Natural language processing (NLP) technologies and applications in legal text processing are gaining momentum. Being one of the most prominent tasks in NLP, named-entity recognition (NER) can substantiate a great convenience for NLP in law due to the variety of named entities in the legal domain and their accentuated importance in legal documents. However, domain-specific NER models in the legal domain are not well studied. We present a NER model for Turkish legal texts with a custom-made corpus as well as several NER architectures based on conditional random fields and bidirectional long-short-term memories (BiLSTMs) to address the task. We also study several combinations of different word embeddings consisting of GloVe, Morph2Vec, and neural network-based character feature extraction techniques either with BiLSTM or convolutional neural networks. We report 92.27% F1 score with a hybrid word representation of GloVe and Morph2Vec with character-level features extracted with BiLSTM. Being an agglutinative language, the morphological structure of Turkish is also considered. To the best of our knowledge, our work is the first legal domain-specific NER study in Turkish and also the first study for an agglutinative language in the legal domain. Thus, our work can also have implications beyond the Turkish language.

Soon after independence, India's northeastern region was swamped in a series of conflicts starting with the Naga secessionist movement in the 1950s, followed by others in the 1960s. The conflicts intensified and engulfed the entire region in the 1970s and 1980s. However, in the 1990s, following reclamation of ethnic identities amid gnawing scarcities, the conflicts slowly turned into internal feuds. Consequently, alliance and re-alliance among the ethnic groups transpired. In the 2000s, it finally led to the balkanization of ethnicity-based autonomy movements in the region. Unfortunately, the state's ad-hoc measures failed to contain protected conflicts and, instead, compounded the situation and swelled hybrid ethnic identities.

Nucleotide excision repair (NER) is a highly versatile and efficient DNA repair process, which is responsible for the removal of a large number of structurally diverse DNA lesions. Its extreme broad substrate specificity ranges from DNA damages formed upon exposure to ultraviolet radiation to numerous bulky DNA adducts induced by mutagenic environmental chemicals and cytotoxic drugs used in chemotherapy. Defective NER leads to serious diseases, such as xeroderma pigmentosum (XP). Eight XP complementation groups are known of which seven (XPA–XPG) are caused by mutations in genes involved in the NER process. The eighth gene, XPV, codes for the DNA polymerase ɳ, which replicates through DNA lesions in a process called translesion synthesis (TLS). Over the past decade, detailed structural information of these DNA repair proteins involved in eukaryotic NER and TLS have emerged. These structures allow us now to understand the molecular mechanism of the NER and TLS processes in quite some detail and we have begun to understand the broad substrate specificity of NER. In this review, we aim to highlight recent advances in the process of damage recognition and repair as well as damage tolerance by the XP proteins.