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Kim Ebensgaard Jensen Aalborg University Constructions in Wonderland: Exploring the functionality of constructions through N-grams Functionality of Language.

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Presentation on theme: "Kim Ebensgaard Jensen Aalborg University Constructions in Wonderland: Exploring the functionality of constructions through N-grams Functionality of Language."— Presentation transcript:

1 Kim Ebensgaard Jensen Aalborg University Constructions in Wonderland: Exploring the functionality of constructions through N-grams Functionality of Language | Sprogets Funktionalitet Aalborg Unviersity 10 December, 2014 Yoshikata Shibuya Kyoto University of Foreign Studies

2 Kim Ebensgaard Jensen Aalborg University Introduction Is there a way to (semi-)automatically identify constructions in texts, discourses, and corpora? Could N-gram analysis and N-gram based network analysis be ways to do that? How can (semi-)automatic identification of constructions help us learn about the functional contributions of constructions in discourse? To this end, we will analyze two literary classics and four political speeches. Yoshikata Shibuya Kyoto University of Foreign Studies

3 Kim Ebensgaard Jensen Aalborg University Introduction Outline Theoretical framework Positioning of paper (Very) basic principles of construction grammar Functionality of constructions Method Data collection and methods Wordclouds N-grams Network analysis Analyses Word clouds N-grams Networks Yoshikata Shibuya Kyoto University of Foreign Studies

4 Kim Ebensgaard Jensen Aalborg University Theoretical grounding Yoshikata Shibuya Kyoto University of Foreign Studies

5 Kim Ebensgaard Jensen Aalborg University Theoretical and methodological positioning of this paper Theoretical positioning Construction grammar (e.g. Goldberg 1995; Croft 2001) Cognitive poetics/stylistics (e.g. Stockwell 2002) Cognitive discourse analysis (e.g. Hart 2013) Methodological positioning Corpus stylistics (e.g. Semino & Short 2004) Corpus-aided discourse studies (e.g. Baker 2012) Text-mining (e.g. Miner et al. 2012) Yoshikata Shibuya Kyoto University of Foreign Studies

6 Kim Ebensgaard Jensen Aalborg University Constructions In construction grammar, a construction is a functional unit that pairs form and semantic and/or discourse-pragmatic function (Goldberg 1995, 2006; Croft 2001, 2005; Hilpert 2014). Examples from English - [form]/[function] (cf. Langacker 1987): [S V IO DO]/[ TRANSFER OF POSSESSION ] (Goldberg 1995) [X BE so Y that Z]/[ SCALAR CAUSATION ] (Bergen & Binsted 2004) [you don't want me to V]/[ THREATENING SPEECH ACT ] (Martínez 2013) [to begin with]/[ INTRODUCTION OF LIST OF ITEMS ] (Lipka & Schmid 1994) [PRO acc CL inf (or NP)]/[ DISBELIEF TOWARDS PROPOSITION ] (Lambrecht 1990) 'What, me worry?!', 'Him a doctor?!' etc. Yoshikata Shibuya Kyoto University of Foreign Studies

7 Kim Ebensgaard Jensen Aalborg University Constructions Constructions may be schematic, substantive (fixed), or something in- between (Fillmore et al. 1988). Constructions may be atomic/simple, complex, or something in- between and form a lexicon-syntax continuum (e.g. Goldberg 1995, Croft 2001). Language competence is an inventory of constructions (aka. the construct-i-con) of varying degrees of abstraction which are instantiated in language use (e.g. Goldberg 1995). In most contemporary incarnations of construction grammar, the construct-i-con is usage-based (e.g. Croft 2001). Constructions are subject to general human cognitive processes and principles, such that language is not a separate, autonomous cognitive faculty; thus, construction grammar is part of the overall endeavor of cognitive linguistics. Yoshikata Shibuya Kyoto University of Foreign Studies

8 Kim Ebensgaard Jensen Aalborg University The discursive and stylistic functionality of constructions If constructions are functional units (pairings of form and meaning/function), then they logically must contribute to discourse as part of a speaker's linguistic repertoire. Here are two examples: Writers of fiction may use constructions in... descriptions of actions and happenings; characterizations (Culpeper 2009) and mind styles (Fowler 1977) by having characters use certain constructions in their dialog and narrative, or by using certain constructions in the descriptions of characters or of their actions; in setting up the text-world and specifying temporal relations in the narrative; foregrounding, deviation, parallelism etc. (e.g. Short & Leech 2007) In political speeches, speakers may use constructions in... framing of issues and other ideologically based representations; organization of topics/issues; rhetorical strategies (including parallelisms etc.). Yoshikata Shibuya Kyoto University of Foreign Studies

9 Kim Ebensgaard Jensen Aalborg University Method Yoshikata Shibuya Kyoto University of Foreign Studies

10 Kim Ebensgaard Jensen Aalborg University Data collection and methods Text data Alice's Adventures in Wonderland by Lewis Carroll (1865), obtained via Gutenberg Project = AW Adventures of Huckleberry Finn by Mark Twain (1884), obtained via Gutenberg Project = HF Inaugural speeches by US Presidents, obtained via Bartleby Text mining and corpus-linguistic methods Wordclouds (R package ‘wordcloud’) N-grams (R package ‘tau’, AntConc) Network analysis (R package ‘igraph’) Concordances (AntConc) Yoshikata Shibuya Kyoto University of Foreign Studies

11 Kim Ebensgaard Jensen Aalborg University Wordclouds Wordclouds are graphical representations of the lexical texture of a text, based on frequencies. They are visual versions of frequency lists.... except they do not provide any information on frequencies. Yoshikata Shibuya Kyoto University of Foreign Studies

12 Kim Ebensgaard Jensen Aalborg University N-grams An N-gram is a string of words that co-occur frequently in a data set (such as a corpus or a text). N-grams are specified in accordance with the number of words in the string in question (N = number) Monogram (1-gram) = one word, bigram (2-gram) = two words, trigram (3-gram) = three words, four-gram (4-gram) = four words, five-gram (5- gram) = five words etc. Examples = 'damn you' (2-gram), 'what the hell' (3-gram), 'pick up the phone' (4-gram) (e.g. Stubbs 2009) Yoshikata Shibuya Kyoto University of Foreign Studies

13 Kim Ebensgaard Jensen Aalborg University N-grams N-gram retrieval is a text mining technique used in the identification of N-grams in a data set, text, or corpus. How it works (in a nutshell): ask a computer to find strings of N words, and it returns a list of N-grams ranked in terms of frequency. An example: 4-grams in all of Shakespeare's plays Find all instances of word + word + word + word combinations in the collective body of Shakespeare's plays. Calculate frequency of word + word + word + word combinations in the collective body of shakespeare's plays. List the word + word + word + word combinations in terms of frequency in the collective body of shakespeare's plays. Yoshikata Shibuya Kyoto University of Foreign Studies

14 Kim Ebensgaard Jensen Aalborg University N-grams N-gram retrieval is a text mining technique used in the identification of N-grams in a data set, text, or corpus. How it works (in a nutshell): ask a computer to find strings of N words, and it returns a list of N-grams ranked in terms of frequency. An example: 4-grams in all of Shakespeare's plays Find all instances of word + word + word + word combinations in the collective body of Shakespeare's plays. Calculate frequency of word + word + word + word combinations in the collective body of shakespeare's plays. List the word + word + word + word combinations in terms of frequency in the collective body of shakespeare's plays. Yoshikata Shibuya Kyoto University of Foreign Studies

15 Kim Ebensgaard Jensen Aalborg University N-grams N-gram analysis can be used for a wide range of things: Identification of "aboutness" of a text or discourse. Phraseological analysis. Probablistic language modeling. Analysis of aspects of style, genre and register. Identification of various types of anonymous writers. Identification of constructions and their functionality in a text or discourse. Etc. Yoshikata Shibuya Kyoto University of Foreign Studies

16 Kim Ebensgaard Jensen Aalborg University Network analysis Network analysis sets up data points as nodes in a network and calculates the strengths of association between them. As a text mining method, it represents word types (as opposed to tokens) in a text as nodes and, based on N-gram relations, sets up relations between the nodes, or words. Yoshikata Shibuya Kyoto University of Foreign Studies

17 Kim Ebensgaard Jensen Aalborg University Analysis Yoshikata Shibuya Kyoto University of Foreign Studies

18 Kim Ebensgaard Jensen Aalborg University Wordclouds: a first look Yoshikata Shibuya Kyoto University of Foreign Studies

19 Kim Ebensgaard Jensen Aalborg University Wordclouds Wordcloud AWWordcloud HF Yoshikata Shibuya Kyoto University of Foreign Studies

20 Kim Ebensgaard Jensen Aalborg University Wordclouds Wordcloud AWWordcloud HF Yoshikata Shibuya Kyoto University of Foreign Studies

21 Kim Ebensgaard Jensen Aalborg University N-grams in AW and HF Yoshikata Shibuya Kyoto University of Foreign Studies

22 Kim Ebensgaard Jensen Aalborg University N-grams in AW Yoshikata Shibuya Kyoto University of Foreign Studies 2-grams 3-grams4-grams

23 Kim Ebensgaard Jensen Aalborg University N-grams in AW Yoshikata Shibuya Kyoto University of Foreign Studies 2-grams 3-grams4-grams

24 Kim Ebensgaard Jensen Aalborg University N-grams in AW Yoshikata Shibuya Kyoto University of Foreign Studies 2-grams 3-grams4-grams

25 Kim Ebensgaard Jensen Aalborg University N-grams in AW Yoshikata Shibuya Kyoto University of Foreign Studies 2-grams 3-grams4-grams

26 Kim Ebensgaard Jensen Aalborg University N-grams in AW Yoshikata Shibuya Kyoto University of Foreign Studies 2-grams 3-grams4-grams

27 Kim Ebensgaard Jensen Aalborg University N-grams in AW Yoshikata Shibuya Kyoto University of Foreign Studies 2-grams 3-grams4-grams speech / dialog

28 Kim Ebensgaard Jensen Aalborg University N-grams in AW Yoshikata Shibuya Kyoto University of Foreign Studies 2-grams 3-grams4-grams speech / dialog definite NP

29 Kim Ebensgaard Jensen Aalborg University N-grams in AW Yoshikata Shibuya Kyoto University of Foreign Studies 2-grams 3-grams4-grams speech / dialog definite NP 'said'

30 Kim Ebensgaard Jensen Aalborg University N-grams in AW Yoshikata Shibuya Kyoto University of Foreign Studies 2-grams 3-grams4-grams speech / dialog definite NP 'said' [DIALOG said NP def/unique ]/[ TOPICALIZATION OF DIALOG ] [DIALOG said NP def/unique ]/[ TOPICALIZATION OF DIALOG ]

31 Kim Ebensgaard Jensen Aalborg University N-grams in HF Yoshikata Shibuya Kyoto University of Foreign Studies 3-grams 4-grams 5-grams 2-grams

32 Kim Ebensgaard Jensen Aalborg University N-grams in HF Yoshikata Shibuya Kyoto University of Foreign Studies 3-grams 4-grams 5-grams 2-grams

33 Kim Ebensgaard Jensen Aalborg University N-grams in HF Yoshikata Shibuya Kyoto University of Foreign Studies 3-grams 4-grams 5-grams 2-grams

34 Kim Ebensgaard Jensen Aalborg University N-grams in HF Yoshikata Shibuya Kyoto University of Foreign Studies 3-grams 4-grams 5-grams 2-grams

35 Kim Ebensgaard Jensen Aalborg University N-grams in HF Yoshikata Shibuya Kyoto University of Foreign Studies 3-grams 4-grams 5-grams 2-grams

36 Kim Ebensgaard Jensen Aalborg University N-grams in HF Yoshikata Shibuya Kyoto University of Foreign Studies 3-grams 4-grams 5-grams 2-grams

37 Kim Ebensgaard Jensen Aalborg University N-grams in HF Yoshikata Shibuya Kyoto University of Foreign Studies 3-grams 4-grams 5-grams 2-grams productive

38 Kim Ebensgaard Jensen Aalborg University N-grams in HF Yoshikata Shibuya Kyoto University of Foreign Studies 3-grams 4-grams 5-grams 2-grams productive less productive

39 Kim Ebensgaard Jensen Aalborg University N-grams in HF Yoshikata Shibuya Kyoto University of Foreign Studies 3-grams 4-grams 5-grams 2-grams productive less productive Two different constructions [it BEn't no X] [there BEn't no X] Two different constructions [it BEn't no X] [there BEn't no X]

40 Kim Ebensgaard Jensen Aalborg University N-grams in HF Yoshikata Shibuya Kyoto University of Foreign Studies 3-grams 4-grams 5-grams 2-grams

41 Kim Ebensgaard Jensen Aalborg University N-grams in HF Yoshikata Shibuya Kyoto University of Foreign Studies 3-grams 4-grams 5-grams 2-grams

42 Kim Ebensgaard Jensen Aalborg University N-grams in HF Yoshikata Shibuya Kyoto University of Foreign Studies 3-grams 4-grams 5-grams 2-grams

43 Kim Ebensgaard Jensen Aalborg University N-grams in HF Yoshikata Shibuya Kyoto University of Foreign Studies 3-grams 4-grams 5-grams 2-grams Event relating constructions used to temporally structure events in the narrative: [X and then Y]/[ EVENT 1 FOLLOWED BY EVENT 2 ] [by and by X]/[ EVENT HAPPENING AFTER SOME TIME ] Event relating constructions used to temporally structure events in the narrative: [X and then Y]/[ EVENT 1 FOLLOWED BY EVENT 2 ] [by and by X]/[ EVENT HAPPENING AFTER SOME TIME ]

44 Kim Ebensgaard Jensen Aalborg University N-gram analysis: pros and cons 1 Pro: Simple N-gram analysis can help us identify and address constructions and their functionality in one text or discourse, as it identifies frequent combinations of words in the text or discourse in question. Con: Problem #1: What simple N-gram analysis does not tell us is whether or not those frequent combination of words can also be found in other text. Our N-gram analyses of AW and HF do not tell us if the findings are actually just general patterns in English or if they are actually good delineations of the texts. Solution to problem #1: To obtain a list of N-grams that really delineate a given text (so that we can identify what constructions are characteristically associated with the text), a comparative analysis can be useful. Yoshikata Shibuya Kyoto University of Foreign Studies

45 Kim Ebensgaard Jensen Aalborg University N-grams: AW vs. HF Note that here we focus on 2-grams Procedures 1.2-gram analysis of AW and HF 2.Normalization of frequencies for AW and HF: per 10,000 words 3.Comparison between 2-grams of AW and those of HF with Fisher’s exact test Yoshikata Shibuya Kyoto University of Foreign Studies

46 Kim Ebensgaard Jensen Aalborg University N-grams: AW vs. HF Yoshikata Shibuya Kyoto University of Foreign Studies

47 Kim Ebensgaard Jensen Aalborg University N-grams: AW vs. HF Yoshikata Shibuya Kyoto University of Foreign Studies

48 Kim Ebensgaard Jensen Aalborg University Other texts: a quick look at inaugural speeches Yoshikata Shibuya Kyoto University of Foreign Studies

49 Kim Ebensgaard Jensen Aalborg University What about other texts? Political texts: inaugural speeches by US Presidents Procedures 1.2-gram analysis of 4 inaugural speeches by George Bush (GB), Bill Clinton (BC), George W. Bush (GWB), and Barack Obama (BO). 2.Normalization: per 1,000 words 3.Fisher’s exact test Yoshikata Shibuya Kyoto University of Foreign Studies

50 Kim Ebensgaard Jensen Aalborg University Results Yoshikata Shibuya Kyoto University of Foreign Studies

51 Kim Ebensgaard Jensen Aalborg University Results Yoshikata Shibuya Kyoto University of Foreign Studies

52 Kim Ebensgaard Jensen Aalborg University N-gram analysis: pros and cons 2 Pros N-grams can help us identify and address constructions and their functionality in one text or discourse Comparative N-gram analysis can help us find N-grams that delineate texts or discourses So far, we have found 2-grams that delineate AW and HF, and also some political speeches. Cons: Problem #2: Shorter N-grams are embedded in longer N-grams (2-grams can be found inside some of the 3-grams and 4-grams). Consequently, our N-gram lists contain some redundancy. Our comparative N-gram analyses focused on 2-grams, but texts contain longer strings of words (3-grams, 4-grams, etc) and also shorter N-grams (1-grams). From the perspective of construction grammar, we should also talk about those longer/shorter N-grams, like we did in our isolated N-gram analyses. Yoshikata Shibuya Kyoto University of Foreign Studies

53 Kim Ebensgaard Jensen Aalborg University N-gram analysis: pros and cons 2 Solution to problem #2 We can use the methods we have used 2-grams with other N- grams, but is there any simpler way to find both short and long N-grams? That is, can we find 1-grams, 2-grams, 3-grams, 4-grams, etc. all at one time? Is there any way to provide descriptively a more efficient analysis on frequently co-occurring combinations of words (constructions)? Our suggestion here is to use network analysis based on 2- grams. Other N-grams emerge in the network, as a network includes all N-grams. Yoshikata Shibuya Kyoto University of Foreign Studies

54 Kim Ebensgaard Jensen Aalborg University Network analysis of AW and HF Yoshikata Shibuya Kyoto University of Foreign Studies

55 Kim Ebensgaard Jensen Aalborg University Network analysis of AW Yoshikata Shibuya Kyoto University of Foreign Studies Top 96 2-grams

56 Kim Ebensgaard Jensen Aalborg University Network analysis of AW A closer look at some N-grams Yoshikata Shibuya Kyoto University of Foreign Studies

57 Kim Ebensgaard Jensen Aalborg University Network analysis of AW A closer look at some N-grams: 'said the march hare' (4- gram) Yoshikata Shibuya Kyoto University of Foreign Studies

58 Kim Ebensgaard Jensen Aalborg University Network analysis of AW A closer look at some N-grams: 'said the march hare' (4- gram) 'said the mock turtle' (4- gram) Yoshikata Shibuya Kyoto University of Foreign Studies

59 Kim Ebensgaard Jensen Aalborg University Network analysis of AW A closer look at some N-grams: 'said the march hare' (4- gram) 'said the mock turtle' (4- gram) 'said the king' (3-gram) Yoshikata Shibuya Kyoto University of Foreign Studies

60 Kim Ebensgaard Jensen Aalborg University Network analysis of AW A closer look at some N-grams: 'said the march hare' (4- gram) 'said the mock turtle' (4- gram) 'said the king' (3-gram) 'said the caterpillar' (3- gram) Yoshikata Shibuya Kyoto University of Foreign Studies

61 Kim Ebensgaard Jensen Aalborg University Network analysis of AW A closer look at some N-grams: 'said the march hare' (4- gram) 'said the mock turtle' (4- gram) 'said the king' (3-gram) 'said the caterpillar' (3- gram) 'said the cat' (3-gram) Yoshikata Shibuya Kyoto University of Foreign Studies

62 Kim Ebensgaard Jensen Aalborg University Network analysis of AW A closer look at some N-grams: 'said the march hare' (4- gram) 'said the mock turtle' (4- gram) 'said the king' (3-gram) 'said the caterpillar' (3- gram) 'said the cat' (3-gram) Yoshikata Shibuya Kyoto University of Foreign Studies

63 Kim Ebensgaard Jensen Aalborg University Network analysis of AW A closer look at some N-grams: 'said the march hare' (4- gram) 'said the mock turtle' (4- gram) 'said the king' (3-gram) 'said the caterpillar' (3- gram) 'said the cat' (3-gram) 'said alice' (2-gram) Yoshikata Shibuya Kyoto University of Foreign Studies

64 Kim Ebensgaard Jensen Aalborg University Network analysis of AW A closer look at some N-grams: 'said the march hare' (4- gram) 'said the mock turtle' (4- gram) 'said the king' (3-gram) 'said the caterpillar' (3- gram) 'said the cat' (3-gram) 'said alice' (2-gram) Yoshikata Shibuya Kyoto University of Foreign Studies [the N]/[ DEFINITE NOMINAL REFERENCE ]

65 Kim Ebensgaard Jensen Aalborg University Network analysis of AW A closer look at some N-grams: 'said the march hare' (4- gram) 'said the mock turtle' (4- gram) 'said the king' (3-gram) 'said the caterpillar' (3- gram) 'said the cat' (3-gram) 'said alice' (2-gram) Yoshikata Shibuya Kyoto University of Foreign Studies [DIALOG said NP def/unique ]/[ TOPICALIZATION OF DIALOG ] [DIALOG said NP def/unique ]/[ TOPICALIZATION OF DIALOG ]

66 Kim Ebensgaard Jensen Aalborg University Network analysis of HF Yoshikata Shibuya Kyoto University of Foreign Studies Top 99 2-grams

67 Kim Ebensgaard Jensen Aalborg University Network analysis of HF Negation constructions Including double negation Yoshikata Shibuya Kyoto University of Foreign Studies

68 Kim Ebensgaard Jensen Aalborg University Network analysis of HF Negation constructions Including double negation Yoshikata Shibuya Kyoto University of Foreign Studies

69 Kim Ebensgaard Jensen Aalborg University Network analysis (HF) Two interesting 2-grams of 'i' Yoshikata Shibuya Kyoto University of Foreign Studies

70 Kim Ebensgaard Jensen Aalborg University Network analysis (HF) Two interesting 2-grams of 'i' 'i reckon' Yoshikata Shibuya Kyoto University of Foreign Studies

71 Kim Ebensgaard Jensen Aalborg University Network analysis (HF) Two interesting 2-grams of 'i' 'i reckon' 'says i' 'i says' Yoshikata Shibuya Kyoto University of Foreign Studies

72 Kim Ebensgaard Jensen Aalborg University Network analysis (HF) Two interesting 2-grams of 'i' 'i reckon' 'says i' 'i says' Yoshikata Shibuya Kyoto University of Foreign Studies

73 Kim Ebensgaard Jensen Aalborg University Network analysis (HF) Two interesting 2-grams of 'i' 'i reckon' 'says i' 'i says' Yoshikata Shibuya Kyoto University of Foreign Studies

74 Kim Ebensgaard Jensen Aalborg University Network analysis (HF) Three N-grams of 'and': Yoshikata Shibuya Kyoto University of Foreign Studies

75 Kim Ebensgaard Jensen Aalborg University Network analysis (HF) Three N-grams of 'and': 'and then' Yoshikata Shibuya Kyoto University of Foreign Studies

76 Kim Ebensgaard Jensen Aalborg University Network analysis (HF) Three N-grams of 'and': 'and then' 'by and by' Yoshikata Shibuya Kyoto University of Foreign Studies

77 Kim Ebensgaard Jensen Aalborg University Network analysis (HF) Three N-grams of 'and': 'and then' 'by and by' 'and so' Yoshikata Shibuya Kyoto University of Foreign Studies

78 Kim Ebensgaard Jensen Aalborg University Network analysis (HF) Three N-grams of 'and': 'and then' 'by and by' 'and so' Yoshikata Shibuya Kyoto University of Foreign Studies

79 Kim Ebensgaard Jensen Aalborg University Network analysis (HF) Three N-grams of 'and': 'and then' 'by and by' 'and so' Yoshikata Shibuya Kyoto University of Foreign Studies [X and so Y]/[ EVENT 1 AMOUNTS TO EVENT 2 ]

80 Kim Ebensgaard Jensen Aalborg University Network analysis At the end of the day, short and long N-grams can be found automatically. A list of N-grams can be used to compute p.values with Fisher’s exact test, for instance. Then, what’s good about using network analysis? Firstly, it allows us to capture several N-gram types simulteneously without too much redundancy. Secondly, the real advantage that network analysis offers is not just its visual effects, but in fact it tells you a lot about the internal functionality of the network. For instance, it is possible to compute the centrality of nodes in a network. This method provides estimates regarding the relationship between a network and the functionality of nodes in it. A simple N-gram analysis does not provide answers to these issues. Yoshikata Shibuya Kyoto University of Foreign Studies

81 Kim Ebensgaard Jensen Aalborg University Centrality Betweenness centrality: Dehmer & Subhash C. Basak (2012: 70-1)... the betweenness centrality is based on the shortest path between nodes.... The betweenness centrality focuses on the number of visits through the shortest paths. If a walker moves from one node to another node via the shortest path, then the nodes with a large number of visits by the walker may have high centrality. Yoshikata Shibuya Kyoto University of Foreign Studies

82 Kim Ebensgaard Jensen Aalborg University Centrality Computing betweenness centrality: Top 15 Yoshikata Shibuya Kyoto University of Foreign Studies

83 Kim Ebensgaard Jensen Aalborg University Concluding remarks Can N-grams be used as means of identifying constructions? Yes, partially. Is network analysis useful in the identification of constructions? Yes, partially. What about functionality? Neither N-grams nor N-gram based networks tell us much about functionality, as they show us purely formal relations. However, they guide us in terms of connections between words that are salient in a given text and may be indicative of constructional as functional units. We can then look at the discursive behavior of such N-grams and extrapolate constructions and their functionality in the text or discourse (and, depending on the corpus, in general). Yoshikata Shibuya Kyoto University of Foreign Studies

84 Kim Ebensgaard Jensen Aalborg University Bibliography Baker, P. (2012). Acceptable bias? Using corpus linguistic methods withcritical discourse analysis. Discourse Studies, 9(3): Bergen, B. & K. Binsted (2004). The cognitive linguistics of scalar humor. In M. Achard & S. Kemmer (eds.). Language, Culture, and Mind. Stanford, CA: CSLI Croft, W. A. (2001). Radical Construction Grammar: Syntactic Theory in Typological Perspective. Oxford: Oxford University Press. Culpeper, J. (2009). In G. Brône & J. Vandaele (eds). Cognitive Poetics: Goals, Gains and Gaps. Berling: Mouton de Gruyter. Dehmer, M. & S. C. Basak (2012). Statistical and Machine Learning Approaches for Network Analysis. Chichester: Wiley-Blackwell. Fillmore, C, P. Kay and M. C. O'Connor (1988). Regularity and idiomaticity in grammatical constructions: The case of let alone. Language, 64: 501–38. Fowler, R. (1977). Linguistics and the Novel. London: Methuen. Hart, C. (2013). Constructing contexts through grammar: Cognitive models and conceptualisation in British newspaper reports on political protests. In J. Flowerdew (ed.), Discourse in Context. London: Bloomsbury Goldberg, A. (1995). Constructions: A Construction Grammar Approach to Argument Structure. Chicago: Chicago University Press. Lambrect, K. (1990). "What, me worry?": Mad Magazine sentences revisited. BLS, 16: Langacker, R. (1987). Foundations of Cognitive Grammar. Vol. 1: Theoretical Prerequisites. Stanford, CA: Stanford University Press. Lipka, L. & H.-J. Schmid (1994). To begin with: Degrees of idiomaticity, textual functions and pragmatic exploitations of a fixed expression. ZAA, 42: Martínez, N. D. C. (2013). Illocutionary Construtions in English: Cognitive Motivation and Linguistic Realization. Bern: Peter Lang. Miner, G., J. Elder, T. Hill, R. Nisbet, D. Delen & A. Fast,. (2012). Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications. Elsevier Academic Press. Semino, E. & M. Short (2004). Corpus Stylistics: Speech, Writing and Thought Presentation in a Corpus of English Writing. Londong: Routledge. Short M. & G. Leech (2007). A Linguistic Introduction to English Fictional Prose (2nd ed.). Harlow: Pearson Longman. Stockwell, P. (2002). Cognitive Poetics. London: Routledge. Stubbs, M. (2009). Technology and phraseology. In U. Römer & R. Schulze (eds.), Exploring the Lexis-Grammar Interface. Amsterdam: John Benjamins Yoshikata Shibuya Kyoto University of Foreign Studies


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