16041292
Expert network: effective and efficient learning from human decisions in text categorization and retrieval
Expert Network (ExpNet) is our new approach to automatic categorization and retrieval of natural language texts. We use a training set of texts with expert-assigned categories to construct a network which approximately reflects the conditional probabilities of categories given a text. The input nodes of the network are words in the training texts, the nodes on the intermediate level are the training texts, and the output nodes are categories. The links between nodes are computed based on statistics of the word distribution and the category distribution over the training set. ExpNet is used for relevance ranking of candidate categories of an arbitrary text in the case of text categorization, and for relevance ranking of documents via categories in the case of text retrieval. We have evaluated ExpNet in categorization and retrieval on a document collection of the MEDLINE database, and observed a performance in recall and precision comparable to the Linear Least Squares Fit (LLSF) mapping method, and significantly better than other methods tested. Computationally, ExpNet has an O(N 1og N) time complexity which is much more efficient than the cubic complexity of the LLSF method. The simplicity of the model, the high recall-precision rates, and the efficient computation together make ExpNet preferable as a practical solution for real-world applications.
14181364
Probabilistic score estimation with piecewise logistic regression
Well-calibrated probabilities are necessary in many applications like probabilistic frameworks or cost-sensitive tasks. Based on previous success of asymmetric Laplace method in calibrating text classifiers' scores, we propose to use piecewise logistic regression, which is a simple extension of standard logistic regression, as an alternative method in the discriminative family. We show that both methods have the flexibility to be piecewise linear functions in log-odds, but they are based on quite different assumptions. We evaluated asymmetric Laplace method, piecewise logistic regression and standard logistic regression over standard text categorization collections (Reuters-21578 and TRECAP) with three classifiers (SVM, Naive Bayes and Logistic Regression Classifier), and observed that piecewise logistic regression performs significantly better than the other two methods in the log-loss metric.
10555290
From Lasso regression to Feature vector machine
Lasso regression tends to assign zero weights to most irrelevant or redundant features, and hence is a promising technique for feature selection. Its limitation, however, is that it only offers solutions to linear models. Kernel machines with feature scaling techniques have been studied for feature selection with non-linear models. However, such approaches require to solve hard non-convex optimization problems. This paper proposes a new approach named the Feature Vector Machine (FVM). It reformulates the standard Lasso regression into a form isomorphic to SVM, and this form can be easily extended for feature selection with non-linear models by introducing kernels defined on feature vectors. FVM generates sparse solutions in the nonlinear feature space and it is much more tractable compared to feature scaling kernel machines. Our experiments with FVM on simulated data show encouraging results in identifying the small number of dominating features that are non-linearly correlated to the response, a task the standard Lasso fails to complete.
7098682
High-performing feature selection for text classification
This paper reports a controlled study on a large number of filter feature selection methods for text classification. Over 100 variants of five major feature selection criteria were examined using four well-known classification algorithms: a Naive Bayesian (NB) approach, a Rocchio-style classifier, a k-nearest neighbor (kNN) method and a Support Vector Machine (SVM) system. Two benchmark collections were chosen as the testbeds: Reuters-21578 and small portion of Reuters Corpus Version 1 (RCV1), making the new results comparable to published results. We found that feature selection methods based on chi2 statistics consistently outperformed those based on other criteria (including information gain) for all four classifiers and both data collections, and that a further increase in performance was obtained by combining uncorrelated and high-performing feature selection methods.The results we obtained using only 3% of the available features are among the best reported, including results obtained with the full feature set.
8555900
Unsupervised Learning of Arabic Stemming Using a Parallel Corpus
This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer. The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10 K sentences) parallel corpus as its sole training resources. No parallel text is needed after the training phase. Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre. Examples and results will be given for Arabic, but the approach is applicable to any language that needs affix removal. Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component. Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text, and 96% of the performance of the proprietary stemmer above.
3009196
Translingual Information Retrieval: Learning from Bilingual Corpora
Abstract Translingual information retrieval (TLIR) consists of providing a query in one language and searching document collections in one or more different languages. This paper introduces new TLIR methods and reports on comparative TLIR experiments with these new methods and with previously reported ones in a realistic setting. Methods fall into two categories: query translation and statistical-IR approaches establishing translingual associations. The results show that using bilingual corpora for automated extraction of term equivalences in context outperforms dictionarybased methods. Translingual versions of the Generalized Vector Space Model (GVSM) and Latent Semantic Indexing (LSI) perform well, as does translingual pseudo-relevance feedback (PRF) and Example-Based Term-in-context translation (EBT). All showed relatively small performance loss between monolingual and translingual versions, ranging between 87–101% of monolingual IR performance. Query translation based on a general machine-readable bilingual dictionary—heretofore the most popular method—did not match the performance of other, more sophisticated methods. Also, the previous very high LSI results in the literature based on “mate-finding” were superseded by more realistic relevance-based evaluations; LSI performance proved comparable to that of other statistical corpus-based methods.
10222228
The third Ramsey numbers for graphs with at most four edges
Abstract With the help of a computer, the Ramsey numbers r 3 ( H ) are obtained for each of the 19 graphs H with at most four edges and no isolated vertices.
6730244
Nemos: a framework for axiomatic and executable specifications of memory consistency models
Summary form only given. Conforming to the underlying memory consistency rules is a fundamental requirement for implementing shared memory systems and developing multiprocessor programs. In order to promote understanding and enable automated verification, it is highly desirable that a memory model specification be both declarative and executable. We present a specification framework called Nemos (Nonoperational yet Executable Memory Ordering Specifications), which supports precise specification and automatic execution in the same framework. We employ a uniform notation based on predicate logic to define shared memory semantics in an axiomatic as well as compositional style. We also apply constraint logic programming and SAT solving to make the axiomatic specifications executable for memory model analysis. To illustrate our approach, we formalize a collection of classical memory models, including sequential consistency, coherence, PRAM, causal consistency, and processor consistency.
3895085
Network localization in partially localizable networks
Knowing the positions of the nodes in a network is essential to many next generation pervasive and sensor network functionalities. Although many network localization systems have recently been proposed and evaluated, there has been no systematic study of partially localizable networks, i.e., networks in which there exist nodes whose positions cannot be uniquely determined. There is no existing study which correctly identifies precisely which nodes in a network are uniquely localizable and which are not. This absence of a sufficient uniqueness condition permits the computation of erroneous positions that may in turn lead applications to produce flawed results. In this paper, in addition to demonstrating the relevance of networks that may not be fully localizable, we design the first framework for two dimensional network localization with an efficient component to correctly determine which nodes are localizable and which are not. Implementing this system, we conduct comprehensive evaluations of network localizability, providing guidelines for both network design and deployment. Furthermore, we study an integration of traditional geographic routing with geographic routing over virtual coordinates in the partially localizable network setting. We show that this novel cross-layer integration yields good performance, and argue that such optimizations will be likely be necessary to ensure acceptable application performance in partially localizable networks.
7236224
Classification of and experimentation on tool interfacing in software development environments
The effectiveness of tool integration in software development environments is ultimately measured by its ability in improving the productivity of the user. Traditionally, issues related to tool integration are often classified according to aspects of such mechanisms as user interface, control, and data integration which are purely from the viewpoint of the environment developer. This mechanism-based classification has worked well in analyzing the relevant issues of tool integration. However, its usefulness as a guide to achieving effective tool integration is limited. The authors introduce an alternative classification scheme aimed at gradual improvement to the effectiveness of tool integration. As such, this classification scheme provides a guide to tool integration along the line of improving the user's productivity. In terms of this classification, they compare and experiment in depth a variety of integration techniques using a uniform tool interfacing architecture.
16965968
Performance of Full Text Search in Structured and Unstructured Peer-to-Peer Systems
While structured P2P systems (such as DHTs) are often regarded as an improvement over unstructured P2P systems (such as super-peer networks) in terms of routing efficiency, it is not clear which architecture is better for full text search. This paper provides a quantitative comparison of full text keyword search in structured and unstructured P2P systems. We examine three techniques (and optimizations to those techniques) proposed in the literature: using a DHT along with inverted lists and Bloom filters; using a super-peer network; and using a random walk over an unstructured network. We use real Web documents and user queries to measure the cost for both document publishing and query processing, in terms of bandwidth and response time. Our results show that all three techniques use roughly the same bandwidth to process queries (with the super-peer technique having a slight edge). The structured network provides the best response time (30 percent better than a super-peer network), but has a high cost of document publishing, using six times as much bandwidth as the super-peer system. The random walk technique requires no publishing, but has a very long response time unless multiple random walks operate in parallel.