The New York Public Library is participating in the Chronicling America initiative to develop an online searchable database of historically significant newspaper articles. Microfilm copies of the papers are scanned and high resolution OCR software is run on them. The text from the OCR provides a wealth of data and opinion for researchers and historians. However, the categorization of articles provided by the OCR engine is rudimentary and a large number of the articles are labeled ``editorial" without further categorization. To provide a more refined grouping of articles, unsupervised machine learning algorithms (such as K-Means) are being investigated. The K-Means algorithm requires tuning of parameters such as the number of clusters and mechanism of seeding to ensure that the search is not prone to being caught in a local minima. We designed a pilot study to observe whether humans are adept at finding sub-categories. The subjective labels provided by humans are used as a guide to compare performance of the automated clustering techniques. In addition, seeds provided by annotators are carefully incorporated into a semi-supervised K-Means algorithm (Seeded K-Means); empirical results indicate that this helps to improve performance and provides an intuitive sub-categorization of the articles labeled ``editorial" by the OCR engine.

This paper presents a novel multimodal approach to automatically detect learner’s uncertainty through the integration of multiple sensors. An acquisition protocol was established to record participants’ electrical brain activity and physiological signals while interacting with a problem solving system specifically designed for uncertainty elicitation. Data were collected from 38 subjects using 8 sensors and two video feeds. Results from machine learning classifiers support the feasibility of our approach. 81% of accuracy was reached using Support Vector Machine (SVM) algorithm.

We present in this paper a novel solution to the problem of paraphrase identification based on lexical dissimilarity kernels. Lexical kernels in conjunction with Support Vector Machines are preferred over other learning methods, e.g. decision trees, due to their ability to handle a high number of features. Dissimilarity-based kernels emphasize dissimilarities among text fragments and therefore are appropriate for text similarity tasks characterized by high lexical overlap. We conducted experiments with our kernels on the Microsoft Research (MSR) Paraphrase Corpus, a standardized data set used for assessing approaches to paraphrase identification. Our reported accuracy results are competitive and robust when compared to state-of-the-art single-model approaches. The results were obtained using 10-fold cross-validation over the entire corpus. We also report competitive results on the test portion of the MSR Paraphrase Corpus, which is the standard way to report results on this corpus.

The filter model of feature selection has been well studied. In previous studies, classification performance has traditionally been proposed as a way to evaluate filter solutions. In this study, a new method of comparing feature ranking techniques is presented providing a straightforward approach for quantifying individual filters’ robustness to class noise. Six commonly-used filters, plus one which is rarely used, are investigated regarding their ability to retain, in the presence of class noise, strong classification performance. Three classifiers and one classification performance metric are considered. The experimental results of this study show that Gain Ratio, one of the well known and widely used filters, is very sensitive to class noise. ReliefF offers the best results with both the NB and kNN learners while Signal-to-noise, though not as widely used in the literature as the others, outperforms all the filters with the SVM learner.

This paper describes preliminary research in the use of ontological knowledge for the task of automatically classifying classical Chinese poetry (CCCP) according to authorship. Based on a collection of poems written by Liu Yong (987–1053 AD) and Su Shi (1037– 1101 AD), which have been analyzed according to a taxonomy of ontological entities at the lexical level, the research looks into the issue of whether characteristic features can be automatically extracted as important stylistic differences between the two poets. This paper examines the efficiency of different ontological concepts as features in CCCP using Support Vector Machine (SVMs). The experiment shows that an integration of ontological knowledge and bags-of-words (BoW) produces a higher precision for CCCP than BoW only with an overall increase of 2.1% and 2.2% in terms of precision and F-score.

This paper investigates feature level fusion for enhancing fault detection from vibration signals in an ocean turbine. Changes in vibration signatures from such rotating machinery typically indicate the presence of a problem such as a shift in its orientation or mechanical impact from its environment. We applied feature level fusion to vibration data acquired from two accelerometers attached to a box fan, and then assessed the abilities of twelve well known machine learners to detect changes in state from the raw accelerometer data and from the fused data. Analysis of the performance of these classifiers showed an overall performance improvement in all twelve classifiers in detecting the state of the fan from the fused data versus from the data from the two individual sensor channels.

This paper discusses a novel hybrid approach for text categorization that combines a machine learning algorithm, which provides a base model trained with a labeled corpus, with a rule-based expert system, which is used to improve the results provided by the previous classifier, by filtering false positives and dealing with false negatives. The main advantage is that the system can be easily fine-tuned by adding specific rules for those noisy or conflicting categories that have not been successfully trained. We also describe an implementation based on k-Nearest Neighbor and a simple rule language to express lists of positive, negative and relevant (multiword) terms appearing in the input text. The system is evaluated in several scenarios, including the popular Reuters-21578 news corpus for comparison to other approaches, and categorization using IPTC metadata, EUROVOC thesaurus and others. Results show that this approach achieves a precision that is comparable to top ranked methods, with the added value that it does not require a demanding human expert workload to train.

Several recent heuristics for domain independent planning adopt some action cost partitioning scheme to derive admissible heuristic estimates. Given a state, two methods for obtaining an action cost partitioning have been proposed: optimal cost partitioning, which results in the best possible heuristic estimate for that state, but requires a substantial computational effort, and ad-hoc (uniform) cost partitioning, which is much faster, but is usually less informative. These two methods represent almost opposite points in the tradeoff between heuristic accuracy and heuristic computation time. One compromise that has been proposed between these two is using an optimal cost partitioning for the initial state to evaluate all states. In this paper, we propose a novel method for deriving a fast, informative cost-partitioning scheme, that is based on computing optimal action cost partitionings for a small set of states, and using these to derive heuristic estimates for all states. Our method provides greater control over the accuracy/computation-time tradeoff, which, as our empirical evaluation shows, can result in better performance.

Natural language processing and statistical methods were used to identify linguistic features associated with the quality of student-generated paragraphs. Linguistic features were assessed using Coh-Metrix. The resulting computational models demonstrated small to medium effect sizes for predicting paragraph quality: introduction quality r2 = .25, body quality r2 = .10, and conclusion quality r2 = .11. Although the variance explained was somewhat low, the linguistic features identified were consistent with the rhetorical goals of paragraph types. Avenues for bolstering this approach by considering individual writing styles and techniques are considered.

Past research has shown that dictionaries and glosses can be beneficial in computer assisted language learning, particularly in vocabulary learning. We propose that L2 vocabulary learners can benefit from the use of a dictionary whose definitions are sensitive to the provided reading context, and that advances in the natural language processing task of word sense disambiguation can be used to automatically order the definitions of such a dictionary. An in-vivo study was conducted with ESL students to investigate the effect that the order of definitions has on vocabulary learning using REAP, a computer based vocabulary tutor. Our results showed that students benefited from having the algorithmically determined best definitions listed at the top of the definition list. Furthermore, our results suggest that word sense disambiguation may currently be good enough for use in intelligent language tutoring environments.