915422
Experiments in Spoken Document Retrieval at CMU
Experience de recherche d'information sur un corpus representant 70 heures d'informations radiodiffusees (CMU= Carnegie Mellon University)
8511358
InterActive Feature Selection
We study the effects of feature selection and human feedback on features in active learning settings. Our experiments on a variety of text categorization tasks indicate that there is significant potential in improving classifier performance by feature reweighting, beyond that achieved via selective sampling alone (standard active learning) if we have access to an oracle that can point to the important (most predictive) features. Consistent with previous findings, we find that feature selection based on the labeled training set has little effect. But our experiments on human subjects indicate that human feedback on feature relevance can identify a sufficient proportion (65%) of the most relevant features. Furthermore, these experiments show that feature labeling takes much less (about 1/5th) time than document labeling. We propose an algorithm that interleaves labeling features and documents which significantly accelerates active learning.
13080115
Building Minority Language Corpora by Learning to Generate Web Search Queries
The Web is a source of valuable information, but the process of collecting, organizing, and effectively utilizing the resources it contains is difficult. We describe CorpusBuilder, an approach for automatically generating Web search queries for collecting documents matching a minority concept. The concept used for this paper is that of text documents belonging to a minority natural language on the Web. Individual documents are automatically labeled as relevant or nonrelevant using a language filter, and the feedback is used to learn what query lengths and inclusion/exclusion term-selection methods are helpful for finding previously unseen documents in the target language. Our system learns to select good query terms using a variety of term scoring methods. Using odds ratio scores calculated over the documents acquired was one of the most consistently accurate query-generation methods. To reduce the number of estimated parameters, we parameterize the query length using a Gamma distribution and present empirical results with learning methods that vary the time horizon used when learning from the results of past queries. We find that our system performs well whether we initialize it with a whole document or with a handful of words elicited from a user. Experiments applying the same approach to multiple languages are also presented showing that our approach generalizes well across several languages regardless of the initial conditions.
17471934
Designing a trace format for heap allocation events
Dynamic storage allocation continues to play an important role in the performance and correctness of systems ranging from user productivity software to high-performance servers. While algorithms for dynamic storage allocation have been studied for decades, much of the literature is based on measuring the performance of benchmark programs unrepresentative of many important allocation-intensive workloads. Furthermore, to date no standard has emerged or been proposed for publishing and exchanging representative allocation workloads. In this paper, we describe a preliminary design of a trace format for such workloads and investigate its effectiveness at representing large allocation traces. Our proposal allows for a flexible encoding of information in the trace to achieve greater compression. We evaluate our preliminary design in two dimensions. First, we measure how effective these encodings are at reducing trace size. Second we consider how a meta-level specification language could be used to describe such formats and to generate trace readers and writers automatically.
1053009
Learning Dictionaries for Information Extraction by Multi-Level Bootstrapping
Information extraction systems usually require two dictionaries: a semantic lexicon and a dictionary of extraction patterns for the domain. We present a multilevel bootstrapping algorithm that generates both the semantic lexicon and extraction patterns simultaneously. As input, our technique requires only unannotated training texts and a handful of seed words for a category. We use a mutual bootstrapping technique to alternately select the best extraction pattern for the category and bootstrap its extractions into the semantic lexicon, which is the basis for selecting the next extraction pattern. To make this approach more robust, we add a second level of bootstrapping (metabootstrapping) that retains only the most reliable lexicon entries produced by mutual bootstrapping and then restarts the process. We evaluated this multilevel bootstrapping technique on a collection of corporate web pages and a corpus of terrorism news articles. The algorithm produced high-quality dictionaries for several semantic categories.
40732361
Meeting Diverse User Needs
Any higher education department must provide a variety of information to several disparate audiences, recognising their diverse needs. Managing these challenges and changes demands flexibility and auditability. Technology can provide the mechanism to meet these information needs and educational challenges, and to manage changing social and political demands. In this paper we offer pointers abstracted and generalised from our experience for organizational change in similar environments. We explain the development of a large web-based information system designed to meet these challenges. Our goal has been not only to improve our site but also to embed it at the heart of the department’s culture. We describe the technical innovations that are required to manage this large-scale site and that permit the flexibility needed to respond to change. Finally, we suggest measures by which the success of enterprises of this kind may be measured. Evidence of changes in student and staff* expectations, behaviour, and study patterns is presented.
6883273
The Workshop Program at the Nineteenth National Conference on Artificial Intelligence
AAAI presented the AAAI-04 workshop program on July 25-26, 2004 in San Jose, California. This program included twelve workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were as follows: (1) Adaptive Text Extraction and Mining; (2) Agent Organizations: Theory and Practice; (3) Anchoring Symbols to Sensor Data; (4) Challenges in Game AI; (5) Fielding Applications of Artificial Intelligence; (6) Forming and Maintaining Coalitions in Adaptive Multiagent Systems; (7) Intelligent Agent Architectures: Combining the Strengths of Software Engineering and Cognitive Systems; (8) Learning and Planning in Markov Processes--Advances and Challenges; (9) Semantic Web Personalization; (10) Sensor Networks; (11) Spatial and Temporal Reasoning; and (12) Supervisory Control of Learning and Adaptive Systems.
11914137
TREC-10 Experiments at University of Maryland CLIR and Video
The University of Maryland Researchers participated in both the Arabic-English Cross Language Information Retrieval (CLIR) and Video tracks of TREC-10. In the CLIR track, our goal was to explore effective monolingual Arabic IR techniques and effective query translation from English to Arabic for cross language IR. For the monolingual part, the use of the different index terms including words, stems, roots, and character n-grams were explored. For the English-Arabic CLIR, the use of MT, wordlist based translation, and non-dictionary words transliteration was explored. In the video track, we participated in the shot boundary detection, and known item search with the primary goals being to evaluate existing technology for shot detection and a new approach to extending simple visual image queries to video sequences. We present a general overview of the approaches, summarize the results in discuss how the algorithms are being extended.