The fast growth of web data and HUMINT reports has resulted in intelligence analysts having to rapidly monitor and analyze event information from massive amounts of unstructured textual data, in order to achieve and maintain persistent Situational Awareness (SA). A tool to automatically extract accurate events while distinguishing between real and hypothetical events, true and false events, frequently occurring and specific events, and past, ongoing and future events, will be useful. IAI proposes to develop an innovative Linguistic enriched And Scalable Event attribute extRaction System: LASER, to address this issue. LASER’s first innovation is that it integrates a rich set of specialized linguistic features exploited from lexical, syntactic and semantic levels into each of the four classification models for the event attributes of modality, polarity, genericity and tense. Secondly, it adopts robust classification models that can handle the unbalanced class problem in event attribute extraction. Finally, it incorporates three post-correction approaches to bring more performance gains to event attribute extraction. LASER also leverages a state-of-the-art event tagger that ensures that the four event attributes can be extracted on top of accurately extracted events. Furthermore, LASER uses powerful cloud computing architecture for information management and algorithmic computation. LASER leverages IAI’s data mining framework, ABMiner, to enable distributed processing of the computational intensive algorithms involved in event extraction and event attribute extraction. ABMiner provides more than 400 algorithms for both supervised and unsupervised learning aggregated from IAI’s machine learning projects and open source libraries such as Weka and RapidMiner. LASER has a wide range of potential applications in the military, as well as in commercial law enforcement, Homeland Security, financial and medical markets.