  Although board games and video games have been studied for decades in
artificial intelligence research, challenging word games remain relatively
unexplored. Word games are not as constrained as games like chess or poker.
Instead, word game strategy is defined by the players' understanding of the way
words relate to each other. The word game Codenames provides a unique
opportunity to investigate common sense understanding of relationships between
words, an important open challenge. We propose an algorithm that can generate
Codenames clues from the language graph BabelNet or from any of several
embedding methods - word2vec, GloVe, fastText or BERT. We introduce a new
scoring function that measures the quality of clues, and we propose a weighting
term called DETECT that incorporates dictionary-based word representations and
document frequency to improve clue selection. We develop BabelNet-Word
Selection Framework (BabelNet-WSF) to improve BabelNet clue quality and
overcome the computational barriers that previously prevented leveraging
language graphs for Codenames. Extensive experiments with human evaluators
demonstrate that our proposed innovations yield state-of-the-art performance,
with up to 102.8% improvement in precision@2 in some cases. Overall, this work
advances the formal study of word games and approaches for common sense
language understanding.
