Semantic Representation
نویسندگان
چکیده
Semantic Representation 2 This chapter deals with how word meaning is represented by speakers of a language, reviewing psychological perspectives on the representation of meaning. We start by outlining four key issues in the investigation of word meaning, then we introduce current theories of semantics and we end with a brief discussion of new directions. Meaning representation has long interested philosophers (since Aristotle) and linguists in addition to psychologists, and a very extensive literature exists in these allied fields. However, given our goal to discuss how meaning is represented in speakers' minds/brains, we will not be concerned with theories and debates arising primarily from these fields except where the theories have psychological or neural implications (as for example the work by we present is limited to the meaning of single words; it will not concern the representation and processing of the meaning of larger linguistic units such as sentences and text. When considering how meaning is represented, four fundamental questions to ask are: (1) How are word meanings related to conceptual structures? (2) How is the meaning of each word represented? (3) How are the meanings of different words related to one another? (4) Can the same principles of organisation hold in different content domains (e.g., words referring to objects, words referring to actions, words referring to properties)? With few exceptions, existing theories of semantic organisation have made explicit claims concerning the representation of each meaning and the relations among different word meanings, while the relation between conceptual and semantic structures is often left implicit, and the issue of whether different Semantic Representation 3 principles are needed for representation of different content domains is often neglected. Let us address these questions in turn.
منابع مشابه
The Effect of Semantic Transfer on Iranian EFL Learners’ Lexical Representation and Processing
متن کامل
A Joint Semantic Vector Representation Model for Text Clustering and Classification
Text clustering and classification are two main tasks of text mining. Feature selection plays the key role in the quality of the clustering and classification results. Although word-based features such as term frequency-inverse document frequency (TF-IDF) vectors have been widely used in different applications, their shortcoming in capturing semantic concepts of text motivated researches to use...
متن کاملبرچسبزنی خودکار نقشهای معنایی در جملات فارسی به کمک درختهای وابستگی
Automatic identification of words with semantic roles (such as Agent, Patient, Source, etc.) in sentences and attaching correct semantic roles to them, may lead to improvement in many natural language processing tasks including information extraction, question answering, text summarization and machine translation. Semantic role labeling systems usually take advantage of syntactic parsing and th...
متن کاملEffect of Historical Buildings Representation in Cyberspace in Creating Tourists’ Destination Image (Qualitative Study of Traditional Accommodations in Kashan)
Introduction: Understanding the representation components of the historical buildings in cyberspace and their impact on the mental image of the tourists is a significant fact in tourism recognition and management. A part of this subject is the impact of place representation on the destination image of the tourist. In this research, the destination is traditional accommodations that attract tour...
متن کاملImage Classification via Sparse Representation and Subspace Alignment
Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...
متن کاملNamed Entity Recognition in Persian Text using Deep Learning
Named entities recognition is a fundamental task in the field of natural language processing. It is also known as a subset of information extraction. The process of recognizing named entities aims at finding proper nouns in the text and classifying them into predetermined classes such as names of people, organizations, and places. In this paper, we propose a named entity recognizer which benefi...
متن کامل