THUIR at TREC 2004: QA
In this paper, we describe ideas and related experiments of Tsinghua University IR group in TREC 2004 QA track. In this track, our system consists three components: Question analysis, Information retrieval, and Answer extraction. Question analysis component extracts Query Term and answer type. Information retrieval component retrieves candidate documents from index set based on paragraph level and re-ranks them to find more relevant documents. And then Answer extraction component matches empirical phrases according to answer type to extract final answer.

title: THUIR at TREC 2003: HARD Experiments
abstract: In this paper, we describe ideas and related experiments of Tsinghua University IR group in TREC-12 HARD Track. In this track, we focus on an automatic delivering mechanism, which combine the existing IR methods and can provide a quick retrieval solution for the practical environment. The final official evaluation show the old ways perform not well, but we think the experiment data will be helpful in evaluating the new ideas developed by other teams. 1. Brief Introduction As a new evaluation track, HARD is designed to find an effective way to locate the search focus precisely from the data coming from the user, including his/her additional information (such as the he/his background) and an interactive input, so as to provide better retrieval result to the original query request. In this track, the key issue is to find the real search focus. There can be two ways to finish it: manually and automatic way. Though the former usually can provide a satisfied performance, we think the automatic way is more useful in practical use and thus try to devote us in this way. In following sections, we introduce what we did in our research work and give the final evaluation result. Some further research work done after final TREC submit is also listed. 2. Construct Baseline Run We get our baseline run (only with document) using the initial query by a BM25 TF*IDF scoring schema. It is a popular method that is fast and practical. The special treatment is only used for initial query: For each topic, the query is constructed simply by the task description (The detail restriction for none-relevant document are ignored). For the search items, different weights are set according to their location(such as description field) and importance in the task description. Also, there is no positive training documents are used to refine the query, because usually the training resource is unlikely to be provided for various immediate search requirements in Web IR.

title: Spatial Fuzzy Clustering Using Varying Coefficients
abstract: To consider spatial information in spatial clustering, the Neighborhood Expectation-Maximization (NEM) algorithm incorporates a spatial penalty term in the objective function. Such an addition leads to multiple iterations in the E-step. Besides, the clustering result depends mainly on the choice of the spatial coefficient, which is used to weigh the penalty term but is hard to determine a priori. Furthermore, it may not be appropriate to assign a fixed coefficient to every site, regardless of whether it is in the class interior or on the class border. In estimating class posterior probabilities, sites in the class interior should receive stronger influence from their neighbors than those on the border. To that end, this paper presents a variant of NEM using varying coefficients, which are determined by the correlation of explanatory attributes inside the neighborhood. Our experimental results on real data sets show that it only needs one iteration in the E-step and consequently converges faster than NEM. The final clustering quality is also better than NEM.

title: Optimized Research of Resource Constrained Project Scheduling Problem Based on Genetic Algorithms
abstract: This paper mainly discusses the genetic algorithms in optimization of network planning project. It focuses on the study of multi-task scheduling (Resource-constrained project scheduling problem, RCPSP). For the problem of the resource-constrained task scheduling problem, the paper proposes a method to improve the genetic algorithms optimization of multi-task scheduling problem. From the aspects of the establishment of the optimized algorithms models, the algorithms design, the accomplishment of algorithms as well as the analysis of the result, we conducts this study in detail, and makes a comparison analysis with the other 11 heuristic methods. In this way, we testify our method and get the good results.