

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  • This paper presents a novel sequence labeling model based on the latent-variable semiMarkov conditional random fields for jointly extracting argument roles of events from texts. The model takes in coarse mention and type information and predicts argument roles for a given event template. This paper addresses the event extraction problem in a primarily unsupervised setting, where no labeled training instances are available.

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  • This paper presents a probabilistic model for sense disambiguation which chooses the best sense based on the conditional probability of sense paraphrases given a context. We use a topic model to decompose this conditional probability into two conditional probabilities with latent variables. We propose three different instantiations of the model for solving sense disambiguation problems with different degrees of resource availability.

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  • We describe two probabilistic models for unsupervised word-sense disambiguation using parallel corpora. The first model, which we call the Sense model, builds on the work of Diab and Resnik (2002) that uses both parallel text and a sense inventory for the target language, and recasts their approach in a probabilistic framework. The second model, which we call the Concept model, is a hierarchical model that uses a concept latent variable to relate different language specific sense labels.

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  • Chaprer 23 LATENT VARIABLE MODELS IN ECONOMETRICS DENNIS J. AIGNER This chapter discusses classical estimation methods for limited dependent variable (LDV) models that employ Monte Carlo simulation techniques to overcome computational problems in such models.

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  • Probabilistic Latent Semantic Analysis (PLSA) models have been shown to provide a better model for capturing polysemy and synonymy than Latent Semantic Analysis (LSA). However, the parameters of a PLSA model are trained using the Expectation Maximization (EM) algorithm, and as a result, the trained model is dependent on the initialization values so that performance can be highly variable. In this paper we present a method for using LSA analysis to initialize a PLSA model.

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  • Almost all research in the social and behavioral sciences, and also in eco­ nomic and marketing research, criminological research, and social medical research deals with the analysis of categorical data. Categorical data are quantified as either nominal or ordinal variables. This volume is a collec­ tion of up-to-date studies on modern categorical data analysis methods, emphasizing their application to relevant and interesting data sets.

    pdf274p banhkem0908 24-11-2012 44 3   Download



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