ABSTRACT The development of computational models for legal reasoning has been a prominent research area for decades. Recently, however, there has been significant progress in enhancing the comprehension of legal contracts through advanced Natural Language Processing (NLP) techniques, particularly transformer‐based models. NLP plays a crucial role in identifying and analysing various types of legal contracts and extracting critical clauses from them. While rule‐based approaches were traditionally dominant, modern deep learning and transformer models are increasingly utilized. These models enable the learning of complex rules that are often difficult for humans to articulate using symbolic or rule‐based systems. Furthermore, ongoing research is exploring neuro‐symbolic models that aim to integrate the strengths of both symbolic and neural approaches. This survey paper identifies gaps in clause relationship linkage and neuro‐symbolic approaches. This survey reviews the techniques and datasets employed in NLP for legal contract analysis, summarizing recent advancements in this field. It emphasizes the evolution of NLP since the introduction of transformer architectures such as GPT‐4, Llama, BERT, XLNet, Gemini and other variants frequently used to address a range of NLP problems. Additionally, it provides an overview of state‐of‐the‐art research that has achieved notable performance in tasks such as clause extraction, document classification, risk assessment, legal question answering and more.

Location Based Social Networks (LBSNs) like Twitter, Foursquare or Instagram are a very good source of extracting human generated data in the form of check-ins, location and social relationships among users. Location data bridges the gap between the physical and digital worlds and enables a deeper understanding of users preference and behaviour. There are many underlying patterns in this type of dataset of human mobility which are utilised for applications like recommender systems. The current approaches involve extracting data from user-item rating, GPS trajectories, or other forms of data, whereas we focus on an integrated model considering factors like user's interest, social influence, time and proximity. There is metadata associated with this dataset which tells us about the whereabouts of the user, with emphasis on types of places. This paper proposes an integrated location recommendation model that considers users' interests, their friends influences, time and seasonality factors, and users' willingness to visit distant locations. We integrate all these parameters to generate a ranked list of locations which will be recommended to the user. Experiments are performed on a real-world dataset which show that our proposed model is effective in the stated conditions.

Scheduling a meeting is a difficult task for people who have overbooked calendars and many constraints. The complexity increases when the meeting is to be scheduled between parties who are situated in geographically distant locations of a city and have varying travel patterns. In this paper, we present a solution that identifies a common meeting point for a group of users who have temporal and spatial locality constraints that vary over time. The problem entails answering an Optimal Meeting Point (OMP) query in spatial databases. Under Euclidean space OMP query solution identification gets reduced to the problem of determining the geometric median of a set of points, a problem for which no exact solution exists. The OMP problem does not consider any constraints as far as availability of users is concerned whereas that is a key constraint in our setting. We therefore focus on finding a solution that uses daily movements information obtained from GPS traces for each user to compute stay points during various times of the day. We then determine interesting locations by analyzing the stay points across multiple users. The novelty of our solution is that the computations are done within the database by using various relational algebra operations in combination with statistical operations on the GPS trajectory data. This makes our solution scalable to larger groups of users and for multiple such requests. Once this list of stay points and interesting locations are obtained, we show that this data can be utilized to construct spatio-temporal graphs for the users that allow us efficiently decide a meeting place. We perform experiments on a real-world dataset and show that our method is effective in finding an optimal meeting point between two users.