Natural language processing (NLP) is one of the most practical areas of AI today. This technology is the driving force behind chatbots, smart speakers and spell checkers, and it could go even further. Many law firms have begun to recognize NLP`s potential. Natural language processing (NLP)-based approaches have recently attracted the attention of legal systems in several countries. It is interesting to examine the diversity of legal systems that have not yet been taken into account. In particular, no works have been published for the legal system of the Republic of Turkey, which is codified in Turkish. We first review the state of the art of NLP in law, and then we examine the problem of predicting judgments for several different courts using different algorithms. This study is much broader than previous studies in terms of the number of different dishes and variety of algorithms it contains. Therefore, it provides a reference point and basis for further studies in this area. We also hope that the scope and systematic nature of the study can provide a framework that can be applied to the study of other legal systems. We present new results to predict the judgments of the Turkish Constitutional Court and the courts of appeal, using only factual descriptions and not seeing the actual judgments. The methods used are based on decision trees (DT), random forests (RF), support vector machines (SVMs) and state-of-the-art deep learning (DL) methods; in particular, closed recurrent units (GRUs), long-term memory networks (LSTM) and bidirectional LSTMs (BiLSTM), with the integration of an attention mechanism for each model.
Prediction results for all algorithms are presented in a comparative and detailed manner. We show that court outcomes in the Turkish legal system can be predicted with high accuracy, especially with deep learning-based methods. The results presented show a similar performance to previous work in the literature for other languages and legal systems. Early versions of NLP often involved a human controlling the machine by adding common rules and standards to the dataset. NLP`s ability to draw conclusions solely from raw data marked a breakthrough for technology. It allowed machines to make decisions based on the fluidity of how people communicate, rather than adhering to strict rules that were often broken. These advances have helped apply natural language processing in the legal industry. Other legal NLP models examine contracts for questionable terms. Some can analyze around 500 common terms and contract types in multiple languages. This analysis helps identify potential omissions, loopholes or small print that a lawyer`s client should be aware of. Some contract review programs can process documents in 20 languages and help lawyers around the world understand or draft contracts.
Others can automatically create templates based on a specific law, agreement, or company policy. These technologies save lawyers time and help them ensure accurate wording and syntax. This is how lawyers today use natural language processing. A typical strategy for new and small players seems to be to focus first on very specific types of documents, such as NDAs, real estate leases, and privacy policies, and then expand the scope of covered documents as the company gains customers and traction. Leverton, from DFKI (funded in 2012; Financing €15 million)19 focuses mainly on real estate documents. It is aimed at companies with large real estate portfolios and deals with contracts in 20 languages. Other smaller players include eBrevia (founded in 2012, with $4.3 million in funding), Eigen Technologies (founded in 2014, funding UKP13M), LegalSifter (founded in 2013, with $6.2 million in funding), and Luminance (founded in 2003, with $13 million in funding), but there are many more. LexCheck uses natural language processing to perform legal document checks that ensure stricter and less ambiguous contracts. To see how it works, request a demo or contact us by email at email@example.com. Almost all laws are expressed in natural language; Therefore, natural language processing (NLP) is a key component in understanding and predicting laws.
Natural language processing transforms unstructured text into a formal representation that computers can understand and analyze. This technology has already overlapped the law and is poised to see rapid innovation and widespread adoption. There are three reasons for this: (1) the number of machine-readable digitized legal data repositories is increasing; (2) Advances in NLP tools are driven by algorithmic and hardware improvements; and (3) there is great potential for significant improvement in the effectiveness of legal services due to inefficiencies in their current practice. With the help of AI legal research, lawyers can frame their requests in natural language as if they were addressed to a colleague. Instead of typing “Non-compete /s (restrictive or illegal) /s long,” a person could type “What is the time limit for non-contests in New Jersey?” Based on the context of the application and thousands of other related requests, the program would make predictions about exactly what the lawyer wants to find and suggest keywords to complete the search (e.g., “incriminating” and “non-competitive”). Artificial intelligence (AI) is changing the way the legal industry works. While the adoption of AI in law is still new, lawyers today have a variety of smart tools at their disposal. One of the most useful AI applications is natural language processing (NLP).
Natural language processing can help shorten these periods by streamlining the search process. NLP legal search engines can translate simple language into “legal language”, making it easier to find relevant documents and cases. More advanced NLP programs can search for concepts, not just specific keywords, and help lawyers find what they need faster. NLP learns human language, uses context and previous queries and results to predict what lawyers need in their research. A clear example of NLP is the use of Google Search. For example, if a user types “restaurant,” Google can automatically suggest “restaurants near me.” The more the user searches for Google, the more Google can predict what the user is looking for when they say “Stay…” If the user misspells “restaurants near me,” Google recognizes the spelling error and returns the correct search results. The same goes for AI in legal research. Like Google, NLP improves legal search results because lawyers use online search tools. Here are some ways AI legal research streamlines and simplifies legal research. Tags: Legal Tech, NLP, Natural Language Processing Law has language at its core, so it`s no surprise that software that works with natural language has long played a role in some areas of the legal profession.
But in recent years, interest in applying modern techniques to a wider range of problems has grown, so this article explores how natural language processing is used in the legal sector today. Ross Intelligence (founded in 2014, funded to the tune of $13.1 million), which uses IBM Watson, and vLex (founded in 1998, funded to the tune of 4 million euros) with a product called Vincent provide natural language query interfaces so that “you can ask your research questions as if you were talking to another lawyer”. It is difficult to overestimate the importance of wording and syntax in law. Any vagueness in a contract or other legal document can open the door to unintentional interpretations. Natural language processing can help lawyers avoid these mistakes when creating documents, protecting their clients and reputation. As mentioned earlier, some NLP-based automation tools can design basic versions of contracts. Other services can automatically organize and classify documents according to the language they contain. Automating these processes saves lawyers time, reduces stress and supports more clients. E-discovery is the process of identifying and collecting electronically stored information in response to a request for disclosure in connection with a dispute or investigation.
Given the hundreds of thousands of files that might reside on a typical hard drive, a key problem is separating that content into what is relevant (or “responsive” in domain terminology) and what is not. In a recent patent dispute with Apple, Samsung collected and processed approximately 3.6TB, or 11,108,653 documents; The cost of processing this evidence over a 20-month period has been estimated at over $13 million. The application of natural language processing and artificial intelligence in general in the legal profession is not new. The first online legal content search systems appeared in the 1960s and 1970s, and legal expert systems were a hot topic of discussion in the 1970s and 1980s (see, for example, Richard Susskind`s Expert Systems in Law, Oxford University Press, 1987). In recent years, however, interest in this field has increased significantly, including, predictably, a growing number of startups claiming to apply deep learning techniques in the context of specific legal applications. Ambiguity is an important issue in legal documents.