Paulo Filipe Telles
They're not going to get much support in the chemistry or physics or biology department. But if they feel like trying it, well, it's a free country, try that. We'll judge it by the results that come out. So if results come from study of massive data, rather like videotaping what's happening outside the window, fine-look at the results. I don't pay much attention to it. I don't see much in the way of results. My judgment, if you like, is that we learn more about language by following the standard method of the sciences.
The standard method of the sciences is not to accumulate huge masses of unanalyzed data and to try to draw some generalization from them. The modern sciences, at least since Galileo, have been strikingly different.
What they have sought to do was to construct refined experiments which ask, which try to answer specific questions that arise within a theoretical context as an approach to understanding the world. Rather, recording data supposes:. Certainly Chomsky's reaction is also due to the term corpus linguistics , which may be interpreted as the linguistics of corpora. A less ambiguous label would be corpus-based linguistics , indicating that there is linguistic activity beyond the corpus. A ndor , J.
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B otha , Rudolf The Conduct of Linguistic Inquiry. Linguistic Inquiry. B ybee , Joan C ollini , Stefan What Are Universities For? New York: Penguin. D urand , Jacques Kawaguchi, M. Amsterdam: Benjamins. F eyerabend , Paul Wider den Methodenzwang. Frankfurt am Main Suhrkamp. G reen , Anthony D. Phonology Limited. H alle , Morris H arris , Zellig Methods in Structural Linguistics. Edition entitled Structural Linguistics. Journal of French Language Studies. Saint-Denis: PUV. Corpus Linguistics and the Web. Amsterdam: Rodopi.
J oos , Martin K aye , Jonathan Dordrecht: Foris. K irchner , Robert An effort-based approach to consonant lenition. D dissertation, University of California at Los Angeles. L aks , Bernard L aks , Bernard Ms L aks , Bernard ed. French liaison and the lexical repository.
S ampson , G. Journal of Linguistics. S cheer , Tobias T rubetzkoy , Nikolai Sergeyevich Of course, identifying material on Google and then testing it with native speakers is a perfectly regular strategy of investigation, and there is no objection. It is only when statistics are directly made on Google data that there is no way to control for the caveats.
A standard response is that caveat-created noise will lean out statistically and may be detected by this means. Or that the volume of this noise is so small that it won't have any significant impact on the result. I have never seen a case where these assertions are checked against the data, the reason being that 1 separating noise from non-noise statistically is not an easy thing to do and 2 even if this were done, the result could not be compared to the real amount of noise, which is unknown and cannot be calculated.
Universities are thus under pressure an economic "incentive" to distribute more degrees. This can be achieved either by trying to provide better instruction and thereby modifying the content of the students' minds, or simply by lowering the standards: students need a degree, they pay for it, so give it to them no matter what their skills and competences.
Guess which way Universities go…. See e. Bybee on this issue. Plan 1. Things that ought to be trivial. The corpus and real-world issues. When the system goes mad: corpora in the midst of "employability" and "societal demand". Instruments serve a purpose and are theory-dependent. More or less direct access to data for different linguistic disciplines. Data are an artefact, not a natural object: they are always constructed. Limitations: relevant information that the corpus cannot provide.
Corpora cannot attest the absence of something. Lee brothers Lee, W. William Raymond Lee, William, Jr. Lee-Neufville treaty Leech Leechman, Rev. Leger, Lt. Jacques Lemarchand, M. Abiel of Woodstock, Conn. Leonard family, of Taunton, Mass. Leonard, George, leader of marauding tories Leonard, Jonathan H. Leonard, Miss of Haverhill? Leonard, Miss, Philadelphia landlady Leonard, Mr. Lewd, lascivious, and wanton behavior, crime, No. Wallis Lewis, Dr. Lewis, Brig.
Andrew Lewis, Dixon H. Lewis, Harriet tenant Lewis, Joseph, witness, No. Lawrence, Wood Lawn, Va. Lewis, Mr. Lewis, W. Lewis, William Lewis, William D. Lexington, Mass. Parr Lincoln, Abner H. Bela , 1st husband of Hannah Quincy Lincoln, Maj. Benjamin Lincoln, Benjamin, Jr. Benjamin Lincoln, Benjamin, Sr.
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