nils-herrmann/clean_scirepeval huggingface dataset:
Language and Task Independent Text Categorization with Simple Language Models

We present a simple method for language independent and task independent text categorization learning, based on character-level n-gram language models. Our approach uses simple information theoretic principles and achieves effective performance across a variety of languages and tasks without requiring feature selection or extensive pre-processing. To demonstrate the language and task independence of the proposed technique, we present experimental results on several languages---Greek, English, Chinese and Japanese---in several text categorization problems---language identification, authorship attribution, text genre classification, and topic detection. Our experimental results show that the simple approach achieves state of the art performance in each case.
Semantic n-gram language modeling with the latent maximum entropy principle

We describe a unified probabilistic framework for statistical language modeling-the latent maximum entropy principle-which can effectively incorporate various aspects of natural language, such as local word interaction, syntactic structure and semantic document information. Unlike previous work on maximum entropy methods for language modeling, which only allow explicit features to be modeled, our framework also allows relationships over hidden features to be captured, resulting in a more expressive language model. We describe efficient algorithms for marginalization, inference and normalization in our extended models. We then present experimental results for our approach on the Wall Street Journal corpus.
Language-independent text learning with statistical n-gram language models

In this thesis, we attempt to build language independent text learning systems that do not require significant human intervention. Our solution is based on statistical n-gram language modeling and unsupervised machine learning. Statistical language modeling is concerned with estimating the probability of word sequences, which provides a natural and principled approach to text learning. Statistical n-gram language models model text as a sequence of characters or words and offer the advantage of language independence. Unsupervised machine learning offers the advantage of significantly reducing human labor. We focus on improving performance on three text learning problems by building statistical n-gram language models and by exploiting the value of un-labeled data. These tasks include language and task independent text classification, language independent lexical learning and unsupervised word segmentation, and Chinese text retrieval. 
The first task we consider is language and task independent text classification. We generalize the naive Bayes classifier by incorporating local dependencies between observations (characters or words) with a Markov chain. The result is a Cbhain Abugmented Nbaive (CAN) Bayes classifier. By exploiting sophisticated smoothing techniques from language modeling research, we can deal with the feature explosion problem efficiently in CAN models. Our language modeling based approach avoids many of the ad hoc and error accumulating aspects of feature selection. The approach can work both at the character and word level. 
The second task we consider is language independent lexical learning and unsupervised word segmentation. We propose an unsupervised lexicon construction approach by applying an iterative optimization procedure (the EM algorithm) to n-gram models. We then segment new sequences based on dynamic programming (the Viterbi algorithm). Our approach removes the necessity of manual dictionary construction and allows for adaptive segmentations based on context. To reduce the sparse data and local maxima problems that occur with the traditional EM algorithm, we use a modified EM algorithm that exploits hierarchical structure and employs a self-supervised estimation strategy. 
Finally, we apply unsupervised word segmentation to Chinese text retrieval. Despite its low segmentation accuracy relative to supervised methods, our unsupervised segmentation method has many advantages over traditional word segmentation approaches. Experiments on the TREC-5 and TREC-6 data sets show that unsupervised word segmentation often outperforms traditional segmentation methods in Chinese text retrieval. We also find that the relationship between word segmentation performance and retrieval performance is not monotonic, as commonly expected.
Learning Continuous Latent Variable Models with Bregman Divergences

We present a class of unsupervised statistical learning algorithms that are formulated in terms of minimizing Bregman divergences— a family of generalized entropy measures defined by convex functions. We obtain novel training algorithms that extract hidden latent structure by minimizing a Bregman divergence on training data, subject to a set of non-linear constraints which consider hidden variables. An alternating minimization procedure with nested iterative scaling is proposed to find feasible solutions for the resulting constrained optimization problem. The convergence of this algorithm along with its information geometric properties are characterized.
Automatic basis selection for RBF networks using Stein's unbiased risk estimator

The problem of selecting the appropriate number of basis functions is a critical issue for radial basis function neural networks. An RBF network with an overlay restricted basis gives poor predictions on new data, since the model has too little flexibility. By contrast, an RBF network with too many basis functions also gives poor generalization performance since it is too flexible and fits too much of the noise on the training data. Bias and variance are complementary quantities, and it is necessary to assign the number of basis function optimally in order to achieve the best compromise between them. In this paper we derive a theoretical criterion for assigning the appropriate number of basis functions. We use Stein's unbiased risk estimator (SURE) to drive a genetic criterion that defines the optimum number of basis functions to use for a given problem. The efficacy of this criterion is illustrated experimentally.
Model-Based Least-Squares Policy Evaluation

A popular form of policy evaluation for large Markov Decision Processes (MDPs) is the least-squares temporal differencing( TD) method. Least-squares TD methods handle large MDPs by requiring prior knowledge feature vectors which form a set of basis vectors that compress the system down to tractable levels. Model-based methods have largely been ignored in favour of model-free TD algorithms due to two perceived drawbacks: slower computation time and larger storage requirements. This paper challenges the perceived advantage of the temporal difference method over a model-based method in three distinct ways. First, it provides a new model-based approximate policy estimation method which produces solutions in a faster computation time than Boyan's least-squares TD method. Second, it introduces a new algorithm to derive basis vectors without any prior knowledge of the system. Third, we introduce an iteratively improving model-based value estimator that can run faster than standard TD methods. All algorithms require model storage but remain computationally competitive in terms of accuracy with model-free temporal differencing methods.
Learning Mixture Models with the Latent Maximum Entropy Principle

We present a new approach to estimating mixture models based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from Jaynes' maximum entropy principle and from standard maximum likelihood estimation. We demonstrate the LME principle by deriving new algorithms for mixture model estimation, and show how robust new variants of the EM algorithm can be developed. Our experiments show that estimation based on LME generally yields better results than maximum likelihood estimation, particularly when inferring latent variable models from small amounts of data.
Session Boundary Detection for Association Rule Learning Using n-Gram Language Models

We present a statistical method using n-gram language models to identify session boundaries in a large collection of Livelink log data. The identified sessions are then used for association rule learning. Unlike the traditional ad hoc timeout method, which uses fixed time thresholds for session identification, our method uses an information theoretic approach that provides a natural technique for performing dynamic session identification. The effectiveness of our approach is evaluated with respect to 4 different interestingness measures. We find that we obtain a significant improvement in each interestingness measure, ranging from a 26.6% to 39% improvement on average over the best results obtained with standard timeout methods.
Combining Naive Bayes and n-Gram Language Models for Text Classification

We augment the naive Bayes model with an n-gram language model to address two shortcomings of naive Bayes text classifiers. The chain augmented naive Bayes classifiers we propose have two advantages over standard naive Bayes classifiers. First, a chain augmented naive Bayes model relaxes some of the independence assumptions of naive Bayes--allowing a local Markov chain dependence in the observed variables--while still permitting efficient inference and learning. Second, smoothing techniques from statistical language modeling can be used to recover better estimates than the Laplace smoothing techniques usually used in naive Bayes classification. Our experimental results on three real world data sets show that we achieve substantial improvements over standard naive Bayes classification, while also achieving state of the art performance that competes with the best known methods in these cases.
Investigating the Maximum Likelihood Alternative to TD(lambda)

The wedging action fixture to which the invention relates has a wedging action means, a pair of pressing members, wedging action releasing means and other members for limiting the relative movement and transmitting forces between these parts. This fixture can be secured to any desired portion of an elongated supporting member by a wedging action performed by the wedging action means. The external force to be borne is applied to the wedging action means so as to obtain a wedging force which is transmitted to a pair of pressing members. The pressing members acts on both surface of the elongated supporting member so as to cramp the latter therebetween or, alternatively, on both opposing walls of a channeled supporting member to urge the walls away from each other, thereby to bear the external force at any desired position on the elongated supporting member. The fixture can easily be unfastened simply by operating the wedging action releasing means, so that it can be easily moved to any desired position on the elongated supporting member or detached from the latter.
Using self-supervised word segmentation in Chinese information retrieval

We propose a self-supervised word-segmentation technique for Chinese information retrieval. This method combines the advantages of traditional dictionary based approaches with character based approaches, while overcoming many of their shortcomings. Experiments on TREC data show comparable performance to both the dictionary based and the character based approaches. However, our method is language independent and unsupervised, which provides a promising avenue for constructing accurate multilingual information retrieval systems that are flexible and adaptive.
Regularized Greedy Importance Sampling

Greedy importance sampling is an unbiased estimation technique that reduces the variance of standard importance sampling by explicitly searching for modes in the estimation objective. Previous work has demonstrated the feasibility of implementing this method and proved that the technique is unbiased in both discrete and continuous domains. In this paper we present a reformulation of greedy importance sampling that eliminates the free parameters from the original estimator, and introduces a new regularization strategy that further reduces variance without compromising unbiasedness. The resulting estimator is shown to be effective for difficult estimation problems arising in Markov random field inference. In particular, improvements are achieved over standard MCMC estimators when the distribution has multiple peaked modes.
Piecewise linear value function approximation for factored MDPs

A number of proposals have been put forth in recent years for the solution of Markov decision processes (MDPs) whose state (and sometimes action) spaces are factored. One recent class of methods involves linear value function approximation, where the optimal value function is assumed to be a linear combination of some set of basis functions, with the aim of finding suitable weights. While sophisticated techniques have been developed for finding the best approximation within this constrained space, few methods have been proposed for choosing a suitable basis set, or modifying it if solution quality is found wanting. We propose a general framework, and specific proposals, that address both of these questions. In particular, we examine <i>weakly coupled MDPs</i> where a number of subtasks can be viewed independently modulo resource constraints. We then describe methods for constructing a piecewise linear combination of the subtask value functions, using greedy decision tree techniques. We argue that this architecture is suitable for many types of MDPs whose combinatorics are determined largely by the existence multiple conflicting objectives.
Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs

One of the central challenges in reinforcement learning is to balance the exploration/exploitation tradeoff while scaling up to large problems. Although model-based reinforcement learning has been less prominent than value-based methods in addressing these challenges, recent progress has generated renewed interest in pursuing modelbased approaches: Theoretical work on the exploration/exploitation tradeoff has yielded provably sound model-based algorithms such as E 3 and Rmax, while work on factored MDP representations has yielded model-based algorithms that can scale up to large problems. Recently the benefits of both achievements have been combined in the Factored E3 algorithm of Kearns and Koller. In this paper, we address a significant shortcoming of Factored E3: namely that it requires an oracle planner that cannot be feasibly implemented. We propose an alternative approach that uses a practical approximate planner, approximate linear programming, that maintains desirable properties. Further, we develop an exploration strategy that is targeted toward improving the performance of the linear programming algorithm, rather than an oracle planner. This leads to a simple exploration strategy that visits states relevant to tightening the LP solution, and achieves sample efficiency logarithmic in the size of the problem description. Our experimental results show that the targeted approach performs better than using approximate planning for implementing either Factored E3 or Factored Rmax.
Waterloo at NTCIR-3: Using Self-supervised Word Segmentation

In this paper, we describe the system we use in the NTCIR-3 CLIR (cross language IR) task. We participate the SLIR (single language IR) track. In our system, we use a self-supervised word-segmentation technique for Chinese information retrieval, which combines the advantages of traditional dictionary based approaches with character based approaches, while overcoming many of their shortcomings. This method is completely language independent and unsupervised, which provides a promising avenue for constructing accurate multi-lingual or cross-lingual information retrieval systems that are flexible and adaptive.
Greedy linear value-approximation for factored Markov decision processes

Significant recent work has focused on using linear representations to approximate value functions for factored Markov decision processes (MDPs). Current research has adopted linear programming as an effective means to calculate approximations for a <i>given</i> set of basis functions, tackling very large MDPs as a result. However, a number of issues remain unresolved: <i>How accurate are the approximations produced by linear programs? How hard is it to produce better approximations? </i> and <i>Where do the basis functions come from?</i> To address these questions, we first investigate the complexity of minimizing the Bellman error of a linear value function approximation--showing that this is an inherently hard problem. Nevertheless, we provide a branch and bound method for calculating Bellman error and performing approximate policy iteration for general factored MDPs. These methods are more accurate than linear programming, but more expensive. We then consider linear programming itself and investigate methods for automatically constructing sets of basis functions that allow this approach to produce good approximations. The techniques we develop are guaranteed to reduce <i>L</i><inf>1</inf> error, but can also empirically reduce Bellman error.
Investigating the Relationship between Word Segmentation Performance and Retrieval Performance in Chinese IR

It is commonly believed that word segmentation accuracy is monotonically related to retrieval performance in Chinese information retrieval. In this paper we show that, for Chinese, the relationship between segmentation and retrieval performance is in fact nonmonotonic; that is, at around 70% word segmentation accuracy an over-segmentation phenomenon begins to occur which leads to a reduction in information retrieval performance. We demonstrate this effect by presenting an empirical investigation of information retrieval on Chinese TREC data, using a wide variety of word segmentation algorithms with word segmentation accuracies ranging from 44% to 95%. It appears that the main reason for the drop in retrieval performance is that correct compounds and collocations are preserved by accurate segmenters, while they are broken up by less accurate (but reasonable) segmenters, to a surprising advantage. This suggests that words themselves might be too broad a notion to conveniently capture the general semantic meaning of Chinese text.
Direct value-approxiIllation for factored MDPs

We present a simple approach for computing reasonable policies for factored Markov decision processes (MDPs), when the optimal value function can be approximated by a compact linear form. Our method is based on solving a single linear program that approximates the best linear fit to the optimal value function. By applying an efficient constraint generation procedure we obtain an iterative solution method that tackles concise linear programs. This direct linear programming approach experimentally yields a significant reduction in computation time over approximate valueand policy-iteration methods (sometimes reducing several hours to a few seconds). However, the quality of the solutions produced by linear programming is weaker-usually about twice the approximation error for the same approximating class. Nevertheless, the speed advantage allows one to use larger approximation classes to achieve similar error in reasonable time.
Self-Supervised Chinese Word Segmentation

We propose a new unsupervised training method for acquiring probability models that accurately segment Chinese character sequences into words. By constructing a core lexicon to guide unsupervised word learning, self-supervised segmentation overcomes the local maxima problems that hamper standard EM training. Our procedure uses successive EM phases to learn a good probability model over character strings, and then prunes this model with a mutual information selection criterion to obtain a more accurate word lexicon. The segmentations produced by these models are more accurate than those produced by training with EM alone.
The Exponentiated Subgradient Algorithm for Heuristic Boolean Programming

Boolean linear programs (BLPs) are ubiquitous in AI. Satisfiability testing, planning with resource constraints, and winner determination in combinatorial auctions are all examples of this type of problem. Although increasingly well-informed by work in OR, current AI research has tended to focus on specialized algorithms for each type of BLP task and has only loosely patterned new algorithms on effective methods from other tasks. In this paper we introduce a single general-purpose local search procedure that can be simultaneously applied to the entire range of BLP problems, without modification. Although one might suspect that a general-purpose algorithm might not perform as well as specialized algorithms, we find that this is not the case here. Our experiments show that our generic algorithm simultaneously achieves performance comparable with the state of the art in satisfiability search and winner determination in combinatorial auctions-- two very different BLP problems. Our algorithm is simple, and combines an old idea from OR with recent ideas from AI.
Direct value-approximation for factored MDPs

We present a simple approach for computing reasonable policies for factored Markov decision processes (MDPs), when the optimal value function can be approximated by a compact linear form. Our method is based on solving a single linear program that approximates the best linear fit to the optimal value function. By applying an efficient constraint generation procedure we obtain an iterative solution method that tackles concise linear programs. This direct linear programming approach experimentally yields a significant reduction in computation time over approximate value- and policy-iteration methods (sometimes reducing several hours to a few seconds). However, the quality of the solutions produced by linear programming is weaker—usually about twice the approximation error for the same approximating class. Nevertheless, the speed advantage allows one to use larger approximation classes to achieve similar error in reasonable time.
A Simple Closed-Class/Open-Class Factorization for Improved Language Modeling

We describe a simple improvement to ngram language models where we estimate the distribution over closed-class (function) words separately from the conditional distribution of open-class words given function words. In English, function words account for about 30% of written language, and also form a natural skeleton for most sentences. By factoring a language model into a function word model and a conditional model over open-class words given function words, we largely avoid the problem of sparse training data in the first phase, and localize the need for sophisticated smoothing techniques primarily to the second conditional model. We test our factored approach on the Brown and Wall Street Journal corpora and observe a 3.5% to 25.2% improvement in perplexity over standard methods, depending on the particular smoothing method and test set used. Compared to other proposals for improving n-gram language models, our factorization has the advantage of inherent simplicity and efficiency, and improves generalization between data sets.
A Hierarchical EM Approach to Word Segmentation

We propose a simple two-level hierarchical probability model for unsupervised word segmentation. By treating words as strings composed of morphemes/phonemes which are themselves composed of character/phone strings, we use EM to first identify the important morphemes/phonemes in a corpus, and then use a second level of EM to identify words given a lower level morpheme/phoneme segmentation. To further improve performance of the basic method we employ a mutual information criterion to eliminate long word agglomerations and reduce the size of the inferred lexicon while moving EM out of poor local maxima. Experiments on the Brown corpus show that our method accurately recovers hidden word boundaries using less training data than current MDL based approaches, even though our method is only trained on raw unsupervised data.