Academic journal article International Journal of Linguistics

Semantic Classes and Functions of Lexical Presupposition Triggers: An Experimental Investigation of Chatters' Use

Academic journal article International Journal of Linguistics

Semantic Classes and Functions of Lexical Presupposition Triggers: An Experimental Investigation of Chatters' Use

Article excerpt


Prior research suggested the possibility of establishing systematic linkages between some intrinsic features of a presupposition and textual and pragmatic functions that it can carry out with greater probability. This study aims, firstly, to provide an organic view of semantics of presupposition triggers, thanks to a lexical database comprising 19,500 entries. Secondly, the database was used to investigate a corpus of chat conversations including about 200,000 tokens. The results show that triggers occur mainly as non-informative, maintaining an information already known by all participants of the communication; but, depending on their different features, some of them are systematically associated to a function of anaphora and textual cohesion; others to strengthen social conventions and stereotypes. The informative function, although in a minority proportion, is quantitatively significant only in correspondence to a single class of presupposition triggers.

Keywords: Presupposition trigger, Informativeness, Anaphora, Chat

1. Introduction

This article presents the results of a study on presupposition in the Italian language, based on a database of about 19,500 lexical presupposition triggers (henceforth: "DB") an d on a corpus of computer-mediated communication, specifically formed by chat conversations (henceforth: "the Corpus") including about 200,000 tokens. The research had these phases:

* we developed the DB;

* using the DB, we built semantic classes of triggers based on the features of the presupposed content;

* we used the DB as an automatic tool for text mining, searching all occurrences of lexical presupposition triggers in the Corpus:

* we marked their textual and pragmatic functions within the chat conversations;

* we crossed the previous results, building a multi-level annotation of the semantic and pragmatic phenomena to which the trigger occurrences are associated, finding some profiles that appear in a quantitatively significant proportion.

In the last phases, our attention has been addressed also to the informative uses of presupposition, namely those in which a writer transmits a content not unanimously accepted as true by the audience. The importance of these uses has been highlighted by many authors, recently by Bonyadi, Samuel (2011) and Zare, Abbaspour, Rajaee (2012), who investigated the issue in newspaper editorials, that is, in an unidirectional context; in this case, the aim was to anchor the analysis in a strongly dialogic context that enabled us to see the readers' immediate reactions.

In the following, results of each phase are detailed.

2. The Database of Italian Presupposition Triggers (DB)

To setup the DB we started from the English existing lists of presupposition triggers: we can mention at least Sellars (1954), Karttunen (1971), Karttunen (1973), Kiparsky, Kiparsky (1971), Sebba (1987), Konig (1991), Levin (1993), Pi (1995), Tomasello (1992), Landau (2000), Bauerle, Reyle, Zimmermann (2010), Jaszczolt, Turner (2003). Then we translated them into Italian and implemented the list through synonyms, opposites and certain types of derivatives and compounds, using De Mauro (1999) and De Mauro (2010). All collected types are reported in Table 1.

Considering that one of the largest Italian dictionaries, De Mauro (1999), reported about 150,000 non-technical headwords, our DB represents a significant portion, 13%. This is a first quantitative measure of the impact of presuppositional triggers on the Italian lexicon.

The distribution is highly concentrated on change-of-state and iterative items, but these categories include many rare words; in contrast, other groups such as focals, which are very small in absolute numbers, include words with a very high frequency in texts (for example: Italian anche, English also or too). 80% of the DB consists of words of basic and common vocabulary, with a high potential impact on the analysis of samples of language; the remaining 20% consists of rare or regional words. …

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