What is Text Feature Engineering?
Text feature engineering is the process of transforming text data into features that can be used for analysis. This is done by constructing different methods such as N-grams, syntactic parsing, entities, word embedding, statistical features, and word-based features. It is an important part of natural language processing (NLP) that helps to extract the valuable information from text.
Text feature engineering is used in a variety of applications, such as text classification, sentiment analysis, and entity identification. It is also used to improve the accuracy of machine learning models by providing more information about the data.
In this article, we will discuss the different techniques of text feature engineering, such as syntactic parsing, part-of-speech tagging, entity extraction, topic modeling, N-grams, and statistical features. We will also discuss how to use these features to improve the accuracy of natural language processing models.
Syntactic Parsing
Syntactic parsing is the process of analyzing the arrangement and grammar of words in a sentence to determine the relationship between them. Syntactic parsing helps to identify the root word of a sentence and how it is connected to the other words in the sentence.
The relationships between words in a sentence can be represented in the form of a dependency tree. This tree shows how the words are connected to each other, and how they are related to the root word. Dependency trees can be generated using the Natural Language Toolkit (NLTK).
Part-of-Speech Tagging
Part-of-speech (POS) tagging is the process of assigning a tag to each word in a sentence to indicate its function and usage. The most common tags include adverbs, verbs, nouns, and adjectives.
POS tagging is used for various NLP tasks, such as improving word-based features, word sense disambiguation, lemmatization, and stop-word removal. It is also used for word sense disambiguation, as certain words have multiple meanings depending on their context.
Entity Extraction
Entity extraction is the process of identifying important parts of a sentence, such as verb phrases or noun phrases. Entity detection algorithms are usually ensemble models, dictionary lookups, rule-based parsing, dependency and POS tagging, and graph-based methods.
The most common entity extraction methods are named entity recognition (NER) and topic modeling. NER is the process of identifying names, locations, companies, and other entities from the text. Topic modeling is the process of automatically identifying topics present in a given corpus. The most popular topic modeling method is Latent Dirichlet Allocation (LDA).
N-Grams
N-grams are combinations of N words. N-grams are usually more informative than word features. Bi-grams, in which N is equal to two, are the most important features of all. Bi-grams can be generated from a text using the following code:
def generate_ngrams(text, n):
words = text.split()
output = []
for i in range(len(words)-n+1):
output.append(words[i:i+n])
return output
generate_ngrams(‘your sample text’, 2)
This code will generate bi-grams for the given text, which can be used for feature extraction.
Text Data Statistical Features
Textual data can be converted directly into numbers using techniques such as inverse document frequency (IDF) and term frequency (TF). IDF and TF are weighted models used for information retrieval tasks.
TF is the count of a term T in a document, while IDF is the ratio of total documents to the number of documents that contain the term T. TF and IDF can be used to calculate the relative importance of terms in a text corpus.
In addition to TF and IDF, there are other readability features, such as punctuation counts, word count, industry-specific word counts, sentence count, Flesch reading ease, syllable counts, and SMOG index. These features can be generated using the Textstat library.
Word Embedding
Word embedding is the process of representing words as vectors. The main goal of word embedding is to reduce the high-dimensional word features into low-dimensional feature vectors, while preserving the contextual similarity present in the text corpus.
The most popular methods for word embedding are GloVe and Word2Vec. These models take a text corpus and produce word vectors as outputs. Word2Vec is composed of two shallow neural networks: Continuous Bag of Words (CBOW) and Skip-gram. These models are used for numerous NLP tasks, as they can construct a vocabulary from the training corpus and learn the word embedding representations.
Conclusion
Text feature engineering is an important part of natural language processing. It is used to transform text data into features that can be used for analysis. In this article, we discussed the different techniques of text feature engineering, such as syntactic parsing, part-of-speech tagging, entity extraction, topic modeling, N-grams, and statistical features. We also discussed how to use these features to improve the accuracy of natural language processing models.