In this paper, we focus on automatic extraction of statute facets from legal statutes such as Act documents. We define statute facets to be key specific aspects of a statute which can potentially be used in legal arguments. For example, Section 25F of the Industrial Disputes Act (India) contains statute facets such as workman, employer, retrenchment of workmen, continuous service for not less than one year, etc. Such statute facets are often used by lawyers as part of their argumentation and also by judges for deciding on a case. In this paper, we propose a weakly supervised technique for extracting such statute facets from legal text. We use dependency tree structure to extract candidate statute facets and use BM25 ranking function to determine statute-specificity of these candidates. We propose a set of facet types which enable us to realize the definition of statute facets in a more computational way. We use recent deep learning models in a few-shot setting to predict an appropriate facet type for each candidate. Only those candidates with high statute-specificity and for which a facet type is predicted with high confidence, are selected as acceptable statute facets. We evaluate the extracted statute facets through both direct and indirect evaluation as well as conduct a user-study to get validation and feedback from lawyers.

This work addresses the problem of encoding cognitive agents that are capable of complex reasoning beyond simple rules, within agent-based models (ABM). This is particularly important for social simulation where agents represent people. We provide a general solution to this problem through infrastructure that allows the integration of state-of-the-art Belief-Desire-Intention (BDI) and ABM systems. In this paper, we demonstrate how this infrastructure is being used to help emergency services in Australia plan for community evacuations.

Semantic similarity and relatedness measures play an important role in natural language processing applications.In this paper, we present the IndoWordNet::Similarity tool and interface, designed for computing the semantic similarity and relatedness between two words in IndoWordNet.A java based tool and a web interface have been developed to compute this semantic similarity and relatedness.Also, Java API has been developed for this purpose.This tool, web interface and the API are made available for the research purpose.

Detection of MultiWord Expressions (MWEs) is one of the fundamental problems in Natural Language Processing.In this paper, we focus on two categories of MWEs -Compound Nouns and Light Verb Constructions.These two categories can be tackled using knowledge bases, rather than pure statistics.We investigate usability of IndoWordNet for the detection of MWEs.Our IndoWordNet based approach uses semantic and ontological features of words that can be extracted from IndoWordNet.This approach has been tested on Indian languages viz., Assamese, Bengali, Hindi, Konkani, Marathi, Odia and Punjabi.Results show that ontological features are found to be very useful for the detection of light verb constructions, while use of semantic properties for the detection of compound nouns is found to be satisfactory.This approach can be easily adapted by other Indian languages.Detected MWEs can be interpolated into WordNets as they help in representing semantic knowledge.

Sudha Bhingardive, Dhirendra Singh, Rudramurthy V, Hanumant Redkar, Pushpak Bhattacharyya. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.

MATSim is a mature and powerful traffic simulator, used for large scale traffic simulations, primarily to assess likely results of various infrastructure or road network changes. More recently there has been work to extend MATSim to allow its use in applications requiring what has been referred to as “within day replanning”. In the work described here we have coupled MATSim with a BDI (Belief Desire Intention) system to allow both more extensive modelling of the agent's decision making, as well as reactivity to environmental situations. The approach used allows for all agents to be “intelligent” or for some to be “intelligent”/reactive, while others operate according to plans that are static within a single day. The former is appropriate for simulations such as a bushfire evacuation, where all agents will be reacting to the changing environment. The latter is suited to introducing agents such as taxis into a standard MATSim simulation, as they cannot realistically have a predetermined plan, but must constantly respond to the current situation. We have prototype applications for both bushfire evacuation and taxis. By extending the capabilities of MATSim to allow agents to respond intelligently to changes in the environment, we facilitate the use of MATSim in a wide range of simulation applications. The work also opens the way for MATSim to be used alongside other simulation components, in a simulation integrating multiple components.

The growing use of agent-based modelling and simulation for complex systems analysis has led to the availability of numerous published models. However, reuse of existing models in new simulations, for studying new problems, is largely not attempted. This is mainly because there is no systematic way of integrating agent-based models, that deals with the nuances of complex interactions and overlaps in concepts between components, in the shared environment. In this paper we present an open source framework, called OpenSim, that allows such integrated simulations to be built in a modular way, by linking together agent-based and other models. OpenSim is designed to be easy to use, and we give examples of the kinds of simulations we have built with this framework.