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.
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.
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.
1333290
History repeats itself: repeat queries in Yahoo's logs
Thanks to the ubiquity of the Internet search engine search box, users have come to depend on search engines both to find and re-find information. However, re-finding behavior has not been significantly addressed. Here we look at re-finding queries issued to the Yahoo! search engine by 114 users over a year.
10387396
N Semantic Classes are Harder than Two
We show that we can automatically classify semantically related phrases into 10 classes. Classification robustness is improved by training with multiple sources of evidence, including within-document cooccurrence, HTML markup, syntactic relationships in sentences, substitutability in query logs, and string similarity. Our work provides a benchmark for automatic n-way classification into WordNet's semantic classes, both on a TREC news corpus and on a corpus of substitutable search query phrases.
1946418
A Cyclic Distributed Garbage Collector for Network Objects
This paper presents an algorithm for distributed garbage collection and outlines its implementation within the Network Objects system. The algorithm is based on a reference listing scheme, which is augmented by partial tracing in order to collect distributed garbage cycles. Processes may be dynamically organised into groups, according to appropriate heuristics, to reclaim distributed garbage cycles. The algorithm places no overhead on local collectors and suspends local mutators only briefly. Partial tracing of the distributed graph involves only objects thought to be part of a garbage cycle: no collaboration with other processes is required. The algorithm offers considerable flexibility, allowing expediency and fault-tolerance to be traded against completeness.
62688407
Extended subject access to hypertext online documentation. Parts I and II: The search-support and maintenance problems
The DFT (DOCUMENT, FIND, THESEUS) online documentation system resembles other hypertext software in managing a full‐text database with reference links (pointers) between passages (nodes). But DFT's prime role as an end‐user reference service at a computer center where text updates are frequent required extra access methods often neglected in hypertext systems. To solve the problem of rapid, high‐recall search for specific answer passages we combined syntactical interface features (substring and fuzzy matching of search terms) with a semantic expansion of the system's entry vocabulary. Extensive aliasing increased the number of descriptions under which any passage could be found. Conversion of the NBS GAMS classification scheme for mathematical software into descriptors allowed us to impose a virtual organization on our subroutine documentation that supports easy, task‐oriented retrieval without tedious path walking. To solve the problem of reliable database maintenance amid frequent passage revisions we developed software tools for flexible input of text and entry‐term changes, as well as for thorough and versatile status reporting. These, together with special features for testing and privately simulating changes and for coordinating the sequence of text and entry‐term updates for maximum efficiency, yielded a robust and practical answer‐delivery system. © 1991 John Wiley & Sons, Inc.
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.
17231423
Lifted-FL: A Pragmatic Implementation of Combined Model Checking and Theorem Proving
Combining theorem proving and model checking offers the tantalizing possibility of efficiently reasoning about large circuits at high levels of abstraction. We have constructed a system that seamlessly integrates symbolic trajectory evaluation based model checking with theorem proving in a higher-order classical logic. The approach is made possible by using the same programming language (fl) as both the meta and object language of theorem proving. This is done by "lifting" fl, essentially deeply embedding fl in itself. The approach is a pragmatic solution that provides an efficient and extensible Verification environment. Our approach is generally applicable to any dialect of the ML programming language and any model-checking algorithm that has practical inference rules for combining results.
16794414
A Framework for Microprocessor Correctness Statements
Most verifications of out-of-order microprocessors compare state-machine-based implementations and specifications, where the specification is based on the instruction-set architecture. The different efforts use a variety of correctness statements, implementations, and verification approaches. We present a framework for classifying correctness statements about safety that is independent of implementation representation and verification approach. We characterize the relationships between the different statements and illustrate how existing and classical approaches fit within this framework.