Kai-Wei Chang

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Thorsten Joachims66
Yoram Singer47
Chih-Jen Lin44
Dan Roth43
Shai Shalev-Shwartz37
46Dan Roth
32Chih-Jen Lin
28Cho-Jui Hsieh
23Ian En-Hsu Yen
23Ivor W. Tsang

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2003
2017

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LIBLINEAR: A Library for Large Linear Classification
LIBLINEAR is an open source library for large-scale linear classification. It supports logistic regression and linear support vector machines. We provide easy-to-use command-line tools and library calls for users and developers. Comprehensive documents are available for both beginners and advanced users. Experiments demonstrate that LIBLINEAR is very… (More)
A dual coordinate descent method for large-scale linear SVM
In many applications, data appear with a huge number of instances as well as features. Linear Support Vector Machines (SVM) is one of the most popular tools to deal with such large-scale sparse data. This paper presents a novel dual coordinate descent method for linear SVM with L1-and L2-loss functions. The proposed method is simple and reaches an… (More)
A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification
Large-scale linear classification is widely used in many areas. The L1-regularized form can be applied for feature selection; however, its non-differentiability causes more difficulties in training. Although various optimization methods have been proposed in recent years, these have not yet been compared suitably. In this paper, we first broadly review… (More)
Typed Tensor Decomposition of Knowledge Bases for Relation Extraction
While relation extraction has traditionally been viewed as a task relying solely on textual data, recent work has shown that by taking as input existing facts in the form of entity-relation triples from both knowledge bases and textual data, the performance of relation extraction can be improved significantly. Following this new paradigm, we propose a… (More)
Large Linear Classification When Data Cannot Fit in Memory
Recent advances in linear classification have shown that for applications such as document classification, the training can be extremely efficient. However, most of the existing training methods are designed by assuming that data can be stored in the computer memory. These methods cannot be easily applied to data larger than the memory capacity due to the… (More)
A sequential dual method for large scale multi-class linear svms
Efficient training of direct multi-class formulations of linear Support Vector Machines is very useful in applications such as text classification with a huge number examples as well as features. This paper presents a fast dual method for this training. The main idea is to sequentially traverse through the training set and optimize the dual variables… (More)
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM
Kernel techniques have long been used in SVM to handle linearly inseparable problems by transforming data to a high dimensional space, but training and testing large data sets is often time consuming. In contrast, we can efficiently train and test much larger data sets using linear SVM without kernels. In this work, we apply fast linear-SVM methods to the… (More)
A Constrained Latent Variable Model for Coreference Resolution
Coreference resolution is a well known clustering task in Natural Language Processing. In this paper, we describe the Latent Left Linking model (L 3 M), a novel, principled, and linguistically motivated latent structured prediction approach to coreference resolution. We show that L 3 M admits efficient inference and can be augmented with knowledge-based… (More)
Coordinate Descent Method for Large-scale L2-loss Linear SVM
Linear support vector machines (SVM) are useful for classifying large-scale sparse data. Problems with sparse features are common in applications such as document classification and natural language processing. In this paper, we propose a novel coordinate descent algorithm for training linear SVM with the L2-loss function. At each step, the proposed method… (More)
The Illinois-Columbia System in the CoNLL-2014 Shared Task
The CoNLL-2014 shared task is an extension of last year's shared task and fo-cuses on correcting grammatical errors in essays written by non-native learners of English. In this paper, we describe the Illinois-Columbia system that participated in the shared task. Our system ranked second on the original annotations and first on the revised annotations. The… (More)