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Academic writing in the Computational Linguistics research area Kees van Deemter University of Aberdeen.

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Presentation on theme: "Academic writing in the Computational Linguistics research area Kees van Deemter University of Aberdeen."— Presentation transcript:

1 Academic writing in the Computational Linguistics research area Kees van Deemter University of Aberdeen

2 Aim of these slides Aim: to offer guidance to students at the 2011 HIT-MSRA Graduate Summerschool in Human Language Technology (Harbin, China) With thanks to the participants of the summer school, and to Judith Masthoff, whose comments led to various improvements

3 Plan of the talk 1.Paper reviewing: a game for anthropologists, adapted for Computational Linguists 2.The peer review process 3.Some ultra-brief notes on writing style and plagiarism

4 A game: Let’s review some papers A game by the anthropologist Jim Moore (UCSD) (adapted from his web site)

5 Moore’s Game 1) I think the sky is blue. The end.

6 BAD: no research; all opinion. Research is about trying to learn more than what you already know/think.

7 2) Jones (2004) says the sky is blue. The end.

8 POOR: If it were Jones (2004), Wong (2005), and Dagosto (2010), then OK (though you picked a dull topic!)— This is a "report,” a dressed-up set of notes.

9 3) Jones (2004) says the sky is blue. I think this is wrong because it looks kinda grey to me. The end.

10 POOR: A hint of something interesting, but just one person's opinion. Arguments need to be supported by data and/or explicit logic.

11 4) Smith (1902) says the sky is yellow. Jones (2004) says it is blue. The end.

12 BAD: no attempt at resolution of obvious conflict. (Another "report")

13 5) Smith (1902) says the sky is yellow. Jones (2004) says it is blue. Experts disagree, but that can be explained because they were writing 102 years apart. The end.

14 Unconvincing: The author attempts to resolve the conflict, but the resolution looks silly. (The sky was yellow in 1902??)

15 6) Smith (1902) says the sky is yellow. Jones (2004) says it is blue. Doe (1967) describes a neurological disorder affecting people who spend too much time on Black's Beach. This disorder reverses colors so that one perceives "yellow" when looking at blue objects. Since Smith lived in La Jolla (Who's Who in LJ, 1910), she may have been suffering from this disorder. The end.

16 GREAT: Given a paradox, the author found relevant material, and suggested a resolution.

17 So much for anthropology …

18 How about Computational Linguistics? 1)I have an algorithm X for computing C. The end. Assume: C is a worthwhile problem Author explains X well

19 How about Computational Linguistics? 1)I have an algorithm X for computing C. The end. Poor. X may be great, but we don’t know!

20 How about Computational Linguistics? 2) I have an algorithm X for computing C. Its Y score is Z. The end.

21 How about Computational Linguistics? 2) I have an algorithm X for computing C. Its Y score is Z. The end. Poor. How can readers know if Z high or low?

22 How about Computational Linguistics? 3)I have an algorithm X for computing C. Its Y score is Z. Algorithm X’ has a Y score of Z-α. The end. Assume: α is substantial X’ is as computationally efficient as X

23 How about Computational Linguistics? 3) I have an algorithm X for computing C. Its Y score is Z. Algorithm X’ has a Y score of Z-α. The end. Better. Comparison with another algorithm. But is X’ the best algorithm to compare X with? Or is it just a straw man?

24 How about Computational Linguistics? 4)I have an algorithm X for computing C. Its Y score is Z. The standard algorithm X’ (Carroll et al. 2010) has a Y score of Z-α. The end.

25 How about Computational Linguistics? 4)I have an algorithm X for computing C. Its Y score is Z. The standard algorithm X’ (Carroll et al. 2010) has a Y score of Z-α. The end. Getting there! (A standard algorithm makes a good basis for comparison.) This could give you a nice conference paper. – But why does X have a better Y score than X’?

26 How about Computational Linguistics? 5)I have an algorithm X for computing C. Its Y score is Z. The standard algorithm X’ (Carroll et al. 2010) has a Y score of Z-α. This is probably because X takes U into account, since U is known to be relevant for C (Watanabe 1996). The end.

27 How about Computational Linguistics? 5)I have an algorithm X for computing C. Its Y score is Z. The standard algorithm X’ (Carroll et al. 2010) has a Y score of Z-α. This is probably because X takes U into account, since U is known to be relevant for C (Watanabe 1996). The end. Good! – But does the Z score matter?

28 How about Computational Linguistics? 6)I have an algorithm X for computing C. Its Y score is Z. The standard algorithm X’ (Carroll et al. 2010) has a Y score of Z-α. This is probably because X takes U into account, since U is known to be relevant for C (Watanabe 1996). The Z score is known to correlate well with human judgments (Zhao 2010) and human task performance (Yang 2011). (…) The end.

29 How about Computational Linguistics? 6) I have an algorithm X for computing C. Its Y score is Z. The standard algorithm X’ (Carroll et al. 2010) has a Y score of Z-α. This is probably because X takes U into account, since U is known to be relevant for C (Watanabe 1996). The Z score is known to correlate well with human judgments (Zhao 2010) and task performance (Yang 2011). The end. Very good! Potentially, you’ve got yourself the core of journal article. Still, maybe X is a boring variant of X’? Moreover, is there any generality to what you’ve found, or is X only applicable to problem C?

30 How about Computational Linguistics? 7) I have an algorithm X for computing C. Its Y score is Z. The standard algorithm X’ (Carroll et al. 2010) has a Y score of Z-α. This is probably because X takes U into account, since U is known to be relevant for C (Watanabe 1996). The Z score is known to correlate well with human judgments (Zhao 2010) and task performance (Yang 2011). X uses an entirely new method, which I also show to be applicable to an important open problem in physics. (Applications to cures against obesity and hair loss are plausible.) The end. Great! The resulting journal article could change your research area.

31 In practice, most of us are happy if we reach level 6, of course. (And as for obesity and hair loss: don’t believe everything you read.)

32 2. The peer review process Peer review: the main mechanism for deciding whether a result is worth publishing (e.g., as a journal article) (1)Authors submit article (2) Editors select expert reviewers (“peers”) (3) Reviewers assess article (4) Editors decide: accept/reject/revise If revise then authors may go back to (1) Submissions as conference papers lack “revise” option

33 A review form (zoom & scroll) *** REVIEW: --- Please provide a detailed review, including justification for --- your scores. This review will be sent to the authors unless --- the PC chairs decide not to do so. This field is required. ….. (this is where you write a few paragraphs about the strengths and weaknesses of the paper. Suggestions for improvements) … *** REMARKS FOR THE PROGRAMME COMMITTEE: --- If you wish to add any remarks for PC members, please write --- them below. These remarks will only be used during the PC --- meeting. They will not be sent to the authors. This field is --- optional. ….. (quite often you’ll leave this section blank)

34 Review form (ctd.) *** OVERALL EVALUATION: (strong accept) (accept) (weak accept) (borderline paper) (weak reject) (reject) (strong reject) *** REVIEWER'S CONFIDENCE: (expert) (high) (medium) (low) (null) *** APPROPRIATENESS: from 1 (lowest) to 5 (highest) (appropriate (most submissions)) (appropriate for CL/NLP but less so for this event) (possibly relevant, but not quite computational) (only marginally relevant) (inappropriate) *** CLARITY: from 1 (lowest) to 5 (highest) (admirably clear) (understandable by most) (mostly understandable with effort) (important unanswered questions) (mostly confusing)

35 Review form (ctd.) *** ORIGINALITY: from 1 (lowest) to 5 (highest) (surprising: new and noteworthy insights) (creative: relatively few would have put these ideas together) (somewhat conventional: a number of people could have come up with this) (rather boring: a minor improvement on previous work) (not new) *** SOUNDNESS: from 1 (lowest) to 5 (highest) (excellent: approach is apt, claims are supported) (solid: approach or evaluation could be strengthened though) (fair: main claims probably correct) (troublesome: should have been done differently, or justified better) (fatally flawed) *** IMPACT: from 1 (lowest) to 5 (highest) (major: will alter others' research direction or basic approach) (substantial: ideas or results will help others' ongoing research) (somewhat influential: will be cited for comparison or as a minor contribution) (marginal: might be cited) (none) *** END

36 3. Briefly: How about the actual writing? Excerpt from W.Strunk’s “The Elements of Style”: (with help from a web page of the University of Central Kansas)

37 1. Use simple and direct sentences. Usually, the active voice is best. Write "Good writers avoid passive voice," not "The passive voice is avoided by good writers.” (Possible exception: when the subject of the sentence is irrelevant. In some disciplines, most academics prefer “A study was performed” over “We performed a study”. There is evidence, however, that agent-less passives are difficult to interpret.)

38 2. Write positively. For example, write "The rats were always sick" instead of "The rats were never healthy." Use definite and specific sentences. For example, write "it rained every day for a week" instead of "a period of unfavorable growth conditions set in.”

39 3. Delete unnecessary words. For example: the question as to whether…………………….. whether the main aim of this study will be to determine ….. we seek to determine advance notice………………………………….. notice at this point in time……………………………... now be that as it may……………………………..…. but in the event that……………………………….… if general consensus…………………………….. consensus due to the fact that…………………..………….. because chemotherapeutic agent…………….………… drug

40 4. Each paragraph should convey a single major idea and have a topic sentence. The topic sentence should state the main idea of the paragraph. Comment: This is just one technique. Others may work equally well.

41 5. Proofread your report before finalizing it. Have a friend or lab partner read a draft of your writing and suggest improvements.

42 6. Don't plagiarize. Learn to summarize and cite the relevant references.

43 Plagiarism is “taking someone else’s work and passing it off as one’s own” There is a grey area. I got my definition from the Mac’s Dictionary application. Do I have to acknowledge this? If you take someone else’s ideas then (try to) say who had them first If you also take someone else’s words verbatim (for more than just a few words) then put quotes around the text as well – A grey area: “just a few words”

44 Caveat These remarks on writing style are very sketchy. A course might be useful for you. Practice is essential.

45 Summing up Paper writing: there’s more to a good paper than an algorithm and a score Peer review: a cornerstone of the scientific process. An important process to understand. Get involved early, as an author and a reviewer Writing style: look up W.Strunk’s “Elements of Style” on the web.

46 The End


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