Similarity computation using semantic networks created from web-harvested data

Iosif, Elias; Potamianos, Alexandros

doi:10.1017/s1351324913000144

Cited by 27 publications

(28 citation statements)

References 44 publications

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“…Models built on this assumption are called Distributional Similarity Models (DSMs) and take into account the co-occurrence distributions of the words in order to cluster them together. Several implementations of DSMs have been proposed in the last decades [3,5,8,10,15] and have being used in tasks such as query expansion [1], building bilingual comparable corpora [16], clustering [2], discovering of meaning of noun compounds [14] etc.…”

Section: Introductionmentioning

confidence: 99%

Comparing Semantic Relatedness between Word Pairs in Portuguese Using Wikipedia

Granada

Santos

Vieira

2014

Lecture Notes in Computer Science

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Open Archive Toulouse Archive OuverteOATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible To cite this version: Leitzke Granada, Roger and Trojahn, Cassia and Vieira, Renata Comparing Semantic Relatedness between Word Abstract. The growth of available data in digital format has been facil-itating the development of new models to automatically infer the seman-tic similarity between word pairs. However, there are still many natural languages without sufficient resources to evaluate measures of semantic relatedness. In this paper we translated word pairs from a well-known baseline for evaluating semantic relatedness measures into Portuguese and performed a manual evaluation of each pair. We compared the correlation with similar datasets in other languages and generated LSA models from Wikipedia articles in order to verify the pertinence of each dataset and how semantic similarity conveys across languages.

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Section: Introductionmentioning

confidence: 99%

Comparing Semantic Relatedness between Word Pairs in Portuguese Using Wikipedia

Granada

Santos

Vieira

2014

Lecture Notes in Computer Science

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“…Semantic similarity is a measurement method that defines each document or term which has a distance based on semantic meaning [14,15]. There are two types of similarity calculation, which are based on the existing resources, such as thesaurus and based on the spread of words in a corpus [22].…”

Section: Semantic Similaritymentioning

confidence: 99%

Sentiment Analysis of Hotel Reviews Using Latent Dirichlet Allocation, Semantic Similarity and LSTM

Priyantina¹,

Sarno²

2019

IJIES

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Product reviews are usually determined by sentiment of customers; however sentiment analysis based on aspects still need further research. A hotel commonly has five aspects, which are location, meal, service, comfort and cleanliness. This research proposes methods to determine review sentiment according to the hotel aspects. A hotel reviews are preprocessed into a term list. Firstly, Latent Dirichelet Allocation (LDA) determines the hidden topics of a term list; then Semantic Similarity categorizes the term list based on the topic resulted by Latent Dirichelet Allocation (LDA) into the five aspects of a hotel. Then in calculating similarity measurement, the term list is expanded by using the Term Frequency-Inverse Cluster Frequency (TF-ICF) method. Finally, a classification of customer sentiment (satisfied or dissatisfied) is conducted by using the combination of Word Embedding and Long-short Term Memory (LSTM). The results show that the proposed method can classify the reviews into the five hotel aspects. The highest aspect categorization performance is obtained by using LDA + TF-ICF 100% + Semantic Similarity which reaches 85%; the performance sentiment classification for the highest aspect-based sentiment analysis is obtained by using Word Embedding + LSTM which reaches 93%; and the comfort aspect receives more negative sentiments compared to the sentiments of other aspects. Also the results show that a sentiment is influenced by an aspect.

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“…The semantic-affective mapping was expanded in [31] for estimating scores for other dimensions, such as word familiarity and age acquisition. An example of the exploitation of affective spaces for semantic tasks can be found in [32] dealing with the detection of semantic opposition.…”

Section: Related Workmentioning

confidence: 99%

Lexical and affective models in early acquisition of semantics

Kolovou¹,

Iosif²,

Potamianos³

2017

6th Workshop on Child Computer Interaction (WOCCI 2017)

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Motivated by theories of early language development in children we investigate the contribution of affective features to early acquisition of lexical semantics. For the task of semantic similarity between words, semantic and affective spaces are modeled using network-based distributed semantic models. We propose a method for constructing semantic activations from a combination of lexical and affective relations and show that affective information plays a prominent role in our lexical development model.

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Similarity computation using semantic networks created from web-harvested data

Cited by 27 publications

References 44 publications

Comparing Semantic Relatedness between Word Pairs in Portuguese Using Wikipedia

Comparing Semantic Relatedness between Word Pairs in Portuguese Using Wikipedia

Sentiment Analysis of Hotel Reviews Using Latent Dirichlet Allocation, Semantic Similarity and LSTM

Lexical and affective models in early acquisition of semantics

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