scholarly journals Discovering Fine-Grained Sentiment with Latent Variable Structured Prediction Models

2011 ◽  
pp. 368-374 ◽  
Author(s):  
Oscar Täckström ◽  
Ryan McDonald
Keyword(s):  
Latent Variable ◽  
Prediction Models ◽  
Structured Prediction ◽  
Fine Grained

scholarly journals AutoSense Model for Word Sense Induction

2019 ◽  
Vol 33 ◽  
pp. 6212-6219 ◽  
Author(s):  
Reinald Kim Amplayo ◽  
Seung-won Hwang ◽  
Min Song
Keyword(s):  
Latent Variable ◽  
Word Sense ◽  
Name Disambiguation ◽  
Variable Model ◽  
Fine Grained ◽  
Word Sense Induction ◽  
Author Name Disambiguation ◽  
Competing Models ◽  
Word Senses ◽  
Better Than

Word sense induction (WSI), or the task of automatically discovering multiple senses or meanings of a word, has three main challenges: domain adaptability, novel sense detection, and sense granularity flexibility. While current latent variable models are known to solve the first two challenges, they are not flexible to different word sense granularities, which differ very much among words, from aardvark with one sense, to play with over 50 senses. Current models either require hyperparameter tuning or nonparametric induction of the number of senses, which we find both to be ineffective. Thus, we aim to eliminate these requirements and solve the sense granularity problem by proposing AutoSense, a latent variable model based on two observations: (1) senses are represented as a distribution over topics, and (2) senses generate pairings between the target word and its neighboring word. These observations alleviate the problem by (a) throwing garbage senses and (b) additionally inducing fine-grained word senses. Results show great improvements over the stateof-the-art models on popular WSI datasets. We also show that AutoSense is able to learn the appropriate sense granularity of a word. Finally, we apply AutoSense to the unsupervised author name disambiguation task where the sense granularity problem is more evident and show that AutoSense is evidently better than competing models. We share our data and code here: https://github.com/rktamplayo/AutoSense.

scholarly journals Assessment for Thermal Conductivity of Frozen Soil Based on Nonlinear Regression and Support Vector Regression Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fu-Qing Cui ◽  
Wei Zhang ◽  
Zhi-Yun Liu ◽  
Wei Wang ◽  
Jian-bing Chen ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Support Vector Regression ◽  
Nonlinear Regression ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Frozen Soil ◽  
Support Vector ◽  
Soil Thermal Conductivity ◽  
Fine Grained ◽  
Fine Grained Soil

The comprehensive understanding of the variation law of soil thermal conductivity is the prerequisite of design and construction of engineering applications in permafrost regions. Compared with the unfrozen soil, the specimen preparation and experimental procedures of frozen soil thermal conductivity testing are more complex and challengeable. In this work, considering for essentially multiphase and porous structural characteristic information reflection of unfrozen soil thermal conductivity, prediction models of frozen soil thermal conductivity using nonlinear regression and Support Vector Regression (SVR) methods have been developed. Thermal conductivity of multiple types of soil samples which are sampled from the Qinghai-Tibet Engineering Corridor (QTEC) are tested by the transient plane source (TPS) method. Correlations of thermal conductivity between unfrozen and frozen soil has been analyzed and recognized. Based on the measurement data of unfrozen soil thermal conductivity, the prediction models of frozen soil thermal conductivity for 7 typical soils in the QTEC are proposed. To further facilitate engineering applications, the prediction models of two soil categories (coarse and fine-grained soil) have also been proposed. The results demonstrate that, compared with nonideal prediction accuracy of using water content and dry density as the fitting parameter, the ternary fitting model has a higher thermal conductivity prediction accuracy for 7 types of frozen soils (more than 98% of the soil specimens’ relative error are within 20%). The SVR model can further improve the frozen soil thermal conductivity prediction accuracy and more than 98% of the soil specimens’ relative error are within 15%. For coarse and fine-grained soil categories, the above two models still have reliable prediction accuracy and determine coefficient (R2) ranges from 0.8 to 0.91, which validates the applicability for small sample soils. This study provides feasible prediction models for frozen soil thermal conductivity and guidelines of the thermal design and freeze-thaw damage prevention for engineering structures in cold regions.

scholarly journals A unified framework for incorporating decision making into explanations of business failure

2015 ◽  
Vol 115 (7) ◽  
pp. 1341-1357 ◽  
Author(s):  
Joseph Amankwah-Amoah
Keyword(s):  
Human Capital ◽  
Prediction Models ◽  
Decision Makers ◽  
Business Failure ◽  
Unified Framework ◽  
Firm Level ◽  
Content Type ◽  
Fine Grained ◽  
Institutional Linkages ◽  
Learning From Others

Purpose – The purpose of this paper is to examine how decision-maker attributes unfold to precipitate organisational failure. The analysis brings to light how key attributes such as information-processing capabilities and human capital decay interact to bring about business decline and exit. Design/methodology/approach – The study is based on an integrated review and conceptualisation of the literature. Findings – The study articulates how a set of attributes of decision makers, i.e. human capital obsolescence, powerlessness, meaninglessness and institutional linkages, contributes to organisational failure. Research limitations/implications – The paper concludes by setting out an array of strategies of learning from others’ failures. Originality/value – In spite of a growing body of research on organisational failure, scholars have placed overwhelming emphasis on ecological explanations and business failure prediction models. The study moves beyond the ecological explanations to offer a more fine-grained analysis of firm-level factors that precipitate business failure.

scholarly journals IsoResolve: predicting splice isoform functions by integrating gene and isoform-level features with domain adaptation

2020 ◽  
Author(s):  
Hong-Dong Li ◽  
Changhuo Yang ◽  
Zhimin Zhang ◽  
Mengyun Yang ◽  
Fang-Xiang Wu ◽  
...  
Keyword(s):  
Latent Variables ◽  
Functional Annotation ◽  
Latent Variable ◽  
Prediction Models ◽  
Domain Adaptation ◽  
Single Gene ◽  
Function Prediction ◽  
Supplementary Information ◽  
Similar Distribution ◽  
Gene Functions

Abstract Motivation High resolution annotation of gene functions is a central goal in functional genomics. A single gene may produce multiple isoforms with different functions through alternative splicing. Conventional approaches, however, consider a gene as a single entity without differentiating these functionally different isoforms. Towards understanding gene functions at higher resolution, recent efforts have focused on predicting the functions of isoforms. However, the performance of existing methods is far from satisfactory mainly because of the lack of isoform-level functional annotation. Results We present IsoResolve, a novel approach for isoform function prediction, which leverages the information from gene function prediction models with domain adaptation (DA). IsoResolve treats gene-level and isoform-level features as source and target domains, respectively. It uses DA to project the two domains into a latent variable space in such a way that the latent variables from the two domains have similar distribution, which enables the gene domain information to be leveraged for isoform function prediction. We systematically evaluated the performance of IsoResolve in predicting functions. Compared with five state-of-the-art methods, IsoResolve achieved significantly better performance. IsoResolve was further validated by case studies of genes with isoform-level functional annotation. Availability and implementation IsoResolve is freely available at https://github.com/genemine/IsoResolve. Supplementary information Supplementary data are available at Bioinformatics online.

Parsing Models for Identifying Multiword Expressions

2013 ◽  
Vol 39 (1) ◽  
pp. 195-227 ◽  
Author(s):  
Spence Green ◽  
Marie-Catherine de Marneffe ◽  
Christopher D. Manning
Keyword(s):  
Language Processing ◽  
Prediction Models ◽  
State Of The Art ◽  
Lexical Representation ◽  
Structured Prediction ◽  
Identification Accuracy ◽  
Syntactic Analysis ◽  
Multiword Expressions ◽  
Context Free ◽  
Alternative Theories

Multiword expressions lie at the syntax/semantics interface and have motivated alternative theories of syntax like Construction Grammar. Until now, however, syntactic analysis and multiword expression identification have been modeled separately in natural language processing. We develop two structured prediction models for joint parsing and multiword expression identification. The first is based on context-free grammars and the second uses tree substitution grammars, a formalism that can store larger syntactic fragments. Our experiments show that both models can identify multiword expressions with much higher accuracy than a state-of-the-art system based on word co-occurrence statistics. We experiment with Arabic and French, which both have pervasive multiword expressions. Relative to English, they also have richer morphology, which induces lexical sparsity in finite corpora. To combat this sparsity, we develop a simple factored lexical representation for the context-free parsing model. Morphological analyses are automatically transformed into rich feature tags that are scored jointly with lexical items. This technique, which we call a factored lexicon, improves both standard parsing and multiword expression identification accuracy.

scholarly journals Structured Output Learning with Conditional Generative Flows

2020 ◽  
Vol 34 (04) ◽  
pp. 5005-5012 ◽  
Author(s):  
You Lu ◽  
Bert Huang
Keyword(s):  
Prediction Models ◽  
State Of The Art ◽  
Prediction Method ◽  
Structured Prediction ◽  
Conditional Likelihood ◽  
Structured Output Learning ◽  
Approximate Likelihood ◽  
Structured Output ◽  
Prediction Problems ◽  
The Relationship

Traditional structured prediction models try to learn the conditional likelihood, i.e., p(y|x), to capture the relationship between the structured output y and the input features x. For many models, computing the likelihood is intractable. These models are therefore hard to train, requiring the use of surrogate objectives or variational inference to approximate likelihood. In this paper, we propose conditional Glow (c-Glow), a conditional generative flow for structured output learning. C-Glow benefits from the ability of flow-based models to compute p(y|x exactly and efficiently. Learning with c-Glow does not require a surrogate objective or performing inference during training. Once trained, we can directly and efficiently generate conditional samples. We develop a sample-based prediction method, which can use this advantage to do efficient and effective inference. In our experiments, we test c-Glow on five different tasks. C-Glow outperforms the state-of-the-art baselines in some tasks and predicts comparable outputs in the other tasks. The results show that c-Glow is versatile and is applicable to many different structured prediction problems.

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