categorizing calls (Tang et al., 2003), call rout-ing (Kuo and Lee, 2003; Haffner et al., 2003), ob-taining call log summaries (Douglas et al., 2005),agent assisting and monitoring (Mishne et al.,2005) has appeared in the past. In some cases, theyhave modeled these as text classification probl[r]
source text into a shorter version while preservingits information content. Humans summarize ona daily basis and effortlessly, but producing highquality summaries automatically remains a chal-lenge. The difficulty lies primarily in the natureof the task which is complex, must satisfy manyconst[r]
plications as the data rates increase towards high-definitiontelevision (HDTV). Surveillance application is one of themost rapidly developing areas. Video m onitoring is nowpresent in almost every store or public place. The amountof video data produced by these systems requires them to beable[r]
NTCIR-4 (Vol. Supl. 2), 2004b. Document Understanding Conference, http://duc.nist.gov/duc2004/tasks.html, 2004. Fahad Alotaiby, Ibrahim Alkharashi and Salah Foda. Processing large Arabic text corpora: Preliminary analysis and results. In Proceedings of the Second In-ternational Conference on[r]
evign´e, France4INRIA Rocquencourt, AOSTE, BP 105, 78153 Le Chesnay, FranceReceived 15 October 2004; Revised 24 May 2005; Accepted 21 June 2005Future generations of mobile phones, including advanced video and digital communication layers, represent a great challenge interms of real-tim[r]
matic summaries only; after all, the whole pointof creating evaluation metrics is to score and rankthe output of systems. Such a perspective can berather short-sighted, though, given that we expectcontinuous improvement from the summarizationsystems to, ideally, human levels, so[r]
multiplexer is required or not to control the signal flow,and whether a pipeline register should be included at thecurrent stage. The look-up t ables for tw iddle factors willbe automatically generated after its word length and thetable size is determined. For the multichannel configuration,the second[r]
ous questions. Our empirical results show that thisapproach leads to good performance on TREC col-lections and our ambiguous question collections.The contribution of this paper are: (1) a new con-cept cluster method that maps a document into avector of subtopics; (2) a new ranking sche[r]
Instructional SystemEnergy management is the process of monitoring, coordinating, and controlling the generation,transmission, and distribution of electrical energy. The physical plant to be managed includes generatingplants that produce energy fed through transformers to the hi[r]
Y. Gong and X. Liu. 2002. Generic text summarizationusing relevance measure and latent semantic analy-sis. In Proceedings of ACM SIGIR, New Orleans,US.E. Hovy. 2005. Automated text summarization. InRuslan Mitkov, editor, The Oxford Handbook ofComputational Linguistics, pages 583–598. OxfordUn[r]
both quality and quantity. However, it remains yet tobe investigated in what ways the recorded interac-tions relate to the competence developed.In conclusion, although many methods of assess-ment have been proposed so far, none of them is yetadequately tested in the environment of[r]
ple who meet each other briefly bychance and who are unlikely tomeet again.shipwreck ["SIp rEk] 1. n. thedestruction of a ship caused byrunning into something. 2. n. theremains of a ship that has under-gone Q. 3. tv. to cause someone tobe harmed or stranded owing toQ.shirt ["S#t] n. a p[r]
the CSJ. However, Uchimoto et al. reported that theaccuracy of automatic word segmentation and POStagging was 94 points in F-measure (Uchimoto etal., 2002). That is much lower than the accuracy ob-tained by manual tagging. Several problems led tothis inaccuracy. In the following, we de[r]
Treat Your CustomersThirty Lessons on Service and Sales That I Learned at My Family’s Dairy Queen StoreBy Bob Miglani Published by Hyperion, 2006ISBN 1401301983IntroductionWhen Bob Miglani was growing up, he spent everysummer working at his family’s Dairy Queen store.At the time, it was mostly just[r]
ity of sentiment prediction, we propose two additions.Firstly, while we use simple heuristics to handle exten-sions of words in tweets, a deeper study is required todecipher the pragmatics involved. Secondly, a spam de-tection module that eliminates promotional tweets beforeperf[r]
including a cornucopia of options, it is not always clearwhich parts of a specification were actually implementedand which part s were omitt ed for the sake of simplicity* Correspondence: chmehl@gmail.comInstitute of Telecommunications, Vienna University of Technol[r]
of signal gener ation and detection/decoding. We use IEEE802.11a to exemplify our presentation in this paper.The OFDM-based WLAN system, as specified by theIEEE 802.11a standard, uses packet-based transmission. Eachpacket, as shown in Figure 1, consists of an OFDM packetpreamble, a sign[r]
correction. The data is mapped to spatial layers according to the type of multi-antenna technique (e.g. closed loop spatial multiplexing, open-loop, spatial multiplexing, transmit diversity, etc.) and then mapped to a modulation symbol which includes QPSK, 16 QAM and 64 QAM. Physical resource[r]
Artefact StatesEnvironment StatesEnvironment DynamicsNWSO…… Fig. 1. The aesthetic awareness display frameworks covers the path from collecting potential awareness data from sensors (Acquisition Layer), to the discovery of meaning in that data (Interpretation Layer), the distribution of