نتایج جستجو برای: a single sentence can provide different meanings
تعداد نتایج: 13986132 فیلتر نتایج به سال:
Decades of research have documented that acquiring the meanings of verbs is, on average, more difficult than acquiring the meanings of nouns. This is likely because verb learning requires more information, and information of a different kind, than noun learning. One particularly powerful source of information for verb learning is linguistic context. For example, Naigles (1990) demonstrated in a...
in the new age, in regard to human success in education in the globalizing world, one can consider english proficiency and individual differences as two important issues. in efl settings, reading skill plays an important role in education. it seems that using multiple intelligences theory in efl classes which values individual differences by tapping different intelligences, can be rewarding. as...
In this paper, extractive speech summarization using different machine learning algorithms was investigated. The task of Speech summarization deals with extracting important and salient segments from speech in order to access, search, extract and browse speech files easier and in a less costly manner. In this paper, a new method for speech summarization without using automatic speech recognitio...
abstract lexical knowledge of complex english words is an important part of language skills and crucial for fluent language use (nation, 2001). the present study, thus, was an attempt to assess the role of morphological decomposition awareness as a vocabulary learning strategy on learners’ productive and receptive recall and recognition of complex english words. so 90 sophomores (female and ma...
The general problem area of machine translation has been outlined and related to other fields of language-data processing in Garvin's “A Linguist's View of Language-data Processing.” It has been discussed in some detail in the two immediately preceding sections by Hays and Harper. This discussion will deal more specifically with the question of syntax: both with the place of syntax in the over-...
Representing a sentence with a fixed vector has shown its effectiveness in various NLP tasks. Most of the existing methods are based on neural network, which recursively apply different composition functions to a sequence of word vectors thereby obtaining a sentence vector. A hypothesis behind these approaches is that the meaning of any phrase can be composed of the meanings of its constituents...
The ‘glue semantics’ that has been developed for Lexical-Functional Grammar (LFG) is based on the idea of using linear logic proofs to effect the semantic assembly of the meaning of a sentence from the meanings of its parts. The production of the meanings or model-theoretic interpretations themselves is standardly achieved by labeling the nodes of the deduction with terms in a combination of li...
One of the key challenges for creating the semantic representation of a text is mapping words found in a natural language text to their meanings. This task, Word Sense Disambiguation (WSD), is confounded by the fact that words have multiple meanings, or senses, dictated by their use in a sentence and the domain. We present an algorithm that employs random walks over the graph structure of knowl...
In previous work we gave a mathematical foundation, referred to as DisCoCat, for how words interact in sentence order produce the meaning of that sentence. To do so, exploited perfect structural match grammar and categories spaces. Here, give DisCoCirc, sentences texts text. First revisit DisCoCat. While DisCoCat all meanings are fixed states (i.e. have no input), DisCoCirc word correspond type...
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