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

LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content