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<Paper uid="W06-1705">
  <Title>Annotated web as corpus</Title>
  <Section position="3" start_page="27" end_page="29" type="relat">
    <SectionTitle>
2 Related Work
</SectionTitle>
    <Paragraph position="0"> The vast majority of previous work on corpus annotation has utilised either manual coding or automated software tagging systems, or else a semi-automatic combination of the two approaches e.g. automated tagging followed by manual correction. In most cases a stand-alone system or client-server approach has been taken by annotation software using batch processing techniques to tag corpora. Only a handful of web-based or email services (CLAWS  ) are available, for example, in the application of part-of-speech tags to corpora. Existing tagging systems are 'small scale' and typically impose some limitation to prevent overload (e.g. restricted access or document size). Larger systems to support multiple document tagging processes would require resources that cannot be realistically provided by existing single-server systems. This corpus annotation bottleneck becomes even more problematic for voluminous data sets drawn from the web. The use of the web as a corpus for teaching and research on language has been proposed a number of times (Kilgarriff, 2001; Robb, 2003; Rundell, 2000; Fletcher, 2001, 2004b) and received a special issue of the journal Computational Linguistics (Kilgarriff and Grefenstette, 2003). Studies have used several different methods to mine web data. Turney (2001) extracts word co-occurrence probabilities from unlabelled text collected from a web crawler. Baroni and Bernardini (2004) built a corpus by iteratively searching Google for a small set of seed terms. Prototypes of Internet search engines for linguists, corpus linguists and lexicographers have been proposed: WebCorp (Kehoe and Renouf, 2002), KWiCFinder (Fletcher, 2004a) and the Linguist's Search Engine (Kilgarriff, 2003; Resnik and Elkiss, 2003). A key concern in corpus linguistics and related disciplines is verifiability and replicability of the results of studies. Word frequency counts in internet search engines are inconsistent and unreliable (Veronis, 2005). Tools based on static corpora do not suffer from this problem, e.g.</Paragraph>
    <Paragraph position="1"> BNCweb  , developed at the University of Zurich, and View  are both based on the British National Corpus.</Paragraph>
    <Paragraph position="2"> Both BNCweb and View enable access to annotated corpora and facilitate searching on part-of-speech tags. In addition, PIE  (Phrases in English), developed at USNA, which performs searches on n-grams (based on words, parts-of-speech and characters), is currently restricted to the British National Corpus as well, although other static corpora are being added to its database. In contrast, little progress has been made toward annotating sizable sample corpora from the web.</Paragraph>
    <Paragraph position="3"> &amp;quot;Real-time&amp;quot; linguistic analysis of web data at the syntactic level has been piloted by the Linguist's Search Engine (LSE). Using this tool, linguists can either perform syntactic searches via parse trees on a pre-analysed web collection of around three million sentences from the Internet Archive (www.archive.org) or build their own collections from AltaVista search engine results. The second method pushes the new collection onto a queue for the LSE annotator to analyse. A new collection does not become available for analysis until the LSE completes the annotation process, which may entail significant delay with multiple users of the LSE server. The Gsearch system (Corley et al., 2001) also selects sentences by syntactic criteria from large on-line text collections. Gsearch annotates corpora with a fast chart parser to obviate the need for corpora with pre-existing syntactic mark-up. In contrast, the Sketch Engine system to assist lexicographers to construct dictionary entries requires large pre-annotated corpora. A word sketch is an automatic one-page corpus-derived summary of a word's grammatical and collocational behaviour. Word Sketches were first used to prepare the Macmillan English Dictionary for Advanced Learners (2002, edited by Michael Rundell). They have also served as the starting point for high-accuracy Word Sense Disambiguation. More recently, the Sketch Engine was used to develop the new edition of the Oxford Thesaurus of English (2004, edited by Maurice Waite).</Paragraph>
    <Paragraph position="4"> Parallelising or distributing processing has been suggested before. Clark and Curran's (2004) work is in parallelising an implementation of log-linear parsing on the Wall Street Journal Corpus, whereas we focus on part-of-speech tagging of a far larger and more varied web corpus, a technique more widely considered a prerequisite for corpus linguistics research. Curran (2003)  http://pie.usna.edu/ suggested distributed processing in terms of web services but only to &amp;quot;allow components developed by different researchers in different locations to be composed to build larger systems&amp;quot; and not for parallel processing. Most significantly, previous investigations have not examined three essential questions: how to apply distributed techniques to vast quantities of corpus data derived from the web, how to ensure that web-derived corpora are representative, and how to provide verifiability and replicability. These core foci of our work represent crucial innovations lacking in prior research. In particular, representativeness and replicability are key research concerns to enhance the reliability of web data for corpora.</Paragraph>
    <Paragraph position="5"> In the areas of Natural Language Processing (NLP) and computational linguistics, proposals have been made for using the computational Grid for data-intensive NLP and text-mining for e-Science (Carroll et al., 2005; Hughes et al, 2004). While such an approach promises much in terms of emerging infrastructure, we wish to exploit existing computing infrastructure that is more accessible to linguists via a P2P approach. In simple terms, P2P is a technology that takes advantage of the resources and services available at the edge of the Internet (Shirky, 2001). Better known for file-sharing and Instant Messenger applications, P2P has increasingly been applied in distributed computational systems. Examples include SETI@home (looking for radio evidence of extraterrestrial life), ClimatePrediction.net (studying climate change), Predictor@home (investigating protein-related diseases) and Einstein@home (searching for gravitational signals). A key advantage of P2P systems is that they are lightweight and geared to personal computing where informal groups provide unused processing power to solve a common problem. Typically, P2P systems draw upon the resources that already exist on a network (e.g. home or work PCs), thus keeping the cost to resource ratio low. For example the fastest supercomputer cost over $110 million to develop and has a peak performance of 12.3 TFLOPS (trillions of floating-point operations per second). In contrast, a typical day for the SETI@home project involved a performance of over 20 TFLOPS, yet cost only $700,000 to develop; processing power is donated by user PCs. This high yield for low start-up cost makes it ideal for cheaply developing effective computational systems to realise, deploy and evaluate our framework. The deployment of computational based P2P systems is supported by archi- null , which provide a platform on which volunteer based distributed computing systems can be built. Lancaster's own P2P Application Framework (Walkerdine et al., submitted) also supports higher-level P2P application development and can be adapted to make use of the BOINC architecture.</Paragraph>
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