Modelling Semantic Association and Conceptual Inheritance for Semantic Analysis
Pascal Vaillant
备注：
8 pages, 2 figures, LaTeX 2e using Springer LNCS class, with packages epsf and amssymb. Proceedings of the 4th International Conference on Text, Speech and Dialogue (TSD 2001), Zelezna Ruda, Czech Republic, 10-13 Sept. 2001
Allowing users to interact through language borders is an interesting challenge for information technology. For the purpose of a computer assisted language learning system, we have chosen icons for representing meaning on the input interface, since icons do not depend on a particular language. However, a key limitation of this type of communication is the expression of articulated ideas instead of isolated concepts. We propose a method to interpret sequences of icons as complex messages by reconstructing the relations between concepts, so as to build conceptual graphs able to represent meaning and to be used for natural language sentence generation. This method is based on an electronic dictionary containing semantic information.
Purdue University（普渡大学）；University of Chicago（芝加哥大学）
A characteristic feature of human semantic cognition is its ability to not only store and retrieve the properties of concepts observed through experience, but to also facilitate the inheritance of properties (can breathe) from superordinate concepts (animal) to their subordinates (dog) -- i.e. demonstrate property inheritance. In this paper, we present COMPS, a collection of minimal pair sentences that jointly tests pre-trained language models (PLMs) on their ability to attribute properties to concepts and their ability to demonstrate property inheritance behavior. Analyses of 22 different PLM…
Characterizing the Role of Similarity in the Property Inferences of Language Models
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Northeastern University（东北大学）；Technion – Israel Institute of Technology（以色列理工学院）；Toyota Technological Institute at Chicago（丰田芝加哥技术学院）
Ferhat Abbas University（费哈特·阿巴斯大学）；College of Sciences（理学院）；College of computer & Information Sciences（计算机与信息科学学院）；King Saud University（沙特国王大学）
Ontology can be seen as a formal representation of knowledge. They have been investigated in many artificial intelligence studies including semantic web, software engineering, and information retrieval. The aim of ontology is to develop knowledge representations that can be shared and reused. This research project is concerned with the use of association rules to extract the Quran ontology. The manual acquisition of ontologies from Quran verses can be very costly; therefore, we need an intelligent system for Quran ontology construction using patternbased schemes and associations rules to disco…
Humans carry stereotypic tacit assumptions (STAs) (Prince, 1978), or propositional beliefs about generic concepts. Such associations are crucial for understanding natural language. We construct a diagnostic set of word prediction prompts to evaluate whether recent neural contextualized language models trained on large text corpora capture STAs. Our prompts are based on human responses in a psychological study of conceptual associations. We find models to be profoundly effective at retrieving concepts given associated properties. Our results demonstrate empirical evidence that stereotypic conce…
Machine learning is a means to uncover deep patterns from rich sources of data. Here, we find that machine learning can recover the conceptual organization of the human mind when applied to the natural language use of millions of people. Utilizing text from billions of webpages, we recover most of the concepts contained in English, Dutch, and Japanese, as represented in large scale Word Association networks. Our results justify machine learning as a means to probe the human mind, at a depth and scale that has been unattainable using self-report and observational methods. Beyond direct psycholo…
Meanings are like Onions: a Layered Approach to Metaphor Processing
Institute for Cognitive Sciences and Technologies (ISTC), CNR, Rome, Italy（认知科学与技术研究所（ISTC），CNR，罗马，意大利）；University of Bologna, Italy（博洛尼亚大学，意大利）；University of Turin, Italy（都灵大学，意大利）
University of Exeter（埃克塞特大学）；University of Pisa（比萨大学）；Institute of Information Science and Technologies, National Research Council（国家研究委员会信息科学与技术研究所）；University of Ghent（根特大学）；Technion, Israel Institute of Technology（以色列理工学院）；University of Kansas（堪萨斯大学）
The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Decades of psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? We here review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating t…
