scholarly journals Beyond cloze probability: Parafoveal processing of semantic and syntactic information during reading

2018 ◽  
Vol 100 ◽  
pp. 1-17 ◽  
Author(s):  
Aaron Veldre ◽  
Sally Andrews
Keyword(s):  
Parafoveal Processing ◽  
Syntactic Information

Do Successor Effects in Reading Reflect Parafoveal Processing? Evidence From Corpus-Based and Experimental Eye Movement Data

2014 ◽  
Author(s):  
Bernhard Angele ◽  
Elizabeth R. Schotter ◽  
Timothy Slattery ◽  
Tara L. Chaloukian ◽  
Klinton Bicknell ◽  
...  
Keyword(s):  
Eye Movement ◽  
Parafoveal Processing ◽  
Movement Data

Key phrase Extraction by Improving TextRank with an Integration of Word Embedding and Syntactic Information

2020 ◽  
Vol 13 ◽  
Author(s):  
Sheng Zhang ◽  
Qi Luo ◽  
Yukun Feng ◽  
Ke Ding ◽  
Daniela Gifu ◽  
...  
Keyword(s):  
Semantic Information ◽  
Performance Enhancement ◽  
Word Embedding ◽  
The Other ◽  
Test Set ◽  
Pagerank Algorithm ◽  
Phrase Extraction ◽  
Extraction Algorithm ◽  
Syntactic Information ◽  
Key Phrase Extraction

Background: As a known key phrase extraction algorithm, TextRank is an analogue of PageRank algorithm, which relied heavily on the statistics of term frequency in the manner of co-occurrence analysis. Objective: The frequency-based characteristic made it a neck-bottle for performance enhancement, and various improved TextRank algorithms were proposed in the recent years. Most of improvements incorporated semantic information into key phrase extraction algorithm and achieved improvement. Method: In this research, taking both syntactic and semantic information into consideration, we integrated syntactic tree algorithm and word embedding and put forward an algorithm of Word Embedding and Syntactic Information Algorithm (WESIA), which improved the accuracy of the TextRank algorithm. Results: By applying our method on a self-made test set and a public test set, the result implied that the proposed unsupervised key phrase extraction algorithm outperformed the other algorithms to some extent.

Incorporate Syntactic Information for Short Text Classification

2011 ◽  
Vol 268-270 ◽  
pp. 697-700
Author(s):  
Rui Xue Duan ◽  
Xiao Jie Wang ◽  
Wen Feng Li
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Environment ◽  
Text Classification ◽  
The Internet ◽  
Selection Methods ◽  
Text Documents ◽  
Short Text ◽  
Syntactic Information ◽  
Dependency Relations

As the volume of online short text documents grow tremendously on the Internet, it is much more urgent to solve the task of organizing the short texts well. However, the traditional feature selection methods cannot suitable for the short text. In this paper, we proposed a method to incorporate syntactic information for the short text. It emphasizes the feature which has more dependency relations with other words. The classifier SVM and machine learning environment Weka are involved in our experiments. The experiment results show that incorporate syntactic information in the short text, we can get more powerful features than traditional feature selection methods, such as DF, CHI. The precision of short text classification improved from 86.2% to 90.8%.

scholarly journals Corrigendum to “Do successor effects in reading reflect lexical parafoveal processing? Evidence from corpus-based and experimental eye movement data” [J. Mem. Lang. 79–80 (2015) 76–96]

2016 ◽  
Vol 88 ◽  
pp. 133-143 ◽  
Author(s):  
Bernhard Angele ◽  
Elizabeth R. Schotter ◽  
Timothy J. Slattery ◽  
Tara L. Tenenbaum ◽  
Klinton Bicknell ◽  
...  
Keyword(s):  
Eye Movement ◽  
Parafoveal Processing ◽  
Movement Data

scholarly journals A language extension set to generate adaptive versions automatically

2018 ◽  
Vol 73 ◽  
pp. 52 ◽  
Author(s):  
Maxime Schmitt ◽  
Cédric Bastoul ◽  
Philippe Helluy
Keyword(s):  
Second Step ◽  
Development Effort ◽  
Approximate Computing ◽  
Problem Size ◽  
Actual Problem ◽  
Language Extension ◽  
Time Space ◽  
Syntactic Information ◽  
Compiler Infrastructure ◽  
The Impact

A large part of the development effort of compute-intensive applications is devoted to optimization, i.e., achieving the computation within a finite budget of time, space or energy. Given the complexity of modern architectures, writing simulation applications is often a two-step workflow. Firstly, developers design a sequential program for algorithmic tuning and debugging purposes. Secondly, experts optimize and exploit possible approximations of the original program to scale to the actual problem size. This second step is a tedious, time-consuming and error-prone task. In this paper we investigate language extensions and compiler tools to achieve that task semi-automatically in the context of approximate computing. We identified the semantic and syntactic information necessary for a compiler to automatically handle approximation and adaptive techniques for a particular class of programs. We propose a set of language extensions generic enough to provide the compiler with the useful semantic information when approximation is beneficial. We implemented the compiler infrastructure to exploit these extensions and to automatically generate the adaptively approximated version of a program. We provide an experimental study of the impact and expressiveness of our language extension set on various applications.

scholarly journals Part-of-Speech Classification from Magnetoencephalography Data Using 1-Dimensional Convolutional Neural Network

2020 ◽  
Author(s):  
Alessandro Lopopolo ◽  
Antal van den Bosch
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Mental State ◽  
Human Subjects ◽  
Brain Activity ◽  
Neural Decoding ◽  
Significant Progress ◽  
Part Of Speech ◽  
Syntactic Information ◽  
Speech Classification

Neural decoding of speech and language refers to the extraction of information regarding the stimulus and the mental state of subjects from recordings of their brain activity while performing linguistic tasks. Recent years have seen significant progress in the decoding of speech from cortical activity. This study instead focuses on decoding linguistic information. We present a deep parallel temporal convolutional neural network (1DCNN) trained on part-of-speech (PoS) classification from magnetoencephalography (MEG) data collected during natural language reading. The network is trained on data from 15 human subjects separately, and yields above-chance accuracies on test data for all of them. The level of PoS was targeted because it offers a clean linguistic benchmark level that represents syntactic information and abstracts away from semantic or conceptual representations.

Sign in / Sign up

Export Citation Format

Share Document