scholarly journals Reference without anaphora: on agency through grammar

Linguistics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Chase Wesley Raymond ◽  
Rebecca Clift ◽  
John Heritage

Abstract In this article, we investigate a puzzle for standard accounts of reference in natural language processing, psycholinguistics and pragmatics: occasions where, following an initial reference (e.g., the ice), a subsequent reference is achieved using the same noun phrase (i.e., the ice), as opposed to an anaphoric form (i.e., it). We argue that such non-anaphoric reference can be understood as motivated by a central principle: the expression of agency in interaction. In developing this claim, we draw upon research in what may initially appear a wholly unconnected domain: the marking of epistemic and deontic stance, standardly investigated in linguistics as turn-level grammatical phenomena. Examination of naturally-occurring talk reveals that to analyze such stances solely though the lens of turn-level resources (e.g., modals) is to address only partially the means by which participants make epistemic and deontic claims in everyday discourse. Speakers’ use of referential expressions illustrates a normative dimension of grammar that incorporates both form and position, thereby affording speakers the ability to actively depart from this form-position norm through the use of a repeated NP, a grammatical practice that we show is associated with the expression of epistemic and deontic authority. It is argued that interactants can thus be seen to be agentively mobilizing the resources of grammar to accommodate the inescapable temporality of interaction.

2020 ◽  
Vol 18 (1) ◽  
pp. 74-82
Author(s):  
F. N. Soloviev

In our work we present a description of integration of natural language processing tools (pseudostem extraction, noun phrase extraction, verb government analysis) in order to extend analytic facilities of the TXM corpora analysis platform. The tools introduced in the paper are combined into a single software package providing TXM platform with an effective specialized corpora preparation tool for further analysis.


2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.


Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  

Author(s):  
Pamela Rogalski ◽  
Eric Mikulin ◽  
Deborah Tihanyi

In 2018, we overheard many CEEA-AGEC members stating that they have "found their people"; this led us to wonder what makes this evolving community unique. Using cultural historical activity theory to view the proceedings of CEEA-ACEG 2004-2018 in comparison with the geographically and intellectually adjacent ASEE, we used both machine-driven (Natural Language Processing, NLP) and human-driven (literature review of the proceedings) methods. Here, we hoped to build on surveys—most recently by Nelson and Brennan (2018)—to understand, beyond what members say about themselves, what makes the CEEA-AGEC community distinct, where it has come from, and where it is going. Engaging in the two methods of data collection quickly diverted our focus from an analysis of the data themselves to the characteristics of the data in terms of cultural historical activity theory. Our preliminary findings point to some unique characteristics of machine- and human-driven results, with the former, as might be expected, focusing on the micro-level (words and language patterns) and the latter on the macro-level (ideas and concepts). NLP generated data within the realms of "community" and "division of labour" while the review of proceedings centred on "subject" and "object"; both found "instruments," although NLP with greater granularity. With this new understanding of the relative strengths of each method, we have a revised framework for addressing our original question.  


2020 ◽  
Author(s):  
Vadim V. Korolev ◽  
Artem Mitrofanov ◽  
Kirill Karpov ◽  
Valery Tkachenko

The main advantage of modern natural language processing methods is a possibility to turn an amorphous human-readable task into a strict mathematic form. That allows to extract chemical data and insights from articles and to find new semantic relations. We propose a universal engine for processing chemical and biological texts. We successfully tested it on various use-cases and applied to a case of searching a therapeutic agent for a COVID-19 disease by analyzing PubMed archive.


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