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How do you incorporate domain knowledge and linguistic features into your feature engineering process?

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Feature engineering is the process of creating and selecting features that can improve the performance of a predictive model. Features are the variables or attributes that represent the characteristics of the data. In natural language processing (NLP), feature engineering can be challenging because text data is often unstructured, noisy, and complex. How do you incorporate domain knowledge and linguistic features into your feature engineering process? In this article, you will learn some tips and techniques to enhance your NLP feature engineering with domain knowledge and linguistic features.

本文章的要点总结

Incorporate contextual cues:

Look for unique text features that could indicate nuanced sentiment, like ALL CAPS or ellipses, which might express strong emotions or sarcasm. This approach leverages the subtleties of language in your analysis.
Code snippets for guidance:

Providing examples of code that apply domain knowledge and linguistic features can be immensely helpful. It's like having a recipe to follow when you're cooking up your data model.

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1 Domain knowledge

Domain knowledge is the specific information and expertise related to the problem or domain that you are trying to model. Domain knowledge can help you identify relevant and meaningful features that capture the essence of the text data. For example, if you are building a sentiment analysis model for movie reviews, you might want to use domain knowledge to create features such as genre, director, actors, ratings, keywords, or phrases that express opinions or emotions. Domain knowledge can also help you filter out irrelevant or noisy features that might confuse or bias your model.

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Jack Dyer

Founder at Leftshifted | Contract Data Consultant
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Domain knowledge is 1 of 3 key pillars of a great data scientist. It can be so valuable to combine the science with real world application. Typical feature engineering techniques are fairy mathematical, blanket approach and can miss nuance and context. For the movie example here, we can derive and create some really interesting features, that involve understanding something about how movie reviews are written. Do words written in CAPS to stand out indicate stronger sentiment? Maybe the use of elipses (…) could indicate sarcasm, suspense or ambivalence? Maybe certain words have different sentiment in different contexts, such as ‘amateur’. We could build domain based features / flags and find out if they’re predictive and valuable.

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2 Linguistic features

Linguistic features are the features that reflect the structure, meaning, and style of the text data. Linguistic features can help you capture the syntactic, semantic, and pragmatic aspects of the text data. For example, you might want to use linguistic features such as part-of-speech tags, named entities, n-grams, word embeddings, sentiment scores, or topic models to represent the text data. Linguistic features can also help you deal with the variability and ambiguity of natural language, such as synonyms, antonyms, slang, idioms, or metaphors.

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3 Feature extraction

Feature extraction is the process of transforming the raw text data into numerical or categorical features that can be used by a predictive model. Feature extraction can involve various techniques such as tokenization, normalization, lemmatization, stemming, stopword removal, or vectorization. Feature extraction can also involve applying domain knowledge and linguistic features to the text data. For example, you might want to use feature extraction to extract keywords, phrases, or entities from the text data based on your domain knowledge. You might also want to use feature extraction to encode the text data into numerical vectors based on your linguistic features.

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4 Feature selection

Feature selection is the process of choosing the most relevant and informative features that can improve the accuracy and efficiency of a predictive model. Feature selection can involve various techniques such as correlation analysis, chi-square test, mutual information, or wrapper methods. Feature selection can also involve applying domain knowledge and linguistic features to the feature set. For example, you might want to use feature selection to eliminate redundant or irrelevant features based on your domain knowledge. You might also want to use feature selection to rank or weight the features based on your linguistic features.

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5 Feature engineering pipeline

Feature engineering pipeline is the workflow or sequence of steps that you follow to perform feature engineering for your predictive model. Feature engineering pipeline can vary depending on the problem, data, and model that you are using. However, a general feature engineering pipeline might include the following steps: data collection, data cleaning, data exploration, feature extraction, feature selection, feature evaluation, and feature optimization. At each step of the feature engineering pipeline, you can incorporate domain knowledge and linguistic features to enhance your feature engineering process.

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Jack Dyer

Founder at Leftshifted | Contract Data Consultant
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It’s incredibly important to apply the same processing to your scoring / prediction data, as you do in training. If the data being fed into a model is different in scoring, to what a model was trained on, we can see wrong or unexpected results. We can even break the pipeline! Worse yet, the model gives bad results but we don’t get any obvious signs and use them. This goes for any normalisation and feature encoding; you should apply the same boundaries that you used in training. You could also plan for what you’ll do if you do observe new categorical elements or numerics outside the ranges you’ve trained models on. Do you want to use predictions based on this? Will your pipeline tolerate this?

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6 Feature engineering best practices

Feature engineering best practices are the guidelines or principles that you can follow to ensure that your feature engineering process is effective and efficient. Feature engineering best practices can include the following: understand your problem and data, use domain knowledge and linguistic features wisely, experiment with different features and techniques, evaluate and compare your features and models, and document and communicate your feature engineering process. By following these best practices, you can improve your feature engineering skills and outcomes.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Vishwa Patel

AI & Data Science, Manager at Charles Schwab | NLP Enthusiast
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In my opinion, enter some small snippets of code that shows users how they can use domain knowledge, or linguistics features in their feature engineering process.

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How do you incorporate domain knowledge and linguistic features into your feature engineering process?

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1 Domain knowledge

2 Linguistic features

3 Feature extraction

4 Feature selection

5 Feature engineering pipeline

6 Feature engineering best practices

7 Here’s what else to consider

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