Of course, it’s not difficult to understand that most of these candidates will be of no interest to us. For example, we may be interested in finding all unigram, bigram, and trigrams.Įven for a reasonable size of text, this will give us a huge volume of candidates to go through. A brute force approach can be to select all n-grams possible for a particular limit of n. Obviously, we begin by performing tokenization of the text we are interested in. The first step toward extracting keyphrases from a text is to identify potential candidates. Language models are also quite effective in generating word embeddings. Word2Vec is a popular algorithm developed by Tomas Mikolov that uses neural networks to generate word embeddings from a large text corpus. Word embeddings prove to be more efficient in representing words as numerical vectors by capturing their meaning. However, these ways fail to capture the semantic value of the text. One of the simplest approaches is one-hot encoding. There are several ways to represent texts as numerical vectors. The vectors here are the numerical representations of the text. For instance, cosine similarity works by measuring the cosine of the angle between two vectors projected in multi-dimensional space. Some popular algorithms to determine the lexical similarity of words include Jaccard Similarity, Levenshtein distance, and cosine similarity. We can be interested in lexical similarity or semantic similarity. In this context, similarity can mean multiple things. For several NLP tasks, we must be able to determine how similar two words or phrases are to each other.
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