Code to accompany the paper Semi-supervised machine learning with word embedding for classification in price statistics: Martindale et.al
Here we make available the code to perform the same analysis as is described in the paper along with a mock dataset.
The mock data is made by selecting the 100 most common words from the actual product names and then choosing between 5 and 15 words to form new product names. The resulting product names do not make sense as names but aim is for the mock data to be some what representative of the true data. In addition to the product name we also randomly select a broad and narrow retailer category for the product from those available in the real data.
All code in this project is written using Python 3.7 and the required python models can be installed using the requirements file using:
pip install -r requirements.txt
The code also makes use of the nltk tokenizer and to use this it is necessary to download the associated tokenizer dataset. In this work we use the punkt tokenizer. The notebook code checks for the presence of punkt and if it is not installed then it is downloaded. To download it yourself run:
import nltk
nltk.download('punkt')In order to run the pipeline use the ipython notebook. A static rendering of the notebook is also provided.
- clean_most_common.py - functions to fine the most common words in the text and select only products containing these words
- Label_matching.py - functions to perform fuzzy matching with fuzzywuzzy, edit distance and Jaccard ratio
- labelling_pipeline.py - Function to perform the matching and label propagation for 1 category item
- Word_vectors.py - Function to calculate the word vectors for the product names
- clothing_mock_data.csv - The mock data set to be labelled
- seed_labels.json - A json file containing a dictionary of starting labels for the fuzzy matching and label propagation for each category