15205488
Query word deletion prediction
Web search query logs contain traces of users' search modifications. One strategy users employ is deleting terms, presumably to obtain greater coverage. It is useful to model and automate term deletion when arbitrary searches are conjunctively matched against a small hand constructed collection, such as a hand-built hierarchy, or collection of high-quality pages matched with key phrases. Queries with no matches can have words deleted till a match is obtained. We provide algorithms which perform substantially better than the baseline in predicting which word should be deleted from a reformulated query, for increasing query coverage in the context of web search on small high-quality collections.
207159138
Generating query substitutions
We introduce the notion of query substitution, that is, generating a new query to replace a user's original search query. Our technique uses modifications based on typical substitutions web searchers make to their queries. In this way the new query is strongly related to the original query, containing terms closely related to all of the original terms. This contrasts with query expansion through pseudo-relevance feedback, which is costly and can lead to query drift. This also contrasts with query relaxation through boolean or TFIDF retrieval, which reduces the specificity of the query. We define a scale for evaluating query substitution, and show that our method performs well at generating new queries related to the original queries. We build a model for selecting between candidates, by using a number of features relating the query-candidate pair, and by fitting the model to human judgments of relevance of query suggestions. This further improves the quality of the candidates generated. Experiments show that our techniques significantly increase coverage and effectiveness in the setting of sponsored search.
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)
5135848
Using temporal profiles of queries for precision prediction
A key missing component in information retrieval systems is self-diagnostic tests to establish whether the system can provide reasonable results for a given query on a document collection. If we can measure properties of a retrieved set of documents which allow us to predict average precision, we can automate the decision of whether to elicit relevance feedback, or modify the retrieval system in other ways. We use meta-data attached to documents in the form of time stamps to measure the distribution of documents retrieved in response to a query, over the time domain, to create a temporal profile for a query. We define some useful features over this temporal profile. We find that using these temporal features, together with the content of the documents retrieved, we can improve the prediction of average precision for a query.
572074
Building mosaics from video using MPEG motion vectors
Abstract : In this paper, we present a novel way of creating mosaics from an MPEG video sequence. Two original aspects of our work are that (1) we explicitly compute camera motion between frames and (2) we deduce the camera motion directly from the motion vectors encoded in the MPEG video stream. This enables us to create mosaics more simply and quickly than with other methods.
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.
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.
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.
32418489
Formal Verification of Out-of-Order Execution with Incremental Flushing
We present an approach for formally verifying that a high-level microprocessor model behaves as defined by an instruction-set architecture. The technique is based on a specialization of self consistency called incremental flushing and reduces the need and effort required to create manually-generated implementation abstractions. Additionally, incremental flushing reduces the computational complexity of the proof obligations generated when reasoning about out-of-order execution. This is accomplished by comparing the functional behavior of the implementation abstraction over two sets of inputs: one that represents normal operation and one that is simpler, but functionally equivalent. The approach is illustrated on a simple out-of-order microprocessor core.