The detection of vague, speculative, or otherwise uncertain language has been performed in the encyclopedic, political, and scientific domains yet left relatively untouched in finance. However, the latter benefits from public sources of big financial data that can be linked with extracted measures of linguistic uncertainty as a mean of extrinsic model validation. Doing so further helps in understanding how the linguistic uncertainty of financial disclosures might induce financial uncertainty to the market. To explore this field, we use term weighting methods to detect linguistic uncertainty in a large dataset of financial disclosures. As a baseline, we use an existing dictionary of financial uncertainty triggers; furthermore, we retrieve related terms in specialized word embedding models to automatically expand this dictionary. Apart from an industry-agnostic expansion, we create expansions incorporating industry-specific jargon. In a set of cross-sectional event study regressions, we show that the such enriched dictionary explains a significantly larger share of future volatility, a common financial uncertainty measure, than before. Furthermore, we show that—different to the plain dictionary—our embedding models are well suited to explain future analyst forecast uncertainty. Notably, our results indicate that enriching the dictionary with industry-specific vocabulary explains a significantly larger share of financial uncertainty than an industry-agnostic expansion.