  The conceptualization of a claim lies at the core of argument mining. The
segregation of claims is complex, owing to the divergence in textual syntax and
context across different distributions. Another pressing issue is the
unavailability of labeled unstructured text for experimentation. In this paper,
we propose LESA, a framework which aims at advancing headfirst into expunging
the former issue by assembling a source-independent generalized model that
captures syntactic features through part-of-speech and dependency embeddings,
as well as contextual features through a fine-tuned language model. We resolve
the latter issue by annotating a Twitter dataset which aims at providing a
testing ground on a large unstructured dataset. Experimental results show that
LESA improves upon the state-of-the-art performance across six benchmark claim
datasets by an average of 3 claim-F1 points for in-domain experiments and by 2
claim-F1 points for general-domain experiments. On our dataset too, LESA
outperforms existing baselines by 1 claim-F1 point on the in-domain experiments
and 2 claim-F1 points on the general-domain experiments. We also release
comprehensive data annotation guidelines compiled during the annotation phase
(which was missing in the current literature).
