Originally inspired by categorical quantum mechanics (Abramsky and Coecke, LiCS'04), the categorical compositional distributional model of natural language meaning of Coecke, Sadrzadeh and Clark provides a conceptually motivated procedure to compute the meaning of a sentence, given its grammatical structure within a Lambek pregroup and a vectorial representation of the meaning of its parts. The predictions of this first model have outperformed that of other models in mainstream empirical language processing tasks on large scale data. Moreover, just like CQM allows for varying the model in which we interpret quantum axioms, one can also vary the model in which we interpret word meaning.
In this paper we show that further developments in categorical quantum mechanics are relevant to natural language processing too. Firstly, Selinger's CPM-construction allows for explicitly taking into account lexical ambiguity and distinguishing between the two inherently different notions of homonymy and polysemy. In terms of the model in which we interpret word meaning, this means a passage from the vector space model to density matrices. Despite this change of model, standard empirical methods for comparing meanings can be easily adopted, which we demonstrate by a small-scale experiment on real-world data. This experiment moreover provides preliminary evidence of the validity of our proposed new model for word meaning.
Secondly, commutative classical structures as well as their non-commutative counterparts that arise in the image of the CPM-construction allow for encoding relative pronouns, verbs and adjectives, and finally, iteration of the CPM-construction, something that has no counterpart in the quantum realm, enables one to accommodate both entailment and ambiguity.
A categorical error sensitivity index (ISEC): A preventive ordinal decision-support measure for irrecoverable errors in manual data entry systems
Universidad Nacional de Cuyo Instituto de Ingeniería（乌拉圭国立大学工程学院）；Universidad Tecnologica Nacional Facultad Regional San Rafael Mendoza（技术大学国家Mendoza地区分校）
Bridging the Semantic Gap for Categorical Data Clustering via Large Language Models
School of Computer Science and Technology（计算机科学与技术学院）；Guangdong University of Technology（广东技术大学）；Department of Computer Science（计算机科学系）；Hong Kong Baptist University（香港 Baptist 大学）
Intra-neuronal attention within language models Relationships between activation and semantics
Neocognition (Chrysippe R&D)（新认知公司（克律西佩研发部））；Facultés Libres de Philosophie et de Psychologie de Paris (ER IPC)（巴黎自由哲学与心理学院（ER IPC））；Faculté de Médecine de Lyon Est (Université Lyon 1)（里昂第一大学东里昂医学院）；Université Paris 8（巴黎第八大学）；INP-PHELMA, Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学INP-PHELMA学院）
