This paper presents data from the United States Department of Defense Suicide Event Report System for years 2012–2015 to detail descriptive, longitudinal rate data and risk factor profiles associated with military suicide. The annual findings were aggregated from all U.S. military suicide deaths and suicide attempts. Data elements included the most common method of suicide (firearms), most common behavioral health diagnoses (substance abuse/dependence), common life stressors (failed intimate–partner relationships), and an individual’s history of operational deployment. Age- and sex-adjusted rates for the Services were compared with rates for the U.S. adult population. Results showed that the current reporting period (2015) is similar to patterns that have been observed over the preceding years and to patterns reported in the overall U.S. adult population. Suicide rates remain elevated but stable for both the Active and Reserve Components of the Military Services compared to historical levels observed prior to 2003. Finally, we discuss common errors and misinterpretations that can occur when analyzing surveillance data.

No abstract

Meaning Representation (AMR) parsing aims at abstracting away from the syntactic realization of a sentence, and denoting only its meaning in a canonical form. As such, it is ideal for paraphrase detection, a problem in which one is required to specify whether two sentences have the same meaning. We show that naïve use of AMR in paraphrase detection is not necessarily useful, and turn to describe a technique based on latent semantic analysis in combination with AMR parsing that significantly advances state-of-the-art results in paraphrase detection for the Microsoft Research Paraphrase Corpus. Our best results in the transductive setting are 86.6% for accuracy and 90.0% for F 1 measure.

No abstract

No abstract