请给我关于这篇文献Cleaning GeoNames Data: A Case Study for Natural Language Processing中3.4的原始内容
时间: 2024-05-25 16:19:35 浏览: 12
3.4 Feature Engineering
Feature engineering is the process of selecting and transforming raw data into features that can be used by a machine learning algorithm. In our case, we used various NLP techniques to extract features from the GeoNames data. We first extracted the name, feature class, and feature code of each GeoNames record. We then used a part-of-speech (POS) tagger to identify the parts of speech of each word in the name field. We also used a named entity recognizer (NER) to identify the entities in the name field, such as countries, cities, and rivers.
We then created several new features based on the extracted information. For example, we created a feature that indicated whether the record was a country or not. We also created features that indicated the number of words in the name field, the number of entities in the name field, and the average length of the words in the name field.
In addition to the NLP-based features, we also created several other features. For example, we created a feature that indicated the distance of each record from the equator, as this is known to be a strong predictor of climate and vegetation patterns. We also created features that indicated the population density and area of each record.
Finally, we used a feature selection algorithm to select the most important features for our machine learning algorithm. We used a random forest classifier, which is a type of ensemble learning algorithm that combines multiple decision trees to improve performance. We found that the most important features were the feature class, distance from the equator, population density, and number of entities in the name field.
Overall, our feature engineering process helped us to extract meaningful information from the raw GeoNames data and create features that were useful for our machine learning algorithm.