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Items can belong to more than one relation, which allows words to appear in the word relation and, say, the syntax relation.
Xia et al. [24] design two types of feature sets: POS based and word relation based.
Their word relation based method improves an accuracy of 87.7 and 85.15% on movie and product datasets.
Also, Xia et al. [24] improved the classification performances from 84.8 to 87.7% using their proposed word relation based feature selection method.
Russell at times used the word "relation" in a broad sense so as to include qualities, which could be considered as "monadic" relations, i.e., relations that only involve one relatum.
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(You have to love the fact that they still use the word "relations". So quaint).
Further research on the possibility of combining the presented statistics-based method with formal knowledge-based methods could also lead to improvements in recognition of word relations in a spoken sentence and consequently to a reduction in recognition errors.
In other words, relation (6) is well defined for all x 1,x 2≥0.
Instead, he insisted that the proper word was "relation": "Relation is a mathematical notion which means one-to-one correspondence".
First, two types of feature sets are designed for sentiment classification, namely the part-of-speech based feature sets and the word-relation based feature sets.
The current study tested these assumptions by comparing different kinds of words (e.g., words versus non-words) and different kinds of word-word relations (e.g., associative versus repetition).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com