[PDF] Top 20 Large Margin Learning of Submodular Summarization Models
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Large Margin Learning of Submodular Summarization Models
... For the pairwise model we use cosine similar- ity between sentences using only words in a given word group during computation. For the word coverage model we create separate features for covering words in different ... See full document
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Framework of Automatic Text Summarization Using Reinforcement Learning
... One of the most well-known extractive ap- proaches is maximal marginal relevance (MMR), which scores each textual unit and extracts the unit that has the highest score in terms of the MMR cri- teria (Goldstein et al., ... See full document
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Summarization of Multi Document Topic Hierarchies using Submodular Mixtures
... a submodular optimization problem on a topic hierarchy using the documents as ...a submodular function that expresses coherence between the chosen topics and this ...a large-margin framework ... See full document
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Interpretable Preference Learning: A Game Theoretic Framework for Large Margin On-Line Feature and Rule Learning
... two-players zero-sum game, in which the considered hy- potheses spaces consist in a set of preference prototypes along with (possibly non-linear) features. Moreover, we show how feature selection naturally comes as a ... See full document
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Learning Multi-Task Communication with Message Passing for Sequence Learning
... multi-task learning, which boosts the performance by a large ...as large as DC-MTL, IC-MTL is efficient to ...our models by incorporating the adversar- ial training mechanism introduced in ... See full document
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Multi document Summarization via Budgeted Maximization of Submodular Functions
... mary can be viewed as a combinatorial optimiza- tion problem which is NP-hard to solve (McDon- ald, 2007). One of the standard methods for this problem is called Maximum Marginal Rele- vance (MMR) (Dang, 2005)(Carbonell ... See full document
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A Combination of Topic Models with Max margin Learning for Relation Detection
... Entity relation detection (ERD) aims at finding rela- tions between pairs of Named Entities (NEs) in text. Availability of annotated corpora (NIST, 2003; Dod- dington et al., 2004) and introduction of shared tasks (e.g. ... See full document
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Distance Metric Learning for Large Margin Nearest Neighbor Classification
... metric learning in energy-based models (Chopra et ...metric learning as an instance of semidefinite ...metric learning based on eigenvalue problems (Shental et ... See full document
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Minibatch and Parallelization for Online Large Margin Structured Learning
... batch learning such as CRF (Lafferty et ...online learning is much harder to parallelize due to the de- pendency between examples, ...parallelly-learned models, and the speedup is typ- ically very ... See full document
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Optimization Strategies for Online Large Margin Learning in Machine Translation
... Statistical machine translation (SMT) systems rep- resent knowledge sources in the form of features, and rely on parameters, or weights, on each feature, to score alternative translations. As in all statistical ... See full document
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Mr MIRA: Open Source Large Margin Structured Learning on MapReduce
... Structured learning problems such as sequence la- beling or parsing, where the output has a rich in- ternal structure, commonly arise in ...batch learning algorithms adapted for structured learning ... See full document
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Statistical Machine Learning For Information Retrieval Adam Berger pdf
... The promising empirical results reported herein do not indicate that “classic” IR tech- niques, like refined term-weighting formulae, query expansion, (pseudo)-relevance feedback, and stopword lists, are unnecessary. The ... See full document
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Unsupervised Abstractive Meeting Summarization with Multi Sentence Compression and Budgeted Submodular Maximization
... Description: a supervised end-to-end framework for abstractive meeting summarization. Commu- nity Detection is performed by (1) building an ut- terance graph with a logistic regression classifier, and (2) applying ... See full document
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Convex Analysis for Minimizing and Learning Submodular Set Functions
... Several submodular minimization problems arising in machine learning and other domains have structure that allows for solving them more ...of submodular minimization problems that can be solved ... See full document
115
Multicategory Large-Margin Unified Machines
... We propose a new group of Multicategory Large-margin Unified Machines (MLUMs) in this paper. Similar to the binary case, the MLUM is a broad family that embraces many of the afore- mentioned classifiers as ... See full document
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Perceptron like Large Margin Classifiers
... directional margin which is not lowered with the number of ...geometric margin at least as large as the directional margin β ...directional margin and consequently a geometric ... See full document
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Perceptron Like Large Margin Classifiers
... a margin β = r 2 is asked for with a step parameter being set to r 2 ...imposed margin constraint without exhausting the upper number of checks the round is considered ...larger margin than the ... See full document
162
Confidence Weighted Learning of Factored Discriminative Language Models
... Language models based on word surface forms only are unable to benefit from avail- able linguistic knowledge, and tend to suffer from poor estimates for rare ...confidence-weighted learning, a form of ... See full document
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Provable Fast Greedy Compressive Summarization with Any Monotone Submodular Function
... pressive summarization task as a combinatorial op- timization problem with a tree constraint, which we call the submodular tree knapsack problem ...the submodular cost submod- ular knapsack problem ... See full document
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A Machine Learning Approach to Sentence Ordering for Multidocument Summarization and Its Evaluation
... MDS is the task of generating a human readable summary from a given set of documents. With the increasing amount of texts available in electronic format, automatic text summarization has become necessary. It can ... See full document
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