Alyssa Mensch

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This paper introduces a novel generation system that composes humanlike descriptions of images from computer vision detections. By leveraging syntactically informed word co-occurrence statistics, the generator filters and constrains the noisy detections output from a vision system to generate syntactic trees that detail what the computer vision system sees.(More)
What do people care about in an image? To drive computational visual recognition toward more human-centric outputs, we need a better understanding of how people perceive and judge the importance of content in images. In this paper, we explore how a number of factors relate to human perception of importance. Proposed factors fall into 3 broad types: 1)(More)
What is the story of an image? What is the relationship between pictures, language, and information we can extract using state of the art computational recognition systems? In an attempt to address both of these questions, we explore methods for retrieving and generating natural language descriptions for images. Ideally, we would like our generated textual(More)
When people describe a scene, they often include information that is not visually apparent; sometimes based on background knowledge, sometimes to tell a story. We aim to separate visual text—descriptions of what is being seen—from non-visual text in natural images and their descriptions. To do so, we first concretely define what it means to be visual,(More)
In this paper, we present a pipeline for named entity extraction and linking that is designed specifically for noisy, grammatically inconsistent domains where traditional named entity techniques perform poorly. Our approach leverages a large knowledge base to improve entity recognition, while maintaining the use of traditional NER to identify mentions that(More)
In this paper, we explore the problem of recognizing named entities in microposts, a genre with notoriously little context surrounding each named entity and inconsistent use of grammar, punctuation, capitalization, and spelling conventions by authors. In spite of the challenges associated with information extraction from microposts, it remains an(More)
Criminal hackers often use social media networks to discuss cyberattacks, share strategies and tools, and identify potential victims for targeted attacks. Analysts examining these discussions can forward information about malicious activity to system administrators who can then detect, defend against, and recover from future attacks (Figure 1). For example,(More)
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