NLP consultant

Natural Language Processing Consulting and Text Analysis

Fast Data Science is an AI consultancy focusing on natural language processing consulting (NLP). NLP can help organisations to simplify mission critical processes and discover insights from large volumes of text.

For example, you may want to summarise documents, identify companies, drugs, or locations mentioned in a text, predict risk of escalation from building reports, or retrieve documents from sensitive data using a semantic search. Whatever your NLP needs, please contact us to speak to an NLP consultant today.

The most valuable NLP consultancy engagements in our experience have been those where we combined natural language understanding with predictive modelling. For example, predicting the probability of a construction defect leading to litigation two years down the line.

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 as an NLP consultant. In 2018 he founded Fast Data Science to deliver NLP consulting services.

An NLP Consultant Explains:

When companies have large amounts of text documents (imagine a law firm’s case load, or regulatory documents in a pharma company), it can be tricky to get insights out of it. For example, you may want to know of the thousands of clinical trials you ran, how many resulted in a particular side effect, when that information is stored in a stack of documents and you don’t have time to read them all. Natural language processing allows us to automate this kind of task.

Fast Data Science - London

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Explainable model from an NLP consulting engagement

Below you can see a demonstration of an explainable AI model which we made in one of our NLP consulting engagments. The model classifiers documents as either HIV or TB. The clients wanted a printout of what led to the model’s decisions, for maximum explanability.

NLP Consulting: Explainable Naive Bayes Text Classifier

Classifies text as "HIV" or "TB" and highlights word contributions. This classifier was trained in Python on 39 clinical trial protocols in these classes for an NLP consultant engagement.

Enter a text to classify:

Classification result:

Click "Classify & Explain" to begin.

NLP Consultant's Word Contribution Analysis:

The NLP Consultants have shaded words in green if they favour the HIV class. Words shaded red favour the TB class.

The input text will be shown here, with words highlighted based on how much they contributed to the decision.

Examples of what an NLP consultant can do

Examples of business applications of natural language processing include:

Natural language understanding
Text generation including LLMs such as LLaMa or GPT-4: we’ve all used GPT in the browser. But NLP consultants can train custom LLMs using a domain-specific or language-specific corpus, use prompt engineering to get an LLM to perform a domain specific task such as answering questions about English insolvency law, or leverage the data returned by an LLM such as token scores to calculate perplexity, identify if a text has been written by an LLM, or detect plagiarism
Identify the author of an unknown text (forensic stylometry)
Natural language dialogue systems - for example, develop a multilingual chatbot specifically for a company and domain, with detailed knowledge of a particular product range. We can leverage state of the art large language models (LLMs) such as GPT-3, or even train our own LLM.
Text analysis - which molecules were used in a biosciences paper? How many subjects in a clinical trial? What percentage of doctors felt there was an interaction between Drug A and Drug B?
Multilingual models capable of handling survey data from different countries
Predictive models based on text input
Topic analysis – clustering, finding similar documents
Domain specific named entity recognition such as drug named entity recognition and linking, company named entity recognition, or country named entity recognition and linking.
Document classification: is an incoming email a request to change address, deregister a company, or a request for a VAT invoice? Is a customer complaint high risk and needs to be dealt with immediately, or lower priority (triage)?
Document-based recommender systems
Unstructured data analysis
Document anonymisation: can we remove all GDPR- or HIPAA-sensitive data from a PDF? Can we train a machine learning model from sensitive data?

What would an NLP consultant do in the healthcare domain?

AI and natural language processing are being increasingly adopted across the healthcare sector. This technology is sometimes called healthtech or MedTech. NLP is being used to compare and detect changes in clinical reports, extract clinical concepts such as MeSH terms from electronic medical records, and develop human-to-machine natural language dialogue systems to improve the healthcare experience. NLP Consulting helps identify growth areas related to AI.

We have worked on a number of projects in healthcare, including:

a model to predict the complexity of clinical trials from the trial protocol for Boehringer Ingelheim.
a model to predict the risk of a clinical trial from the trial protocol for the Gates Foundation. Published in Wood TA and McNair D. Clinical Trial Risk Tool: software application using natural language processing to identify the risk of trial uninformativeness. Gates Open Res 2023, 7:56 doi: 10.12688/gatesopenres.14416.1
a desktop application to analyse researchers’ outputs, fields, collaborations and affiliations using PubMed search result exports.
a model to identify researchers who have used open sourced molecules in their published research without attribution, also for Boehringer Ingelheim.

What technologies does an NLP consultant use?

We do a lot of natural language processing with Python. We have worked on a variety of NLP models, including:

Transformers, including custom trained transformers using libraries such as HuggingFace, Keras or spaCy
Large Language Models (LLMs) such as Open AI’s GPT-3.5, Google BARD, or similar
Bag of words, tf*idf, Jaccard similarity, 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
Google Cloud, AWS, Microsoft Azure, Azure Cognitive Services, etc

Natural Language Processing in Python and R

Our NLP consultants work with the following programming languages and frameworks:

Examples of past NLP consulting engagements

NLP consulting 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 (Boehringer Ingelheim)
a model to classify jobseekers’ CVs into industries and salary bands (CV-Library).
analysis of survey responses and deployment of public NLP dashboards allowing members of the public to analyse and explore text datasets (White Ribbon Alliance)