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Found 12 papers, 0 papers with code
The 2019 BBN Cross-lingual Information Retrieval System
In this paper, we describe a cross-lingual information retrieval (CLIR) system that, given a query in English, and a set of audio and text documents in a foreign language, can return a scored list of relevant documents, and present findings in a summary form in English.
Reformulating Information Retrieval from Speech and Text as a Detection Problem
In the IARPA MATERIAL program, information retrieval (IR) is treated as a hard detection problem; the system has to output a single global ranking over all queries, and apply a hard threshold on this global list to come up with all the hypothesized relevant documents.
What Set of Documents to Present to an Analyst?
We describe the human triage scenario envisioned in the Cross-Lingual Information Retrieval (CLIR) problem of the [REDUCT] Program.
Combining Word Embeddings and N-grams for Unsupervised Document Summarization
An improved sentence similarity graph is built and used in a submodular objective function for extractive summarization, which consists of a weighted coverage term and a diversity term.
MEM_GE: a new maximum entropy method for image reconstruction from solar X-ray visibilities
Although widely used in image deconvolution, this method has been formulated in radio astronomy for the analysis of observations in the spatial frequency domain, and an Interactive Data Language (IDL) code has been implemented for image reconstruction from solar X-ray Fourier data.
Image Deconvolution
Image Reconstruction
+1
Solar and Stellar Astrophysics
Instrumentation and Methods for Astrophysics
49N45, 94A08
Polygonal symplectic billiards
In this article, we study polygonal symplectic billiards.
Symplectic Geometry Dynamical Systems 37J10, 49M25
Desaturating EUV observations of solar flaring storms
Image saturation has been an issue for several instruments in solar astronomy, mainly at EUV wavelengths.
Statistical Machine Translation Features with Multitask Tensor Networks
We present a three-pronged approach to improving Statistical Machine Translation (SMT), building on recent success in the application of neural networks to SMT.
