Our main area of focus is natural language processing (NLP). The manager, Thomas Wood, studied a Masters in 2008 at Cambridge University in Computer Speech, Text and Internet Technology and since then he has been working exclusively in machine learning and mostly in NLP. In 2018 he founded Fast Data Science to deliver data science consultancy, focusing on NLP.
We have built NLP pipelines from scratch, and worked on natural language dialogue systems, document classifiers and text based recommender systems. For these tasks we have used both traditional machine learning techniques as well as the state of the art such as neural networks.
Natural Language Processing technologies that we use
We have worked on a variety of NLP models, including
- Bag of words, tf*idf, cosine similarity
- NLP pipelines, lemmatisation, parsers, chunkers
- Deep neural networks
- convolutional neural networks (text as well as images)
- RNN, LSTM
- Seq2seq, word2vec, doc2vec
- see a live demo of a CNN for author identification
- Clustering: Latent Dirichlet Allocation
- This is useful for extracting topics from a set of unstructured documents, for example legal documents, survey responses, factory error reports, etc.
- Search engines and search term recommenders
We work with the following programs
- Python NLTK
Examples of past Natural Language Processing projects
NLP projects we have worked on for major household names include
- a spoken dialogue system to control a smart home
- an unsupervised text analysis program to analyse text descriptions of manufacturing defects
- a model to classify jobseekers’ CVs into industries and salary bands.
- analysis of survey responses