LINGUIST List 33.2179

Sat Jul 02 2022

Calls: Applied Linguistics, Computational Linguistics, Discourse Analysis, Semantics, Syntax, Text/Corpus Linguistics / Information (Jrnl)

Editor for this issue: Sarah Goldfinch <>

Date: 29-Jun-2022
From: Jennifer D'Souza <>
Subject: Applied Linguistics, Computational Linguistics, Discourse Analysis, Semantics, Syntax, Text/Corpus Linguistics / Information (Jrnl)
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Full Title: Information

Linguistic Field(s): Applied Linguistics; Computational Linguistics; Discourse Analysis; Semantics; Syntax; Text/Corpus Linguistics

Subject Language(s): English

Call Deadline: 10-Dec-2022

Call for Papers:

Dear Colleagues and Friends,

We have organized a Special Issue on ''Information Extraction and Language Discourse Processing.'' Below is the relevant background for this SI outlining its scope.

Information extraction (IE) plays an increasingly important and pervasive role in today’s era of digitalized communication media based on the Semantic Web. E.g., search engine results, as snippets, are slowly replaced by “rich snippets”; there is an interest in converting scholarly publications to structured records available in such downstream IT applications as Leaderboards, etc. IE is thus the task of automatically extracting structured information from unstructured and/or semi-structured electronically represented documents. In most cases, this activity concerns processing of human language texts by means of natural language processing (NLP). The automatic extraction of information from unstructured sources has opened up new avenues for querying, organizing, and analyzing data by drawing upon the clean semantics of structured databases and the abundance of unstructured data.

Apart from extrinsic models of IE, research in linguistics and computational linguistics have long pointed out that text is not just simple sequence of clauses and sentences but rather follows a highly elaborated structure formalized within discourse. The framework used for discourse analysis has long since been rhetorical structure theory (RST). Within a well-written text, no unit of the text is completely isolated; interpretation requires understanding the unit’s relation with the context. Research in discourse analysis aims to unmask such relations in the text, which is helpful for many downstream applications such as summarization, information retrieval, and question answering.

This Special Issue seeks novel research reports on the spectrum that blends information extraction and language discourse processing research in diverse communities. The editors welcome submissions along various dimensions derived from the nature of the extraction task, the advanced neural techniques used for extraction, the variety of input resources exploited, and the type of output produced. Quantitative, qualitative, and mixed methods studies are welcome, as are case studies and experience reports if they describe an impactful application at a scale that delivers useful lessons to the journal readership.

Topics of interest include (but are not limited to):

- Knowledge base population with discourse-centric information extraction (IE)
- Coreference resolution and its impact on discourse-centric IE
- Relationship extraction leveraging linguistic discourse
- Template filling
- Impact of pragmatics or rhetorics on information extraction
- Discourse-centric IE at scale
- Intelligent and novel assessment models of discourse-centric IE
- Survey of discourse-centric IE in natural language processing (NLP)
- Challenges implementing discourse-centric IE in real-world scenarios
- Modeling domains using discourse-centric IE
- Human–AI hybrid systems for learning discourse and IE

Please find the online portal for the SI with submission instructions here

Yours cordially,
Dr. Jennifer D'Souza
Prof. Dr. Chengzhi Zhang
Guest Editors

Page Updated: 02-Jul-2022