If you are in a court setting and you hear a judge or magistrate talk about BERT then you need to know that they are referring to an “AI” program that determines the outcome of the case in question.
Documents that are lodged in many cases are extensive and it is fair to say that most Judges or Magistrates are unable to read every document, affidavit and annexure involved in every case.
In the same way that our check out staff are being replaced by self serve checkouts the Corporation Owned Courts are in the process of making Magistrates and Judges redundant.
What is BERT?
BERT, short for Bidirectional Encoder Representations from Transformers, is a machine learning (ML) framework for natural language processing. In 2018, Google developed this algorithm to improve contextual understanding of unlabeled text across a broad range of tasks by learning to predict text that might come before and after (bi-directional) other text.
Examples of BERT
BERT is used for a wide variety of language tasks. Below are examples of what the framework can help you do:
- Determine if a movie’s reviews are positive or negative
- Help chatbots answer questions
- Help predicts text when writing an email
- Can quickly summarize long legal contracts
- Differentiate words that have multiple meanings based on the surrounding text
Why is BERT important?
BERT converts words into numbers. This process is important because machine learning models use numbers, not words, as inputs. This allows you to train machine learning models on your textual data. That is, BERT models are used to transform your text data to then be used with other types of data for making predictions in a ML model.
Can BERT be used for topic modeling?
Yes. BERTopic is a topic modeling technique that uses BERT embeddings and a class-based TF-IDF to create dense clusters, allowing for easily interpretable topics while keeping important words in the topic descriptions.
What is Google BERT used for?
It’s important to note that BERT is an algorithm that can be used in many applications other than Google. When we talk about Google BERT, we are referencing its application in the search engine system. With Google, BERT is used to understand the intentions of the users’ search and the contents that are indexed by the search engine.
Is BERT a neural network?
Yes. BERT is a neural-network-based technique for language processing pre-training. It can be used to help discern the context of words in search queries.
Is BERT supervised or unsupervised?
BERT is a deep bidirectional, unsupervised language representation, pre-trained using a plain text corpus.
H2O.ai and BERT: BERT pre-trained models deliver state-of-the-art results in natural language processing (NLP). Unlike directional models that read text sequentially, BERT models look at the surrounding words to understand the context. The models are pre-trained on massive volumes of text to learn relationships, giving them an edge over other techniques. With GPU acceleration in H2O Driverless AI, using state-of-the-art techniques has never been faster or easier.