7.18.2020

Fiona Apple McAfee-Maggart (born September 13, 1977) People also ask What nationality is Fiona Apple? please_look_at_the_pictures

Omg her energy is just chaotic

The OG Angry or “Different” artist

I tried enabling closed captions but my monitor exploded.

I can't get enough of this song, i'm so full but i need more

1

This song does something to me...... Pure emotion

Nobody: Fiona: “ If I’m butter than he’s a hot knife “

2

This is the first day I heard it 1. know the whole song by heart 2. Watched the video about 12 times today 3. Committed a crime of looking depressed 😭😭😭

8

Mountain man + Fiona apple = yeeeaaasss queeeen!! OMG plz !!! Plz do a Collab with Mountain man.

Wow. The rhythm and silence and singing and synced song. Beautiful. The piano is key to the drums. It adds character and charm. If I'm butter he's a hot knife. If I'm butter, well welcome to margarine. Cheaper. And all I can afford. This song is brilliant. Takes me to places I wish I could be.

1

wow never knew this video existed, surprised that it was directed by PTA (parent teachers association)

1

To me this song perfectly represents Fiona Apple's unique place within our culture: she is a singular genius who delivers on her own terms

2

I feel like Fiona could be a great actress

Gotta ad I didn't know this was 6yrs ago. But still. Genius xoxo

Fiona, Gurl, the wait was worth it. Glad to see you back with a vengence ;)

1

who are these people without ears shootin' a thumbs down on this video? smh

2

I don't understand, she always seems like she's having trouble saying the words? It makes me feel weird

I always thought it said something about a "cinnamon scone". CinemaScope, hmm I guess.

₩○₩ colour us impressed! ₩○₩ fiona ₩○₩

Saw her in concert several years ago. One of the best shows I've ever seen! Hope to do it again!

Fiona Apple has the exquisite ability to outdo herself every time that she releases an album, it’s her still, her soul bare but yet it’s a new her, there’s evolution but within her ... Fetch is another great album , she has yet to disappoint me !


This is the worst

TEETH KEEPS YOU SAFE... SEE? <3 K

1

I DON'T KNOW.... HMM?? WHAT DO YOU KNOW... FOR SURE? HMMMMM???? YOUR TEETH R STRONGER.. AFRICAN AMERICAN.. YES <3 K

1

R U AFRICAN AMERICAN? Y YOUR LIPS R SO BIG? <3 K


I WAS READING YOU... R ALBANIAN? IS THAT CORRECT? PART? HMMMMM? RARE... YES .. IN MY TOWN RARE... YES. KNOW I DON'T WANT TO GO BACK TO ALBANIA HA! I AM PART ... GERMAN.... SLOVAK ... <3 K NATIVE AMERICAN... YES... THOSE R WHAT I KNOW.... YES.... AMISH... HA! PA DUTCH <3 K





  1. COVERAGE FOR LIST:

    People also ask

    What nationality is Fiona Apple?



  2. IN CORPUS:
    brown_strip.txt
    * SIZE: 1,083,279 wds

    HITS of list in corpus: 213,393
    COVERAGE
    (hits/size)x100 = 19.70%

    @dougmeet
    👏😁1w

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    👌🏼

    interculturelan
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Keyword highlighting:
  • Some of these are intrinsic to web text, and include the unorthodox definition of ‘text’,heterogeneity of web-held data, lack of reliable punctuation, lack of reliableinformation on language, date, author; and the focus on current news and recentlyupdated pages at the expense of access to earlier data.Figure 6: external collocates and key phrases for search pattern [stitch in time saves]Other current WebCorp performance problems relate to the high degree of processingand storage required to meet user needs expressed for simultaneous use for more users,including class-sized groups; grammatical and better collocational analysis; and moresophisticated pattern matching.However, the major constraint on the improvement of WebCorp performance is itsreliance on a commercial search engine.
  • Our initial estimate is that thenewspaper ‘domain’ accessible through the WebCorp Linguistic Search Engine willcontain at least 750 million word tokens.Our newspaper crawler has been employed for our own use for the past 5 years andincorporates the following:•exclusion lists (i.e. particular kinds of pages on newspaper sites NOT to download)•error logging and re-queuing of failed pages•extraction of date, author, headline and sub-headline•URL parsing to extract section of newspaper (Sport, Media, etc)•storage of articles by date (to facilitate diachronic analysis)•removal of advertising banners and links•stripping of HTML mark-upWe shall continue to use these tailored crawlers for our newspaper ‘domain’ and forother domains where all pages are in a uniform format.
  • SearchingNew search engine architectureFigure 7: the new WebCorp Linguistic Search Engine architectureThe components of the new linguistic search engine system are as follows:1.web crawler2.parser / tokeniser3.indexer4.WebCorp tools5.WebCorp user front end6.more, also off-line, linguistic processing toolsand we have already developed them individually, as we shall now outline.1. Web Crawler: We have already developed a crawler module in Perl to select and download articlesfrom UK newspaper websites.
  • Search-and-rescue crewrescues former mayor's dog stuck on ledgeFigure 5: results for search pattern [stitch in time saves] with nine filtered outWebCorp also provides some basic statistical information, in particular about the‘collocational profile’ (Renouf, e.g. 1993) of the word, though this is of necessitycurrently restricted to simple ranked frequency of occurrence in the set of pages visited.Figure 6 shows ‘external collocates’ for the phrasal fragment [stitch in time saves],since the word slot on which the query is focussed lies outside the pattern submitted(i.e. in position R1).
Sentences:

  1. Figure 4: results for search term [donkey brown]An instance of the phrasal variability and creativitywhich can be investigated with theuse of WebCorp is the proverb a stitch in time saves nine.This conventional andestablished idiom can be searched for in its canonical form, but if the linguist suspectsthat, like all so-called ‘frozen expressions’, it can actually be modified in use, WebCorpoffers the opportunity to test this through the submission of this string with various keywords suppressed.Thus in Figure 5, we see the output of variants forced by the use ofthe word filter option to suppress the word nine in the output.What this reveals, amongseveral other interesting facts about phrasal creativity in general, is that one conventionof creative modification is that the substituted word may rhyme or be phonologicallyreminiscent of the original word, as in examples 9 and 10.Whether this is intended toassist interpretation or pay homage to the original phrase probably depends on thecreative process and context involved.1.A stitch in time saves embarrassment on the washing line.2.Like they say, a stitch in time saves two in the bush.3.The best maxim is be vigilant - a stitch in time saves a lot of moneyand inconvenience.Keeping a careful eye on your building will savefortunes 4.follow the adage "a stitch in time saves spoilt underwear".5.A stitch in time saves lives.Tenants tipped to share safety training 6.Data Integrity: A stitch in time saves your data.Under OS 8.5 andhigher Disk First aid automatically launches during startup 7.you know what they say; A stitch in time saves disintegration onentering hyperspace.8.he winds up trying to tie his shambling creation together, just likethe Doktor: 9.a stitch in time saves, nein? 10.Montrose team's stitch in time savescanine.Search-and-rescue crewrescues former mayor's dog stuck on ledgeFigure 5: results for search pattern [stitch in time saves] with nine filtered outWebCorp also provides some basic statistical information, in particular about the‘collocational profile’ (Renouf, e.g. 1993) of the word, though this is of necessitycurrently restricted to simple ranked frequency of occurrence in the set of pages visited.Figure 6 shows ‘external collocates’ for the phrasal fragment [stitch in time saves],since the word slot on which the query is focussed lies outside the pattern submitted(i.e. in position R1).If a search were being conducted on a variable word slot within thepattern, the corresponding ‘internal collocate’ (Renouf, 2003) analysis could equally beprovided.In addition, a simple heuristic (Renouf, ibid.) provides a set of possible keyphrases found within the results: in Figure 6, this indicates the more popular alternativephrases emerging in the place of the canonical a stitch in time saves nine.As said, the development of WebCorp has been founded on user feedback.This hascontinued to flow, and because we have been in a constant state of iterativedevelopment and testing, the comments have very often been taken account of inresponse to an earlier request by the time the same comment reappears.There are, alongside the extensive functions of WebCorp that have successfully beendeveloped, a range of problems which hinder the further improvement of the system.Some of these are intrinsic to web text, and include the unorthodox definition of ‘text’,heterogeneity of web-held data, lack of reliable punctuation, lack of reliableinformation on language, date, author; and the focus on current news and recentlyupdated pages at the expense of access to earlier data.Figure 6: external collocates and key phrases for search pattern [stitch in time saves]Other current WebCorp performance problems relate to the high degree of processingand storage required to meet user needs expressed for simultaneous use for more users,including class-sized groups; grammatical and better collocational analysis; and moresophisticated pattern matching.However, the major constraint on the improvement of WebCorp performance is itsreliance on a commercial search engine.The problems posed by this dependence are asfollows: the speed of results is inhibited; there are unpredictable changes in Googleservice and even at the best of times, Google is geared to commercial rather thanlinguistic or even academic requirements, which can mean, for example, unreliableword count statistics, and lack of consistent support for wildcard search.Google alsouses its own page ranking to deliver the results.The top ranked pages are notnecessarily the most relevant ones in view of linguistics.In addition, the delay built inby Google-dependent text extraction means that the time subsequently required for thelinguistic post-processing of text is currently prohibitive, whether for POS tagging, for date and alphabetical sorting, or other requisite procedures.4.The WebCorp Linguistic Search Engine Our response to the problems anticipated and cited above has been to develop WebCorpwith an eye to creating the components that will be integral to an independent, linguistically tailored search engine.We are currently calling this the ‘Distinguishable Search Engine’, since WebCorp functionality will be integrated into the newarchitecture alongside the search engine, and the whole fronted by an enhanced versionof the WebCorp GUI.The new architecture is displayed graphically in Figure 7.Thegeneric term ‘linguistic search engine’ is in fact a misnomer, since the search engine,while informed by linguistic knowledge, will not be ‘linguistic’ as such.We sometimescall our embryonic system ‘the UCE Search Engine’, since our university is the primeinvestor in the new search engine component, providing both vast storage and amplehardware, as part of its serious commitment to research support in its centres ofexcellence.

Nichole Kolovani

  1. SearchingNew search engine architectureFigure 7: the new WebCorp Linguistic Search Engine architectureThe components of the new linguistic search engine system are as follows:1.web crawler2.parser / tokeniser3.indexer4.WebCorp tools5.WebCorp user front end6.more, also off-line, linguistic processing toolsand we have already developed them individually, as we shall now outline.1. Web Crawler: We have already developed a crawler module in Perl to select and download articlesfrom UK newspaper websites.

  2. These are currently restricted to the Guardian andIndependent but we shall add to them, with tabloid and other categories of journalism.Not all newspaper sites have full archives like the Guardian, so instead of downloadingretrospectively, as we have done hitherto, we shall download the current day’s articlesdaily in order to build up the corpus progressively.

  3. Our initial estimate is that thenewspaper ‘domain’ accessible through the WebCorp Linguistic Search Engine willcontain at least 750 million word tokens.Our newspaper crawler has been employed for our own use for the past 5 years andincorporates the following:•exclusion lists (i.e. particular kinds of pages on newspaper sites NOT to download)•error logging and re-queuing of failed pages•extraction of date, author, headline and sub-headline•URL parsing to extract section of newspaper (Sport, Media, etc)•storage of articles by date (to facilitate diachronic analysis)•removal of advertising banners and links•stripping of HTML mark-upWe shall continue to use these tailored crawlers for our newspaper ‘domain’ and forother domains where all pages are in a uniform format.

  4. We also have a specialised toolto extract neologisms from online articles in real-time.

  5. We shall expand this ‘live’system to monitor and record neologisms, although once the web texts are downloadedinto corpus format, we will begin to achieve this through the application of our fullAPRIL system [ ], as we have begun to do withGuardian articles more recently.In addition to our structured sub-domains, we shall download a very large (multi-terabyte) subset of random texts from the web, to create a mini version of the web itself.Some users will prefer to look at this, much as they do with WebCorp at present, ratherthan at carefully chosen sub-domains.

  6. The aim will not in itself be to build eitherspecific sub-corpora or ‘collections’ of texts from the web, as other people have done(e.g. BootCaT tools), but to find the right balance and combination of raw data, forinstance in selecting random texts within a specific domain.More generic tools will be required for the creation of this multi-terabyte mini-web, tocope with a variety of page layouts and formats.

  7. Several ready-made tools are availablefreely online but we are developing a new crawler for our specific task, building uponour experience with the newspaper downloads and making use of other open-sourcelibraries whenever possible.The new crawler will need to be ‘seeded’ in some way, i.e. told where to embark on itscrawl of the web.

  8. We could make the search process completely random by choosing astarting page and allowing the crawler to follow all links blindly, downloading everypage it encounters.

  9. This will not be appropriate, however, when building a structuredcorpus with carefully selected sub-domains.

  10. We shall employ other ‘seeding’ techniques including the use of Open Directory index,where editors classify web pages according to textual ‘domain’.

  11. 200212Figure 12: results from APRIL system - new adjectives with prefix un-, suffix -ed7.Concluding remarksIn view of the frustration and limitations posed by the current search engines, otherresearchers are also beginning to contemplate building their own search engine softwareand tools.

  12. The WaCky Project (2005) is still at the ideas stage, as is Kilgarriff (2003).Kilgarriff (2003) proposes a five-year project for a system similar to ours, where a setof URLs relevant to linguists would be downloaded, processed off-line and stored as acorpus for linguistic research.

  13. He plans less frequent updating than we do within ourdifferentiated update schedule.

  14. There is also some mention of future Grid interaction inhis design.

  15. We embrace the cooperative spirit that is implicit in the Grid ideal, but arenot dependent on the distributing processing element of Grid activity, being more thanadequately resourced with regard to computing storage and hardware.

  16. We have completed the components required for the creation of a linguistically-tailoredand accessorised search engine, and shall in the coming months assemble aninfrastructure that will be progressively incorporated into the WebCorp front-end toenhance its performance, and that of its users, on the fronts outlined above.

  17. Theimprovements will be incrementally perceptible at M.

  18. and Bernardini, S.

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  27. Available online from R., Jones, R.

  28. and Mladenic, D.

  29. (2001) Mining the web to create minoritylanguage corpora.

  30. CIKM 2001, 279–286.Kehoe, A.

  31. (forthcoming) Diachronic linguistic analysis on the web with WebCorp, inA.

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  33. Kehoe (eds.) The Changing Face of Corpus Linguistics(Amsterdam& Atlanta: Rodopi).Kehoe, A.

  34. and Renouf, A.

  35. (2002) WebCorp: Applying the Web to Linguistics andLinguistics to the Web.

  36. World Wide Web 2002 Conference, Honolulu, Hawaii, 7-11May 2002.

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  39. Proceedings ofThe Shallow Processingof Large Corpora Workshop (SProLaC 2003) Corpus Linguistics 2003, LancasterUniversity.Morley, B.

  40. (forthcoming) WebCorp: A tool for online linguistic information retrievaland analysis, in A.

  41. Renouf and A.

  42. Kehoe (eds.) The Changing Face of CorpusLinguistics(Amsterdam & Atlanta: Rodopi).Renouf, A., Pacey, M., Kehoe, A.

  43. and Davies, P.

  44. (forthcoming), Monitoring LexicalInnovation in Journalistic Text Across Time.Renouf, A., Morley, B.

  45. and Kehoe, A.

  46. (2003) Linguistic Research with the XML/RDFaware WebCorp Tool.

  47. WWW2003, Budapest.

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  49. (2002) WebCorp: providing a renewable data source for corpus linguists, inS.

  50. Granger and S.

  51. Petch-Tyson (eds.) Extending the scope of corpus-based research:new applications, new challenges.

  52. (Amsterdam & Atlanta: Rodopi) 39-58.Renouf, A.

  53. (1996) The ACRONYM Project: Discovering the Textual Thesaurus, in I.Lancashire, C.

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  55. Percy (eds.) Papers from English Language Research onComputerized Corpora (ICAME 16) (Rodopi, Amsterdam) 171-187.

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  57. (1993) Making Sense of Text: Automated Approaches to MeaningExtraction.

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