Figure 5: Paraphrasing by using semantic embeddings.
r is determined by a geometric distribution with parameter p in which P [r] ∼ p
r
.
The index s of the synonym chosen given a word is also determined by a another
geometric distribution in which P [s] ∼ p
s
. This method ensures synonyms that
are more similar to the original word are selected with a greater probability.
Some methods [41, 42, 43] apply a similar method.
A widely used text augmentation method called EDA (Easy Data Aug-
mentation Techniques) [6] also replaces the original words with their synonyms
using WordNet: they randomly choose n words from the sentence that are not
stop words, and replace each of these words with one of its synonyms chosen
at random, instead of following the geometric distribution.
5
Zhang et al. [44]
apply a similar method in extreme multi-label classification.
In addition to synonyms, Coulombe et al. [7] propose to use hypernyms
to replace the original words. They also recommend the types of words that
are candidates for lexical substitution in order of increasing difficulty: adverbs,
adjectives, nouns and verbs. Zuo et al. [45] use WordNet and VerbNet [46] to
retrieve synonyms, hypernyms, and words of the same category.
Thesauruses
Advantage(s):
1. Easy to use.
Limitation(s):
1. The scope and part-of-speech of replacement words are limited.
2. This method cannot solve the problem of ambiguity.
3. The sentence semantics may be affected if too many replacements occur.
2.1.2. Semantic Embeddings
This method overcomes the limitation of the replacement range and word
part-of-speech in the Thesaurus-based method. It uses pre-trained word vectors,
such as Glove, Word2Vec, FastText, etc., and replaces them with the word
closest to the original word in the vector space, as shown in Figure 5.
In the Twitter message classification task, Wang et al. [8] pioneer to use
both word embeddings and frame embeddings instead of discrete words.
6
As
for word embeddings, each original word in the tweet is replaced with one of
the k-nearest-neighbor words using cosine similarity. For example, “Being late
is terrible” becomes “Being behind are bad”. As for frame semantic embeddings,
5
n is proportional to the length of the sentence.
6
The frame embeddings refer to the continuous embeddings of semantic frames [47].
8
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