Lexicon coverage is often the limiting factor in natural language processing systems. Recent work has attempted to remedy this situation by extracting information from machine readable dictionaries. Unfortunately, no NLP lexicon system or dictionary could possibly list all the potential words of English. However, humans are often able to interpret novel word forms (that is, words they have not ...

We introduce the notion of the ‘meaning bound’ of a word with respect to another word by making use of the World-Wide Web as a conceptual environment for meaning. The meaning of a word with respect to another word is established by multiplying the product of the number of webpages containing both words by the total number of webpages of the World-Wide Web, and dividing the result by the product...

Introduction Iconicity, or the resemblance-based mapping between aspects of form and meaning, has long been marginalised in linguistic research due to the predominance of arbitrari-ness, where there is no connection between the form of a word and aspects of its meaning other than social convention [1]. For example, there is nothing iconic about the arbitrary word dog; d does not mean " four-leg...

The first word of the Hebrew text of Genesis "תישארב" as traditionally vocalized means literally "In a beginning." However, tradition gives it the meaning and translation "In the beginning." The literal meaning is considered as contradicting reality. Therefore, Rashi suggested a syntactic solution that maintains the traditional meaning. However, this syntactic solution, as is shown later, requi...

Existing techniques for vector-space semantic representations have provided useful tools for the automatic building of semantic type systems. However, these models tend to pay little attention to the position of each element in the sequence of words. This leads to the loss of valuable information. We present an unsupervised technique that extracts rich representations encoding morphological, sy...

An important issue in language learning is how new words are integrated in the brain representations that sustain language processing. To identify the brain regions involved in meaning acquisition and word learning, we conducted a functional magnetic resonance imaging study. Young participants were required to deduce the meaning of a novel word presented within increasingly constrained sentence...

Lexical semantics is the study of word meaning. The semantic web is a vision of what the web could be if it would foremost consist of knowledge (structured data) rather than text or other unstructured data as it is today. This talk is about the future of word meaning if the semantic web becomes a reality. First, I will therefore briefly clarify what the semantic web vision consists of, followed...

This paper presents a new method for analyzing words in the Persian language context to find orthographical and structural errors regardless of the meaning. This technique tokenizes each word in a statement then tries to detect the kind of word, and analyses its correctness in terms of orthography and morphology by means of a lexicon. It should be noted that some words in the Persian language h...

This article argues that Vygotsky’s choice of word meaning as the basic unit of analysis for cultural psychology connects him to a German psycholinguistic tradition—exemplified in the work of G. W. F. Hegel and J. G. Herder—distinct from the Marxist tradition. While later commentators criticize Vygotsky’s reliance on word meaning, arguing that it cannot explain the formation of consciousness, t...

First-order logic provides a powerful and flexible mechanism for representing natural language semantics. However, it is an open question of how best to integrate it with uncertain, probabilistic knowledge, for example regarding word meaning. This paper describes the first steps of an approach to recasting first-order semantics into the probabilistic models that are part of Statistical Relation...