An acid test for any new Word Sense Disambiguation (WSD) algorithm is its performance against the Most Frequent Sense (MFS). The field of WSD has found the MFS baseline very hard to beat. Clearly, if WSD researchers had access to MFS values, their striving to better this heuristic will push the WSD frontier. However, getting MFS values requires sense annotated corpus in enormous amounts, which is out of bounds for most languages, even if their WordNets are available. In this paper, we propose an unsupervised method for MFS detection from the untagged corpora, which exploits word embeddings. We compare the word embedding of a word with all its sense embeddings and obtain the predominant sense with the highest similarity. We observe significant performance gain for Hindi WSD over the WordNet First Sense (WFS) baseline. As for English, the SemCor baseline is bettered for those words whose frequency is greater than 2. Our approach is language and domain independent.

This paper describes an integration framework that allows development of simulations where the cognitive reasoning and decision making is programmed and executed within an existing BDI (Belief, Desire, Intention) system, and the simulation is played out in an existing ABM (Agent Based Modelling) system. The framework has a generic layer which manages communication and synchronisation, a system layer which integrates specific BDI and ABM systems, and the application layer which contains the program code for a particular application. The code is available on GitHub: https://github.com/agentsoz/bdi-abm-integration

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