No abstract

Code-mixing is a linguistic phenomenon where multiple languages are used in the same occurrence that is increasingly common in multilingual societies. Codemixed content on social media is also on the rise, prompting the need for tools to automatically understand such content. Automatic Parts-of-Speech (POS) tagging is an essential step in any Natural Language Processing (NLP) pipeline, but there is a lack of annotated data to train such models. In this work, we present a unique language tagged and POS-tagged dataset of code-mixed English-Hindi tweets related to five incidents in India that led to a lot of Twitter activity. Our dataset is unique in two dimensions: (i) it is larger than previous annotated datasets and (ii) it closely resembles typical real-world tweets. Additionally, we present a POS tagging model that is trained on this dataset to provide an example of how this dataset can be used. The model also shows the efficacy of our dataset in enabling the creation of codemixed social media POS taggers. 3 This dataset also comprises of 772 Facebook posts and 762 WhatsApp messages 4 https:

We present a luminescent heterometallic multifunctional theranostic Eu–Pt2 complex, [{cis-PtCl2(DMSO)}2Eu(L)(H2O)], possessing two cytotoxic Pt-centers with four DNA-binding sites, which shows intracellular Eu-based red luminescence sensitized by platinum based MLCT excited states.

While growing code-mixed content on Online Social Networks (OSNs) provides a fertile ground for studying various aspects of code-mixing, the lack of automated text analysis tools render such studies challenging. To meet this challenge, a family of tools for analyzing code-mixed data such as language identifiers, partsof-speech (POS) taggers, chunkers have been developed. Named Entity Recognition (NER) is an important text analysis task which is not only informative by itself, but is also needed for downstream NLP tasks such as semantic role labeling. In this work, we present an exploration of automatic NER of code-mixed data. We compare our method with existing off-theshelf NER tools for social media content, and find that our systems outperforms the best baseline by 33.18 % (F 1 score).