Sumit Singh

• Ph.D
• Research Scholar at Indian Institute of Information Technology Allahabad

Our team, silp_nlp, participated in the LLMs4OL Challenge at ISWC 2024, engaging in all three tasks focused on ontology generation. The tasks include predicting the type of a given term, extracting a hierarchical taxonomy between two terms, and extracting non-taxonomy relations between two terms. To accomplish these tasks, we used machine learning...

Named entities are random, like emerging entities and complex entities. Most of the large language model’s tokenizers have fixed vocab; hence, they tokenize out-of-vocab (OOV) words into multiple sub-words during tokenisation. During fine-tuning for any downstream task, these sub-words (tokens) make the named entity classification more complex sinc...

Perceptual computing (Per-C) is a branch of CWW (Computing with words) that assist people in making subjective decisions. Their applications take linguistic inputs (i.e., words) from the user and return a linguistic output (i.e., word). The perception of these linguistic inputs suffers from uncertainties, for which IT2FSs (Interval Type-2 Fuzzy Set...

Natural Language Generation (NLG) is a crucial component of a Spoken Dialogue System. Its task is to generate utterances with intended attributes like fluency, variation, readability, scalability and adequacy. As the handcrafted models are rigid and tedious to build, people have proposed many statistical and deep-learning based models to bring abou...

Interval Type-2 fuzzy sets (IT2FSs) are used for modeling uncertainty and imprecision in a better way. In a conversation, the information given by humans are mostly words. IT2FSs can be used to provide a suitable mathematical representation of a word. The IT2FSs can be further processed using Computing with the words (CWW) engine to return the IT2F...