30,554 research outputs found

    Phonetic Searching

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    An improved method and apparatus is disclosed which uses probabilistic techniques to map an input search string with a prestored audio file, and recognize certain portions of a search string phonetically. An improved interface is disclosed which permits users to input search strings, linguistics, phonetics, or a combination of both, and also allows logic functions to be specified by indicating how far separated specific phonemes are in time.Georgia Tech Research Corporatio

    Trade-offs between Selection Complexity and Performance when Searching the Plane without Communication

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    We consider the ANTS problem [Feinerman et al.] in which a group of agents collaboratively search for a target in a two-dimensional plane. Because this problem is inspired by the behavior of biological species, we argue that in addition to studying the {\em time complexity} of solutions it is also important to study the {\em selection complexity}, a measure of how likely a given algorithmic strategy is to arise in nature due to selective pressures. In more detail, we propose a new selection complexity metric χ\chi, defined for algorithm A{\cal A} such that χ(A)=b+log\chi({\cal A}) = b + \log \ell, where bb is the number of memory bits used by each agent and \ell bounds the fineness of available probabilities (agents use probabilities of at least 1/21/2^\ell). In this paper, we study the trade-off between the standard performance metric of speed-up, which measures how the expected time to find the target improves with nn, and our new selection metric. In particular, consider nn agents searching for a treasure located at (unknown) distance DD from the origin (where nn is sub-exponential in DD). For this problem, we identify loglogD\log \log D as a crucial threshold for our selection complexity metric. We first prove a new upper bound that achieves a near-optimal speed-up of (D2/n+D)2O()(D^2/n +D) \cdot 2^{O(\ell)} for χ(A)3loglogD+O(1)\chi({\cal A}) \leq 3 \log \log D + O(1). In particular, for O(1)\ell \in O(1), the speed-up is asymptotically optimal. By comparison, the existing results for this problem [Feinerman et al.] that achieve similar speed-up require χ(A)=Ω(logD)\chi({\cal A}) = \Omega(\log D). We then show that this threshold is tight by describing a lower bound showing that if χ(A)<loglogDω(1)\chi({\cal A}) < \log \log D - \omega(1), then with high probability the target is not found within D2o(1)D^{2-o(1)} moves per agent. Hence, there is a sizable gap to the straightforward Ω(D2/n+D)\Omega(D^2/n + D) lower bound in this setting.Comment: appears in PODC 201

    miraQA: Initial experiments in Question Answering

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    We present the miraQA system that constitutes MIRACLE first experience in Question Answering for monolingual Spanish and has been developed for QA@CLEF 2004. The architecture of the system is described and details of our approach to Statistical Answer Extraction based on Hidden Markov Models are presented. One run that uses last year question set for training purposes has been submitted. The results are presented together with ideas for improvement

    Saying What You're Looking For: Linguistics Meets Video Search

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    We present an approach to searching large video corpora for video clips which depict a natural-language query in the form of a sentence. This approach uses compositional semantics to encode subtle meaning that is lost in other systems, such as the difference between two sentences which have identical words but entirely different meaning: "The person rode the horse} vs. \emph{The horse rode the person". Given a video-sentence pair and a natural-language parser, along with a grammar that describes the space of sentential queries, we produce a score which indicates how well the video depicts the sentence. We produce such a score for each video clip in a corpus and return a ranked list of clips. Furthermore, this approach addresses two fundamental problems simultaneously: detecting and tracking objects, and recognizing whether those tracks depict the query. Because both tracking and object detection are unreliable, this uses knowledge about the intended sentential query to focus the tracker on the relevant participants and ensures that the resulting tracks are described by the sentential query. While earlier work was limited to single-word queries which correspond to either verbs or nouns, we show how one can search for complex queries which contain multiple phrases, such as prepositional phrases, and modifiers, such as adverbs. We demonstrate this approach by searching for 141 queries involving people and horses interacting with each other in 10 full-length Hollywood movies.Comment: 13 pages, 8 figure
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