N Harsha Vardhan

N Harsha Vardhan, Manav Chaudhary

Sentence-level relation extraction (RE) aims to identify the relationship between 2 entities given a contextual sentence. While there have been many attempts to solve this problem, the current solutions have a lot of room to improve. In this paper, we approach the task of relationship extraction in the financial dataset REFinD. Our approach incorporates typed entity markers representations and various models finetuned on the dataset, which has allowed us to achieve an F1 score of 69.65% on the validation set. Through this paper, we discuss various approaches and possible limitations.

Jatin Agarwala, George Paul, Nemani Harsha Vardhan et al.

Music engagement spans diverse interactions with music, from selection and emotional response to its impact on behavior, identity, and social connections. Social media platforms provide spaces where such engagement can be observed in natural, unprompted conversations. Advances in natural language processing (NLP) and big data analytics make it possible to analyze these discussions at scale, extending music research to broader contexts. Reddit, in particular, offers anonymity that encourages diverse participation and yields rich discourse on music in ecological settings. Yet the scale of this data requires tools to extract, process, and analyze it effectively. We present Muse-it, a platform that retrieves comprehensive Reddit data centered on user-defined queries. It aggregates posts from across subreddits, supports topic modeling, temporal trend analysis, and clustering, and enables efficient study of large-scale discourse. Muse-it also identifies music-related hyperlinks (e.g., Spotify), retrieves track-level metadata such as artist, album, release date, genre, popularity, and lyrics, and links these to the discussions. An interactive interface provides dynamic visualizations of the collected data. Muse-it thus offers an accessible way for music researchers to gather and analyze big data, opening new avenues for understanding music engagement as it naturally unfolds online.