Linguistic Pragmatic Analysis Task

A Linguistic Pragmatic Analysis Task is a natural language understanding task about the intended meaning, implications, or practical consequences of a statement within its context

Context:
- It can (typically) involve understanding the broader context in which a statement is made.
- It can (often) require making inferences about unstated assumptions or potential implications.
- It can involve applying domain-specific knowledge to interpret the text.
- It can involve recognizing implicature, i.e., what is implied but not explicitly stated.
- It can be used across various domains like law, medicine, and accounting where nuanced interpretation of text is crucial.
- ...
Example(s):
- In a legal context, interpreting a contract clause like "must initiate all repair work swiftly" to understand potential loopholes or practical outcomes.
- In a medical context, interpreting patient instructions where the wording might have implications for patient care.
- In an accounting context, analyzing financial statements where specific phrasings might have significant implications for financial practices.
- Discourse Pragmatics.
- ...
Counter-Example(s):
See: Contextual Understanding, Inference, Domain Knowledge, Implicature, Linguistic Pragmatics Discipline.

[www.codingninjas.com](www.codingninjas.com)
- NOTE: Pragmatic analysis in NLP is the study of practical aspects of human action and thought, or the study of the use of linguistic signs, words, and sentences in actual situations. This approach underlines the significance of pragmatics in all linguistic contact and exchanges, emphasizing the context-dependent nature of language interpretation.

"The Importance of Pragmatic Analysis in NLP." In: [www.techsmartfuture.com](www.techsmartfuture.com)
- NOTE: Pragmatic analysis in NLP involves interpreting the meaning of a text based on its context, including the speaker’s intention, audience, setting, and cultural background. This aspect is crucial in NLP as it helps computers understand the meaning of text beyond its surface level, such as identifying sarcasm, irony, metaphors, and other figurative language.

(Becker et al., 2020) ⇒ Maria Becker, Michael Bender, and Marcus Müller. (2020). “Classifying Heuristic Textual Practices in Academic Discourse: A Deep Learning Approach to Pragmatics.” International Journal of Corpus Linguistics, 25(4). DOI: https://doi.org/10.1075/ijcl.19097.bec
- ABSTRACT: In this paper, we investigate how deep learning techniques can be applied to discourse pragmatics. As a testcase we analyse heuristic textual practices, defined as linguistic implementations of decision routines in research processes in academic discourse. We develop a complex annotation scheme of pragmalinguistic categories on different levels of granularity and manually annotate a corpus of texts across various scientific disciplines. This is the basis for training recurrent neural networks to classify heuristic textual practices. Our experiments show that the annotation categories are robust enough to be recognised by our models which learn similarities of the sentence-surfaces represented as word embeddings. Our study aims at an iterative human-in-the-loop process in which manual-hermeneutic and algorithmic procedures mutually advance the insight process. It underlines the fact that the interaction between manual and automated methods opens up a promising field for further research, allowing interpretative analyses of complex pragmatic phenomena in large corpora.

(Weisser, 2018) ⇒ Martin Weisser. (2018). “How to Do Corpus Pragmatics on Pragmatically Annotated Data.” How to Do Corpus Pragmatics on Pragmatically Annotated Data. https://torrossa.com/en/resources/an/5002531
- QUOTE: ... Corpus- and computer-based methods of analysis have 'revolutionised' much of the research in linguistics or natural language processing over the last few decades. Major advances …

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