


































Studia grazie alle numerose risorse presenti su Docsity
Guadagna punti aiutando altri studenti oppure acquistali con un piano Premium
Prepara i tuoi esami
Studia grazie alle numerose risorse presenti su Docsity
Prepara i tuoi esami con i documenti condivisi da studenti come te su Docsity
Trova i documenti specifici per gli esami della tua università
Preparati con lezioni e prove svolte basate sui programmi universitari!
Rispondi a reali domande d’esame e scopri la tua preparazione
Riassumi i tuoi documenti, fagli domande, convertili in quiz e mappe concettuali
Studia con prove svolte, tesine e consigli utili
Togliti ogni dubbio leggendo le risposte alle domande fatte da altri studenti come te
Esplora i documenti più scaricati per gli argomenti di studio più popolari
Ottieni i punti per scaricare
Guadagna punti aiutando altri studenti oppure acquistali con un piano Premium
A comprehensive overview of discourse analysis, exploring its key concepts, methodologies, and applications. It delves into the relationship between language and identity, politeness strategies, and the role of context in shaping meaning. The document also examines corpus linguistics and its use in analyzing large datasets of language, highlighting the importance of studying language in its natural environment. It further discusses the functions of language, register variation, and the impact of the internet on language use.
Tipologia: Schemi e mappe concettuali
1 / 42
Questa pagina non è visibile nell’anteprima
Non perderti parti importanti!



































Discourse analysis is an approach to the analysis of language that examines:
Other contributions to discourse analysis were provided by Firth, Halliday and Sinclair. *****
The presupposition is the common ground that is assumed to exist between the language users (shared knowledge about the language and about the world.) It determines the way of speaking, in that a speaker say something based on his / her assumption of what the listener is likely to know and of what the listener will infer. As a result, the speaker does not need to give all the information. We can distinguish between two kinds of presupposition:
There is a difference in grammar depending on whether we look at it from a sentence-based perspective or from a text-based / a discourse-based perspective.
The thematic progression refers to the way in which the theme of a clause may pick up a meaning from a preceding theme or rheme / how information flow is created in a text.
Cohesion is a network of grammatical, lexical and other relations that connects together the parts of a text. The cohesive devises are:
Both range of distribution and dispersion involve dividing the corpus into segments. Moreover, a calculation of log-likelihood is used to identify the differences between the text type frequencies. A type may, in fact, occur more often in and are more distinctive of conversational speech, task-oriented, imaginative writing or informative writing. KEYWORDS The term “keywords” refers to words (mainly lexical words, in that grammatical words appear at the top of any frequency list) that are important because they occur more frequently in a corpus. They therefore indicate what the corpus is about. Keywords are often grouped in semantic categories, based on their meaning. And they can be tracked (tracciare) to identify where they occur in the corpus. They can, in fact, not be distributed / concentrated evenly in the corpus, but only in some parts of it. In keyword studies, a specialized corpus is compared with a much larger and general corpus. There are three alternatives:
Multidimensional analysis is an approach to the study of variation between types of text. It is in fact used to compare several sub-corpora, each one representative of a text type. Once the sub-corpora have been tagged or annotated and the frequency of each linguistic feature in each sub-corpus has been calculated, the analyses focuses on which language features are most likely to appear together in a sub- corpus and which ones are most unlikely to appear together in a sub-corpus. This shows which language features attract each other and which ones repel each other. It differs from approaches such as keywords or lexical bundles in three ways:
The computer-aided translation (CAT) refers to the use of a computer software to assist to various degree a human translator in the translation process. The translation is therefore carried out principally by a person, but it also involves the use of a computer software that facilitates some aspects of it. This contrasts with Machine Translation (MT), which refers to a translation that is carried out principally by a computer software, but that may also involve some human intervention (pre- or post-editing.) CAT TOOLS CAT tools are computer software designed specifically with the translation task. The most popular CAT tool is the Translation Environment Tool (TEnT), which is an integrated suite of tools. It is constructed around a translation memory (TM), which often functions in association with a terminology management system. TRANSLATION MEMORY TOOLS A TM is a tool that allows users to store previously translated text and then consult them for reuse. To do this, the source and the target texts are stored in the TM database as bitexts and an aligned bitext is created by dividing the texts into segments and by linking each segment to its corresponding segment in the translation. When a translator has a new text to translate, the TM divides it into segments and compares each segment with the contents of the TM database, in order to identify whether any portion of the new text has been previously translated as part of a text stored in it. The matches can be accepted, modified or rejected by the translator. TERMINOLOGY TOOLS A terminology management system (TMS) is a tool used to store terminological information in and to retrieve it from a termbase. Translators can customize the term records with various fields (term, equivalent, definition, context, source…) Termbases can also be integrated with TM databases. By doing so, TMS can scan a new text, compare its contents against a specified termbase and identify the matches between them. OTHER TEnT COMPONENTS
The TM’s benefits are an increased productivity and an improved quality. Once matches are found, being able to automatically copy an paste items from the TM database or termbase into the target text saves translators typing time and reduces typographic errors. Nevertheless, the segment-by-segment processing approach underlying the TM means that the notion of text may be lost. Moreover, CAT tools affect the translators’ professional status and intellectual property rights. Some clients may, in fact, attribute less value to the work of translators who use them, in that it is easier and faster than human translation, or may be more demanding (esigente) if they use their own TM to pre-translate a text before sending it to the translator. Yet, even exact matches do not equate to zero time spent for the translator. He / she has in fact to evaluate the suggested sentences and make adjustments. Finally, legal questions surround the ownership and the sharing of CAT data. Translators may wish to exchange or to sell a TM, but its source texts and translations are ownership of the client and he / she may demand confidentiality. POST-EDITING The term “post-editing” defines the activity of revising a text that has been translated automatically by a Machine Translation (MT.) To do so, the editor compares the source text with the raw (grezzo) translation produced by the Machine Translation and identifies and fixes the errors. At the time, the most common process involves sending the text to the Machine Translation and then editing it post Machine Translation, hence the affix “post” in “post-editing.” We can distinguish between:
Translation and interpretation have played a central role in human interaction for thousands of years. But they have limits. In fact, the more a community is linguistically mixed, the less it can rely on individuals to ensure the communication between the different groups. In communities where only few languages are in contact, bilingualism can be a solution (children can acquire more than one language.) But in communities where many languages are in contact, such solution can not be applied. Such problem has traditionally been solved by finding a language to act as a lingua franca (common language.) The different communities can, in fact, adopt: