Semantic Noise and Conceptual Stagnation in Natural Language Processing

IF 0.2 4区 社会学 0 HUMANITIES, MULTIDISCIPLINARY ANGELAKI-JOURNAL OF THE THEORETICAL HUMANITIES Pub Date : 2023-05-04 DOI:10.1080/0969725X.2023.2216555
S. de Jager
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Abstract

Abstract Semantic noise, the effect ensuing from the denotative and thus functional variability exhibited by different terms in different contexts, is a common concern in natural language processing (NLP). While unarguably problematic in specific applications (e.g., certain translation tasks), the main argument of this paper is that failing to observe this linguistic matter of fact as a generative effect rather than as an obstacle, leads to actual obstacles in instances where language model outputs are presented as neutral. Given that a common and long-standing challenge in NLP is the interpretation of ambiguous – i.e., semantically noisy – cases, this article focuses on an exemplar ambiguity-resolution task in NLP: the problem of anaphora in Winograd schemas. The main question considered is: to what extent is the standard approach to disambiguation in NLP subject to a stagnant “image of language”? And, can a transdisciplinary, dynamic approach combining linguistics and philosophy elucidate new perspectives on these possible conceptual shortcomings? In order to answer these questions we explore the term and concept of noise, particularly in its presentation as semantic noise. Owing to its definitional plurality, and sometimes even desirable unspecificity, the term noise is thus used as proof of concept for semantic generativity being an inherent characteristic in linguistic representation, and its concept is used to interrogate assumptions admitted in the resolution of Winograd schemas. The argument is speculative and theoretical in method, and the result is an analysis which provides an account of the fundamentally dialogical and necessarily open-ended effects of semantic noise in natural language.
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自然语言处理中的语义噪声与概念停滞
摘要语义噪声是自然语言处理中的一个常见问题,它是由不同术语在不同语境中表现出的外延变异和功能变异所产生的影响。尽管在特定的应用中(例如,某些翻译任务)存在无可争议的问题,但本文的主要论点是,在语言模型输出为中性的情况下,如果不能将这一语言事实视为一种生成效应而不是一种障碍,就会导致实际的障碍。鉴于NLP中一个常见且长期存在的挑战是对歧义(即语义噪声)情况的解释,本文重点讨论了NLP中的一个示例歧义解决任务:Winograd图式中的回指问题。考虑的主要问题是:NLP中消除歧义的标准方法在多大程度上受制于停滞不前的“语言形象”?而且,将语言学和哲学相结合的跨学科、动态的方法能否阐明对这些可能的概念缺陷的新观点?为了回答这些问题,我们探讨了噪声的术语和概念,特别是在其作为语义噪声的表现中。由于其定义的多样性,有时甚至是理想的非特定性,术语“噪音”因此被用作概念证明,因为语义生成性是语言表征的固有特征,其概念被用来质疑Winograd图式解析中承认的假设。这一论点在方法上是推测性的和理论性的,其结果是对自然语言中语义噪声的根本对话性和必然开放性影响的分析。
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来源期刊
ANGELAKI-JOURNAL OF THE THEORETICAL HUMANITIES
ANGELAKI-JOURNAL OF THE THEORETICAL HUMANITIES HUMANITIES, MULTIDISCIPLINARY-
CiteScore
0.60
自引率
33.30%
发文量
57
期刊介绍: Angelaki: journal of the theoretical humanities was established in September 1993 to provide an international forum for vanguard work in the theoretical humanities. In itself a contentious category, "theoretical humanities" represents the productive nexus of work in the disciplinary fields of literary criticism and theory, philosophy, and cultural studies. The journal is dedicated to the refreshing of intellectual coordinates, and to the challenging and vivifying process of re-thinking. Angelaki: journal of the theoretical humanities encourages a critical engagement with theory in terms of disciplinary development and intellectual and political usefulness, the inquiry into and articulation of culture.
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