8656103
Improving novelty detection for general topics using sentence level information patterns
The detection of new information in a document stream is an important component of many potential applications. In this work, a new novelty detection approach based on the identification of sentence level information patterns is proposed. First, the information-pattern concept for novelty detection is presented with the emphasis on new information patterns for general topics (queries) that cannot be simply turned into specific questions whose answers are specific named entities (NEs). Then we elaborate a thorough analysis of sentence level information patterns on data from the TREC novelty tracks, including sentence lengths, named entities, sentence level opinion patterns. This analysis provides guidelines in applying those patterns in novelty detection particularly for the general topics. Finally, a unified pattern-based approach is presented to novelty detection for both general and specific topics. The new method for dealing with general topics will be the focus. Experimental results show that the proposed approach significantly improves the performance of novelty detection for general topics as well as the overall performance for all topics from the 2002-2004 TREC novelty tracks.
16221853
UMass at TREC 2004: Novelty and HARD
For the TREC 2004 Novelty track, UMass participated in all four tasks. Although finding relevant sentences was harder this year than last, we continue to show marked improvements over the baseline of calling all sentences relevant, with a variant of tfidf being the most successful approach. We achieve 5‐9% improvements over the baseline in locating novel sentences, primarily by looking at the similarity of a sentence to earlier sentences and focusing on named entities. For the High Accuracy Retrieval from Documents (HARD) track, we investigated the use of clarification forms, fixed- and variable-length passage retrieval, and the use of metadata. Clarification form results indicate that passage level feedback can provide improvements comparable to user supplied related-text for document evaluation and outperforms related-text for passage evaluation. Document retrieval methods without a query expansion component show the most gains from related-text. We also found that displaying the top passages for feedback outperformed displaying centroid passages. Named entity feedback resulted in mixed performance. Our primary findings for passage retrieval are that document retrieval methods performed better than passage retrieval methods on the passage evaluation metric of binary preference at 12,000 characters, and that clarification forms improved passage retrieval for every retrieval method explored. We found no benefit to using variable-length passages over fixed-length passages for this corpus. Our use of geography and genre metadata resulted in no significant changes in retrieval performance.
2304647
Evaluating Question-Answering Techniques in Chinese
An important first step in developing a cross-lingual question answering system is to understand whether techniques developed with English text will also work with other languages, such as Chinese. The Marsha Chinese question answering system described in this paper uses techniques similar to those used in the English systems developed for TREC. Marsha consists of three main components: the query processing module, the Hanquery search engine, and the answer extraction module. It also contains some specific techniques dealing with Chinese language characteristics, such as word segmentation and ordinals processing. Evaluation of the system is done using a method based on the TREC question-answering track. The results of the evaluation show that the performance of Marsha is comparable to some English question answering systems in TREC 8 track. An English language version of Marsha further indicates that the heuristics used are applicable to the English question answering task.
8799846
Syntactic features in question answering
Syntactic information potentially plays a much more important role in question answering than it does in information retrieval. Although many people have used syntactic evidence in Question Answering, there haven't been many detailed experiments reported in the literature. The aim of the experiment described in this paper is to study the impact of a particular approach for using syntactic information on question answering effectiveness. Our results indicate that a combination of syntactic information with heuristics for ranking potential answers can perform better than the ranking heuristics on their own.
16393547
A new robust relevance model in the language model framework
In this paper, a new robust relevance model is proposed that can be applied to both pseudo and true relevance feedback in the language-modeling framework for document retrieval. There are at least three main differences between our new relevance model and other relevance models. The proposed model brings back the original query into the relevance model by treating it as a short, special document, in addition to a number of top-ranked documents returned from the first round retrieval for pseudo feedback, or a number of relevant documents for true relevance feedback. Second, instead of using a uniform prior as in the original relevance model proposed by Lavrenko and Croft, documents are assigned with different priors according to their lengths (in terms) and ranks in the first round retrieval. Third, the probability of a term in the relevance model is further adjusted by its probability in a background language model. In both pseudo and true relevance cases, we have compared the performance of our model to that of the two baselines: the original relevance model and a linear combination model. Our experimental results show that the proposed new model outperforms both of the two baselines in terms of mean average precision.
13418116
An information-pattern-based approach to novelty detection
In this paper, a new novelty detection approach based on the identification of sentence level information patterns is proposed. First, ''novelty'' is redefined based on the proposed information patterns, and several different types of information patterns are given corresponding to different types of users' information needs. Second, a thorough analysis of sentence level information patterns is elaborated using data from the TREC novelty tracks, including sentence lengths, named entities (NEs), and sentence level opinion patterns. Finally, a unified information-pattern-based approach to novelty detection (ip-BAND) is presented for both specific NE topics and more general topics. Experiments on novelty detection on data from the TREC 2002, 2003 and 2004 novelty tracks show that the proposed approach significantly improves the performance of novelty detection in terms of precision at top ranks. Future research directions are suggested.
35636017
A Novel Pixel-Level and Feature-Level Combined Multisensor Image Fusion Scheme
This paper proposes a novel image fusion scheme, which combines the merits of pixel-level and feature-level fusion algorithms. It avoids some of the well-known problems in pixel-level fusion such as blurring effects and high sensitivity to noise and misregistration. The algorithm first segments images into several regions, then extract features from each segmented region to get the fused image. Two typical image segmentation methods, region growing and edge detection based are both presented in this paper. Experimental results have demonstrated that the proposed method has extensive application scope and it outperforms the multiscale decompositions (MSD) based fusion approaches, both in visual effect and objective evaluation criteria.
7335001
Pivotbrowser: a tag-space image searching prototype
We propose a novel iterative searching and refining prototype for tagged images. This prototype, named PivotBrowser, captures semantically similar tag sets in a structure called pivot. By constructing a pivot for a textual query, PivotBrowser first selects candidate images possibly relevant to the query. The tags contained in these candidate images are then selected in terms of their tag relevances to the pivot. The shortlisted tags are clustered and one of the tag clusters is used to select the results from the candidate images. Ranking of the images in each partition is based on their relevance to the tag cluster. With the guidance of the tag clusters presented, a user is able to perform searching and iterative query refinement.
15133148
Modeling Image Data for Effective Indexing and Retrieval in Large General Image Databases
In this paper, we propose an image semantic model based on the knowledge and criteria in the field of linguistics and taxonomy. Our work bridges the "semantic gap" by seamlessly exploiting the synergy of both visual feature processing and semantic relevance computation in a new way, and provides improved query efficiency and effectiveness for large general image databases. Our main contributions are as follows: we design novel data structures, namely, a lexical hierarchy, an image-semantic hierarchy, and a number of atomic semantic domains, to capture the semantics and the features of the database, and to provide the indexing scheme. We present a novel image query algorithm based on the proposed structures. In addition, we propose a novel term expansion mechanism to improve the lexical processing. Our extensive experiments indicate that our proposed techniques are effective in achieving high runtime performance with improved retrieval accuracy. The experiments also show that the proposed method has good scalability.
26669443
Bottom-up discovery of frequent rooted unordered subtrees
In the past decade, XML has emerged as the standard language for information exchanging over the Internet. Due to its tree-structure paradigm, XML is superior for its capability of storing, querying, and manipulating complex data. Therefore, discovering frequent tree patterns over tree-structured data has become an interesting topic for XML data management. In this paper, we propose a tree mining algorithm, named BUXMiner, for finding a special class of frequent trees, called rooted unordered trees, from a tree-structured database. BUXMiner employs an efficient bottom-up approach to enumerate all candidate trees over a compact global tree guide and computes the frequent trees based on the tree guide. In addition to BUXMiner, we also propose a mining approach called BUMXMiner to discover the maximal frequent rooted unordered trees. We compare BUXMiner with previous tree-structure mining algorithms, namely XQPMinerTID and FastXMiner, which were also proposed to discover rooted unordered trees. The experimental results show that our algorithm outperforms XQPMinerTID and FastXMiner in terms of efficiency. The performance results from real-world applications also indicate the usefulness of our proposed tree mining algorithms in a variety of web applications, such as analysis of web page access patterns and mining frequent XML query patterns for caching.
1979432
Using Fault Injection and Modeling to Evaluate the Performability of Cluster-Based Services
We propose a two-phase methodology for quantifying the performability (performance and availability) of cluster-based Internet services. In the first phase, evaluators use a fault-injection infrastructure to measure the impact of faults on the server's performance. In the second phase, evaluators use an analytical model to combine an expected fault load with measurements from the first phase to assess the server's performability. Using this model, evaluators can study the server's sensitivity to different design decisions, fault rates, and environmental factors. To demonstrate our methodology, we study the performability of 4 versions of the PRESS Web server against 5 classes of faults, quantifying the effects of different design decisions on performance and availability. Finally, to further show the utility of our model, we also quantify the impact of two hypothetical changes, reduced human operator response time and the use of RAIDs.