33920606
Hot-Spot Passage Retrieval in Question Answering
Question Answering has been the recent focus of information retrieval research; many systems just incorporate a search engine as a black box and most effort has been devoted to the question analyzer and the answer identifier. In the context of QA, however, passage provides an ideal medium between the document collection and an exact answer. And passage retrieval is a finer-grain approach than the traditional document retrieval both for the answer identifier and a human reader. In this paper, distinctions are first made between document retrieval and passage retrieval. And the Hot-Spot Passage Retrieval algorithm, which takes into account the measures of blurred BM25, coverage and height, is examined in detail. For evaluation, an isolated test is conducted and the algorithm gains 18.3% better answer redundancy and 4.8% better coverage rate than Okapi's original passage retrieval algorithm.
3032576
Generative models for name disambiguation
Name ambiguity is a special case of identity uncertainty where one person can be referenced by multiple name variations in different situations or evenshare the same name with other people. In this paper, we present an efficient framework by using two novel topic-based models, extended from Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). Our models explicitly introduce a new variable for persons and learn the distribution of topics with regard to persons and words. Experiments indicate that our approach consistently outperforms other unsupervised methods including spectral and DBSCAN clustering. Scalability is addressed by disambiguating authors in over 750,000 papers from the entire CiteSeer dataset.
474823
Boosting the Feature Space: Text Classification for Unstructured Data on the Web
The issue of seeking efficient and effective methods for classifying unstructured text in large document corpora has received much attention in recent years. Traditional document representation like bag-of-words encodes documents as feature vectors, which usually leads to sparse feature spaces with large dimensionality, thus making it hard to achieve high classification accuracies. This paper addresses the problem of classifying unstructured documents on the Web. A classification approach is proposed that utilizes traditional feature reduction techniques along with a collaborative filtering method for augmenting document feature spaces. The method produces feature spaces with an order of magnitude less features compared with a baseline bag-of-words feature selection method. Experiments on both real-world data and benchmark corpus indicate that our approach improves classification accuracy over the traditional methods for both support vector machines and AdaBoost classifiers.
207081005
Certifying LexBFS Recognition Algorithms for Proper Interval Graphs and Proper Interval Bigraphs
Recently, D. Corneil found a simple 3-sweep lexicographic breadth first search (LexBFS) algorithm for the recognition of proper interval graphs. We point out how to modify Corneil's algorithm to make it a certifying algorithm, and then describe a similar certifying 3-sweep LexBFS algorithm for the recognition of proper interval bigraphs. It follows from an earlier paper that the class of proper interval bigraphs is equal to the better known class of bipartite permutation graphs, and so we have a certifying algorithm for that class as well. All our algorithms run in time O(m+n), including the certification phase. The certificates of representability (the intervals) can be authenticated in time O(m+n). The certificates of nonrepresentability (the forbidden subgraphs) can be authenticated in time O(n).
10932878
Thread Transparency in Information Flow Middleware
Applications that process continuous information flows are challenging to write because the application programmer must deal with flow-specific concurrency and timing requirements, necessitating the explicit management of threads, synchronization, scheduling and timing. We believe that middleware can ease this burden, but middleware that supports control-flow centric interaction models such as remote method invocation does not match the structure of these applications. Indeed, it abstracts away from the very things that the information-flow centric programmer must control
14698607
Statistical Multiplexing Over DSL Networks
Most previous work in statistical multiplexing only considered the case where the link transmission rates are fixed. In this paper, we consider statistical multiplexing in networks with adaptive transmission rates, with focus on DSL broadband access networks. This requires a jointly optimized allocation of buffer space and transmission bandwidth to traffic flows, which takes the flow traffic characteristics, the user QoS requirements, and the user interactions at the physical layer into consideration. Using the effective bandwidth concept, we propose a class of alternate maximization (AM) algorithms (AM-D and AM-M), which solve the statistical multiplexing problem for both delay insensitive data traffic and delay sensitive multimedia traffic. With low complexity as a design goal, the AM algorithms incorporate our recently proposed autonomous spectrum balancing (ASB) algorithm, which was originally designed for DSL physical layer spectrum management. Our numerical results show that the AM algorithms combines the gain due to statistical multiplexing and that due to spectrum management.
1676234
A complete distance field representation
Distance fields are an important volume representation. A high quality distance field facilitates accurate surface characterization and gradient estimation. However, due to Nyquist's law, no existing volumetric methods based on the linear sampling theory can fully capture surface details, such as comers and edges, in 3D space. We propose a novel complete distance field representation (CDFR) that does not rely on Nyquist's sampling theory. To accomplish this, we construct a volume where each voxel has a complete description of all portions of surface that affect the local distance field. For any desired distance, we are able to extract a surface contour in true Euclidean distance, at any level of accuracy, from the same CDFR representation. Such point-based iso-distance contours have faithful per-point gradients and can be interactively visualized using splatting, providing per-point shaded image quality. We also demonstrate applying CDFR to a cutting edge design for manufacturing application involving high-complexity parts at unprecedented accuracy using only commonly available computational resources.
1720329
Toward Inductive Logic Programming for Collaborative Problem Solving
In this paper, we tackle learning in distributed systems and the fact that learning does not necessarily involve the participation of agents directly in the inductive process itself. Instead, many systems frequently employ multiple instances of induction separately. The paper's main contribution is a new approach that tightly integrates processes of induction between distributed agents, based on inductive logic programming techniques, for a wider class of problem solving tasks. The approach combines inverse entailment with an epistemic approach to reasoning about knowledge, facilitating a systematic approach to the sharing of knowledge and invention of predicates only when required. We illustrate the approach for learning declarative program fragments and for a well-known path planning problem and compare results empirically to (multiple instances of) single agent-based induction over varying distributions of data. Given a chosen path planning algorithm, our algorithm enables agents to combine their local knowledge in an effective way to avoid central control while significantly reducing communication costs.