Improving text relatedness by incorporating phrase relatedness with word relatedness

2018 ◽  
Vol 34 (3) ◽  
pp. 939-966
Author(s):  
Rashadul Hasan Rakib ◽  
Aminul Islam ◽  
Evangelos Milios
Keyword(s):  
Word Relatedness

Open-Ended Questions

2020 ◽  
pp. 202-218
Author(s):  
Subhadra Dutta ◽  
Eric M. O’Rourke
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Written Language ◽  
Text Data ◽  
Employee Survey ◽  
Trade Offs ◽  
Word Relatedness ◽  
Survey Responses

Natural language processing (NLP) is the field of decoding human written language. This chapter responds to the growing interest in using machine learning–based NLP approaches for analyzing open-ended employee survey responses. These techniques address scalability and the ability to provide real-time insights to make qualitative data collection equally or more desirable in organizations. The chapter walks through the evolution of text analytics in industrial–organizational psychology and discusses relevant supervised and unsupervised machine learning NLP methods for survey text data, such as latent Dirichlet allocation, latent semantic analysis, sentiment analysis, word relatedness methods, and so on. The chapter also lays out preprocessing techniques and the trade-offs of growing NLP capabilities internally versus externally, points the readers to available resources, and ends with discussing implications and future directions of these approaches.

Relationship between Free Associational and Judgmental Measures of Word Relatedness

1965 ◽  
Vol 16 (1) ◽  
pp. 279-282
Author(s):  
W. A. Bousfield ◽  
C. R. Puff
Keyword(s):  
Separate Group ◽  
Conceptual Category ◽  
Associative Relatedness ◽  
Word Relatedness ◽  
The Individual

Judgments of degree of associative relatedness were obtained for the members of S-R pairs selected from free associational norms. Each of 13 pairs, designated as taxonomic, represented a different conceptual category. The individual members of each of 13 pairs, designated as associative, were not taxonomically similar. The inter-item free associational strengths of the two types of pairs varied from high to low. A separate group of 20 Ss was assigned to each list to give judgments of the forward and reverse associative strengths of the individual pairs. The correlations between the free associational and judgmental measures of the relatedness of the members of the pairs were highly significant.

Efficient Computation of Co-occurrence Based Word Relatedness

2015 ◽  
Author(s):  
Jie Mei ◽  
Xinxin Kou ◽  
Zhimin Yao ◽  
Andrew Rau-Chaplin ◽  
Aminul Islam ◽  
...  
Keyword(s):  
Efficient Computation ◽  
Word Relatedness

Exploring ESA to Improve Word Relatedness

2014 ◽  
Author(s):  
Nitish Aggarwal ◽  
Kartik Asooja ◽  
Paul Buitelaar
Keyword(s):  
Word Relatedness

scholarly journals A Random Walk–Based Model for Identifying Semantic Orientation

2014 ◽  
Vol 40 (3) ◽  
pp. 539-562 ◽  
Author(s):  
Ahmed Hassan ◽  
Amjad Abu-Jbara ◽  
Wanchen Lu ◽  
Dragomir Radev
Keyword(s):  
Random Walk ◽  
Language Processing ◽  
Response Analysis ◽  
Markov Random Walk ◽  
Text Filtering ◽  
Semantic Orientation ◽  
On Line ◽  
Word Relatedness ◽  
Standard Set ◽  
Weakly Supervised

Automatically identifying the sentiment polarity of words is a very important task that has been used as the essential building block of many natural language processing systems such as text classification, text filtering, product review analysis, survey response analysis, and on-line discussion mining. We propose a method for identifying the sentiment polarity of words that applies a Markov random walk model to a large word relatedness graph, and produces a polarity estimate for any given word. The model can accurately and quickly assign a polarity sign and magnitude to any word. It can be used both in a semi-supervised setting where a training set of labeled words is used, and in a weakly supervised setting where only a handful of seed words is used to define the two polarity classes. The method is experimentally tested using a gold standard set of positive and negative words from the General Inquirer lexicon. We also show how our method can be used for three-way classification which identifies neutral words in addition to positive and negative words. Our experiments show that the proposed method outperforms the state-of-the-art methods in the semi-supervised setting and is comparable to the best reported values in the weakly supervised setting. In addition, the proposed method is faster and does not need a large corpus. We also present extensions of our methods for identifying the polarity of foreign words and out-of-vocabulary words.

A rating scale measure of word relatedness

1969 ◽  
Vol 8 (2) ◽  
pp. 252-256 ◽  
Author(s):  
J. Ronald Gentile ◽  
Robert Seibel
Keyword(s):  
Rating Scale ◽  
Word Relatedness ◽  
Scale Measure
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