3098509
The nature of novelty detection
Sentence level novelty detection aims at spotting sentences with novel information from an ordered sentence list. In the task, sentences appearing later in the list with no new meanings are eliminated. For the task of novelty detection, the contributions of this paper are three-fold. First, conceptually, this paper reveals the computational nature of the task currently overlooked by the Novelty community—Novelty as a combination of partial overlap (PO) and complete overlap (CO) relations between sentences. We define partial overlap between two sentences as a sharing of common facts, while complete overlap is when one sentence covers all of the meanings of the other sentence. Second, technically, a novel approach, the selected pool method is provided which follows naturally from the PO-CO computational structure. We provide formal error analysis for selected pool and methods based on this PO-CO framework. We address the question how accurate must the PO judgments be to outperform the baseline pool method. Third, experimentally, results were presented for all the three novelty datasets currently available. Results show that the selected pool is significantly better or no worse than the current methods, an indication that the term overlap criterion for the PO judgments could be adequately accurate.
31340161
The Empirical Impact of the Nature of Novelty Detection
Novelty detection systems aim at reducing redundant documents or sentences from a list of documents chronologically ordered. In the task, sentences appearing later in the list with no new meanings are eliminated. In an accompanying paper, the nature of novelty detection was revealed – Novelty as a combination of the PO (partial overlap) and CO (complete overlap) relations, which can be treated as two classification tasks; theoretical impacts were given. This paper provides what the nature of the task mean empirically. One new method – selected pool – implementing the nature of the task gained improvements on TREC Novelty datasets. New evaluation criteria are given, which are natural from the viewpoint of the nature of novelty detection.
7099358
THU TREC 2002: Novelty Track Experiments
This is the first time that Tsinghua University took part in TREC. In this year's novelty track, our basic idea is to find the key factor that help people find relevant and new information on a set of documents with noise. We paid attention to three points: 1. how to get full information from a short sentence; 2. how to complement hidden well-known knowledge to the sentences; 3. how to make the determination of duplication. Accordingly, expansion-based technologies are the key points. Studies of expansion technologies have been performed on three levels: efficient query expansion based on thesaurus and statistics, replacement-based document expansion, and term-expansion-related duplication elimination strategy based on overlapping measurement. Besides, two issues have been studied: finding key information in topics, and dynamic result selection. A new IR system has been developed for the task. In the system, four weighting strategies have been implemented: ltn.lnu, BM2500, FUB1, FUB2. It provides both similarity and overlapping measurements, based on term expansion. Comparisons can be made on sentence-to-sentence or sentence-to-pool level.
35924921
Approximating the Minimum k-way Cut in a Graph via Minimum 3-way Cuts
For an edge weighted undirected graph G and an integer k > 2, a k-way cut is a set of edges whose removal leaves G with at least k components. We propose a simple approximation algorithm to the minimum k-way cut problem. It computes a nearly optimal k-way cut by using a set of minimum 3-way cuts. We show that the performance ratio of our algorithm is 2 − 3/k for an odd k and 2 − (3k − 4)/(k2 − k) for an even k. The running time is O(kmn3 log(n2/m)) where n and m are the numbers of vertices and edges respectively.
41378670
A Unified Framework for Approximating Multiway Partition Problems
We present a simple and unified framework for developing and analyzing approximation algorithms for some multiway partition problems (with or without terminals), including the k-way cut (or kcut), multiterminal cut (or multiway cut), hypergraph partition and target split.
8864219
On generalized greedy splitting algorithms for multiway partition problems
Given a system (V,T,f,k) where V is a finite set, T ⊆ V, f : 2V → R is a submodular function and k ≥ 2 is an integer, the multiway partition problem (MPP) asks to find a k-partition P = {V1, V2,...,Vk} of V that satisfies Vi ∩ T ≠ 0 for all i and minimizes f(V1) + f(V2)+...+f(Vk). This formulation captures a generalization of many NP-hard partition problems in graphs or hypergraphs. Previously, the authors have shown a simple framework for approximating MPPs by greedily increasing the size of partition by one. In this paper, we show that, if T = V, improved guarantees are available by greedily increasing the size by two. We also show polynomial time implementations for several problem classes.
32435431
Regularized Semi-supervised Classification on Manifold
Semi-supervised learning gets estimated marginal distribution PX with a large number of unlabeled examples and then constrains the conditional probability p(y | x) with a few labeled examples. In this paper, we focus on a regularization approach for semi-supervised classification. The label information graph is first defined to keep the pairwise label relationship and can be incorporated with neighborhood graph which reflects the intrinsic geometry structure of PX. Then we propose a novel regularized semi-supervised classification algorithm, in which the regularization term is based on the modified Graph Laplacian. By redefining the Graph Laplacian, we can adjust and optimize the decision boundary using the labeled examples. The new algorithm combines the benefits of both unsupervised and supervised learning and can use unlabeled and labeled examples effectively. Encouraging experimental results are presented on both synthetic and real world datasets.
31753599
Adaptive Closed-Loop Power Control Using an MMSE Receiver in DS-CDMA Systems
A closed-loop power control algorithm, which includes both power control and power allocation functions for DS-CDMA systems, is proposed. The target power level is updated by an iterative algorithm for a minimum mean square error (MMSE) linear filter receiver. The received power is compared with the target to generate the current power control command (PCC). The transmitted power is adjusted by applying a variable stepsize generator that utilizes the PCC history and the estimated mobile speed. Simulation results are presented to show the improvement of the proposed adaptive power control over the conventional fixed stepsize power control.