On Politics and Argumentation

Transcription

1 On Politics and Argumentation Maria Boritchev To cite this version: Maria Boritchev. On Politics and Argumentation. MALOTEC, Mar 2017, Nancy, France <hal > HAL Id: hal Submitted on 18 Dec 2017 HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

2 on politics and argumentation Maria Boritchev March 15th, 2017 University of Lorraine

3 about the field Argument Mining Aim: identify argument structures that can be found within the discourse Argumentative schemes Relationships between pairs of arguments Introducing automation 2000 Applications: Stock market Computerized essay grading Computer-supported peer review 1 / 42

4 arg-tech 2 / 42

5

6 question How to automatically mine intertextual correspondences between U.S. candidates argumentation during primaries debates and Reddit users comments on those? 4 / 42

7 outline Some theory Some politics A spoonful of reality Politics, people and argumentation What now? 5 / 42

8 some theory

9 an introductory example Example (A simple dialogue) Alice: Q. Bob: Why Q? Alice: P. Modelisation (A simple dialogue) P (P Q) Q 7 / 42

10 about the method Inference Anchoring Theory Aim: link dialogical and argumentative structures Combination of logic and argumentation theories Reference: Budzynska and Reed, 2011 Contribution to the Argument Web Mediation project Ethos mining 8 / 42

11 a concrete example Example (Reddit comments) Redditor1: Every American should be a capitalist. Redditor2: Why? Redditor1: Our country was built on capitalism. 9 / 42

12 iat diagram (loading...) 10 / 42

13 iat diagram (loading...) 11 / 42

14 iat diagram (loading...) 12 / 42

15 iat diagram (loading...) 13 / 42

16 iat diagram (loading...) 14 / 42

17 iat diagram (loaded!) 15 / 42

18 some politics

19 about the corpora U.S. presidential debates The American Presidency Project Web archive: American presidency related documents Transcripts of all presidential debates from 1960 to elections: 9 Democratic debates 12 Republican debates 3 General debates 17 / 42

20 about the corpora Reddit Social media and news aggregation, web content rating and discussion English-speaking website subreddits: news, science, politics, gaming, movies,... In /r/politics, October 13 DNC Primary Debate - During-debate Discussion Megathread 18 / 42

21 assembling the reddit comments corpus 1. For each debate, select the corresponding thread. 2. Sort the comments by time-stamp (oldest on top). 3. Remove all comments having no children. 4. Remove all comments trees beginning with irony or wordplay (rethoric structures are not handled by IAT). 5. Keep comments trees classified by excerpts (time-stamp identification), discard all others. 19 / 42

22 a spoonful of reality

23 model 21 / 42

24 iat in practice 22 / 42

25 high dialogical activity 23 / 42

26 excerpt Example CHAFEE: Anderson, you re looking at a block of granite when it comes to the issues. Whether it s...[crosstalk] COOPER: It seems like pretty soft granite. I mean, you ve been a Republican, you ve been an independent. 24 / 42

27 hda moments extraction 25 / 42

28 politics, people and argumentation

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30 correspondence mining 28 / 42

31 stating the intertextual correspondence task Input R = {r 1,..., r i,..., r n } set of Reddit comments segments. D = {d 1,..., d j,..., d m } set of presidential debates segments. Output C = (c i,j ), matrix of the correspondence coefficients between r i and d j. 29 / 42

32 sketching the algorithm Natural Language Processing tools: Speakers similarity Frequent wordsets similarity Semantic similarity w 2 c i,j = w 1 Sp(r i, d j ) + length(r i ) tf-idf(u, d j, D) u r i w 3 + length(r i ) length(d j ) Semsim(u, v) u r i,v d j 30 / 42

33 sketching the algorithm Natural Language Processing tools: Speakers similarity Frequent wordsets similarity Semantic similarity w 2 c i,j = w 1 Sp(r i, d j ) + length(r i ) tf-idf(u, d j, D) u r i w 3 + length(r i ) length(d j ) Semsim(u, v) u r i,v d j 31 / 42

34 speakers similarity Example r i = Hillary Clinton knows Bernie Sanders gun control record isn t his strong suit d j = CLINTON thinks what Senator SANDERS is saying certainly makes sense in the terms of the inequality that we have S(t) = {CLINTON, SANDERS} 32 / 42

35 sketching the algorithm Natural Language Processing tools: Speakers similarity Frequent wordsets similarity Semantic similarity w 2 c i,j = w 1 Sp(r i, d j ) + length(r i ) tf-idf(u, d j, D) u r i w 3 + length(r i ) length(d j ) Semsim(u, v) u r i,v d j 33 / 42

36 tf-idf Definition (Term frequency) Let w be a word of a (non-empty) segment t in a corpus C. Term frequency of w in t is defined as Tf(w, t) = {v t, v = w}. {v t} Definition (Inverse document frequency) Inverse document frequency of w in C is defined as 0 if w / t, Idf(w, C) = s, s C log otherwise. s C, w s 34 / 42

37 tf-idf Definition (Term frequency inverse document frequency) Let w be a word of a segment t in a corpus C. tf-idf of w in t in C is defined as tf-idf(w, t, C) = Tf(w, t) Idf(w, C). 35 / 42

38 sketching the algorithm Natural Language Processing tools: Speakers similarity Frequent wordsets similarity Semantic similarity w 2 c i,j = w 1 Sp(r i, d j ) + length(r i ) tf-idf(u, d j, D) u r i w 3 + length(r i ) length(d j ) Semsim(u, v) u r i,v d j 36 / 42

39 wordnet hierarchy (part.) 37 / 42

40 semantic similarity Definition Semantic similarity of w 1 and w 2 is defined as Semsim(w 1, w 2 ) = 1 min i,j {length(path(w 1 #i, w 2 #j))} max v,k {length(path(entity, v#k))}. 38 / 42

41 sketching the algorithm Natural Language Processing tools: Speakers similarity Frequent wordsets similarity Semantic similarity w 2 c i,j = w 1 Sp(r i, d j ) + length(r i ) tf-idf(u, d j, D) u r i w 3 + length(r i ) length(d j ) Semsim(u, v) u r i,v d j 39 / 42

42 what now?

43 conclusion and perspectives Stating the intertextual correspondence task Developing the corpora Sketching the intertextual correspondence algorithm Future work Other corpora? Other methods? 41 / 42

44 Thank you for your attention 42 / 42