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How to apply AI in legal tech: what can it do for lawyers?

Artificial intelligence (AI) is one of the most revolutionary and fascinating technologies of our time. AI allows us to create models capable of performing highly creative tasks that assist professionals by providing ideas, inspiration and efficiency. But did you know that AI can also contribute directly to legal tech and help lawyers in their daily work? Join us in this article and discover the most interesting and useful use cases of AI for lawyers, from contract review to sentiment analysis. If you are a lawyer or you are interested in the world of law, don't miss this article. You'll be surprised what AI can do for you - read on!

10 AI use cases for lawyers

  1. Named Entity Recognition (NER)
  2. Text summarisation
  3. Natural language generation
  4. Sentiment analysis
  5. Machine translation
  6. Question answering
  7. Part-of-speech tagging (POS)
  8. Co-reference resolution
  9. Dependency parsing
  10. Risk monitoring and reporting

Hands of a lawyer in a suit touching a tablet. Bigle Legal article on use cases of AI in legal tech.

Named Entity Recognition (NER)

Named Entity Recognition (NER) is a natural language processing (NLP) task that consists of identifying and classifying named entities in a text, such as people, places, organisations, dates or quantities. The NER can help lawyers review external contracts and extract relevant metadata, such as the contracting parties, the subject matter of the contract, the main clauses, payment terms and so on. NER can also facilitate the tagging of legal documents according to their type or category, such as lawsuits, judgments, appeals or deeds.

Text summaries 

Text summaries are another PLN task that consists of generating a short, coherent text that captures the essential information of a longer text. This function helps lawyers review and synthesise large amounts of legal information, such as legislation, case law or doctrine. Text summarisation can also generate executive summaries of contracts or legal documents to facilitate understanding and analysis.

Natural language generation

Natural Language Generation (NLG) is the reverse of PLN: it is the process of creating text from structured or unstructured data. NLG can help lawyers generate legal content with quality and consistency, such as contracts, lawsuits, appeals or briefs. It can also customise the content according to the client's or recipient's preferences or needs.

Sentiment analysis

Sentiment analysis is a subcategory of text classification that involves determining the attitude or emotion expressed in a text as positive, negative or neutral. Sentiment analysis can help lawyers improve the wording of their contracts to avoid counterparty opposition or rejection in negotiation. Sentiment analysis can also detect potential conflicts or claims based on the tone or language used by the parties.

Automatic translation

Automatic translation provided by AI translates text from one language to another using computer algorithms. Automatic translation can help lawyers change the language of their documents with a single click, allowing them to work with international clients or colleagues without language barriers. Automatic translation can also facilitate legal research in other languages or jurisdictions.

Question answering

Question answering by AI consists of providing an accurate and concise answer to a question asked in natural language. Question answering can help lawyers improve their search engines and implement legal chatbots that can resolve queries or doubts internally in their company, as well as from their clients or potential clients. Question answering can also speed up the search for relevant information in legal databases.

Part-of-speech (POS) labelling 

Part-of-speech (POS) labelling is a PLN function that consists of assigning a grammatical category to each word in a text, such as noun, verb, adjective or adverb. POS can help lawyers analyse legal texts, extract relevant information and retrieve related information. POS can also improve the quality and consistency of texts generated by the GLN.

Co-reference resolution  

Co-reference resolution involves identifying and linking expressions that refer to the same object or entity in a text, such as pronouns, synonyms or appellatives. Co-reference resolution can help lawyers extract more accurate and complete information from legal texts, especially when multiple parties or entities are involved. Co-reference resolution can also improve comprehension and dialogue with legal chatbots.

Dependency parsing

Dependency parsing offered by AI represents the grammatical structure of a text by means of a directed graph, where the nodes are the words and the arcs are the syntactic relations between them. Dependency parsing can help lawyers analyse legal texts, extract relevant information and retrieve related information. Dependency parsing can also improve the quality and consistency of texts generated by GLN and Automatic translation

Risk monitoring and reporting

Risk monitoring and reporting is based on detecting and assessing the legal risks associated with an activity or business, such as contractual breaches, legal claims, administrative sanctions or reputational damage. Risk monitoring and reporting can help lawyers make informed decisions and prevent or mitigate potential negative consequences.

AI is a reality that is already transforming the world of law. Lawyers must be prepared to face the challenges and take advantage of the opportunities offered by this technology. AI can be an ally to improve client service, legal efficiency and legal quality. Are you ready to incorporate AI into your professional practice? Follow us closely on our LinkedIn and take a look at our AI website to follow the artificial intelligence we are developing.