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This paper describes CAMR, the transitionbased parser that we use in the SemEval-2016 Meaning Representation Parsing task. The main contribution of this paper is a description of the additional sources of information that we use as features in the parsing model to further boost its performance. We start with our existing AMR parser and experiment with three(More)
Most successful Entity Linking (EL) methods aim to link mentions to their referent entities in a structured Knowledge Base (KB) by comparing their respective contexts, often using similarity measures. While the KB structure is given, current methods have suffered from impoverished information representations on the mention side. In this paper, we(More)
The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable.(More)
In this paper we tackle a challenging name tagging problem in an emergent setting the tagger needs to be complete within a few hours for a new incident language (IL) using very few resources. Inspired by observing how human annotators attack this challenge, we propose a new expectation-driven learning framework. In this framework we rapidly acquire,(More)
People create morphs, a special type of fake alternative names, to achieve certain communication goals such as expressing strong sentiment or evading censors. For example, “Black Mamba”, the name for a highly venomous snake, is a morph that Kobe Bryant created for himself due to his agility and aggressiveness in playing basketball games. This paper presents(More)
We describe novel approaches to tackling the problem of natural language processing for low-resource languages. The approaches are embodied in systems for name tagging and machine translation (MT) that we constructed to participate in the NIST LoReHLT evaluation in 2016. Our methods include universal tools, rapid resource and knowledge acquisition, rapid(More)
Internet users are keen on creating different kinds of morphs to avoid censorship, express strong sentiment or humor. For example, in Chinese social media, users often use the entity morph “方便面 (Instant Noodles)” to refer to “周永康 (Zhou Yongkang)” because it shares one character “康 (Kang)” with the well-known brand of instant noodles “康师傅 (Master Kang)”. We(More)
To extract English name mentions, we apply a linear-chain CRFs model trained from ACE 20032005 corpora (Li et al., 2012a). For Chinese and Spanish, we use Stanford name tagger (Finkel et al., 2005). We also encode several regular expression based rules to extract poster name mentions in discussion forum posts. In this year’s task, person nominal mentions(More)
The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining(More)
We argue that NLP researchers are especially well-positioned to contribute to the national discussion about gun violence. Reasoning about the causes and outcomes of gun violence is typically dominated by politics and emotion, and data-driven research on the topic is stymied by a shortage of data and a lack of federal funding. However, data abounds in the(More)