Biohydrogen: Opportunities and challenges as an alternative energy resource

As the energy demand is continuously rising with the increase in population, the use of fossil fuels is also increasing at the same rate. These fossil fuels release greenhouse gases (GHG) which are harmful to human health and our environmental health and these fuels are also expected to exhaust in the near future. This eventually has led to an emerging need to shift to a more reliable, sustainable, clean energy source. Biohydrogen as fuel is a potential alternative, as hydrogen has proved to be one such fuel which has the potential to replace fossil fuels. There is a need to produce it in a clean, sustainable way to compete with the fuels that are being used currently. The hydrogen which is produced biologically is known as biohydrogen. Microorganisms also play a huge role in the process of hydrogen generation by virtue of their natural mechanism. Hydrogen can be produced biologically using approaches like biophotolysis (direct and indirect), fermentation (dark and photo) and microbial electrolysis cell (MEC). Among all, dark fermentation seems to be the most efficient when compared to other procedures. The challenges currently being faced with this technology are the yield of hydrogen, the high cost of the reactor and system efficiency. This technology still needs a lot of research and improvement to replace fossil fuels entirely

Named Entity Recognition in Indian court judgments

Identification of named entities from legal texts is an essential building block for developing other legal Artificial Intelligence applications. Named Entities in legal texts are slightly different and more fine-grained than commonly used named entities like Person, Organization, Location etc. In this paper, we introduce a new corpus of 46545 annotated legal named entities mapped to 14 legal entity types. The Baseline model for extracting legal named entities from judgment text is also developed.Comment: to be published in NLLP 2022 Workshop at EMNL

Corpus for Automatic Structuring of Legal Documents

In populous countries, pending legal cases have been growing exponentially. There is a need for developing techniques for processing and organizing legal documents. In this paper, we introduce a new corpus for structuring legal documents. In particular, we introduce a corpus of legal judgment documents in English that are segmented into topical and coherent parts. Each of these parts is annotated with a label coming from a list of pre-defined Rhetorical Roles. We develop baseline models for automatically predicting rhetorical roles in a legal document based on the annotated corpus. Further, we show the application of rhetorical roles to improve performance on the tasks of summarization and legal judgment prediction. We release the corpus and baseline model code along with the paper.Comment: Accepted at LREC 2022, 10 Pages (8 page main paper + 2 page references

SemEval 2023 Task 6: LegalEval -- Understanding Legal Texts

In populous countries, pending legal cases have been growing exponentially. There is a need for developing NLP-based techniques for processing and automatically understanding legal documents. To promote research in the area of Legal NLP we organized the shared task LegalEval - Understanding Legal Texts at SemEval 2023. LegalEval task has three sub-tasks: Task-A (Rhetorical Roles Labeling) is about automatically structuring legal documents into semantically coherent units, Task-B (Legal Named Entity Recognition) deals with identifying relevant entities in a legal document and Task-C (Court Judgement Prediction with Explanation) explores the possibility of automatically predicting the outcome of a legal case along with providing an explanation for the prediction. In total 26 teams (approx. 100 participants spread across the world) submitted systems paper. In each of the sub-tasks, the proposed systems outperformed the baselines; however, there is a lot of scope for improvement. This paper describes the tasks, and analyzes techniques proposed by various teams.Comment: 13 Pages (9 Pages + References), Accepted at SemEval 202