David Jurgens

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Social networks are often grounded in spatial locality where individuals form relationships with those they meet nearby. However, the location of individuals in online social networking platforms is often unknown. Prior approaches have tried to infer individuals’ locations from the content they produce online or their online relations, but often are limited(More)
Semantic similarity is an essential component of many Natural Language Processing applications. However, prior methods for computing semantic similarity often operate at different levels, e.g., single words or entire documents, which requires adapting the method for each data type. We present a unified approach to semantic similarity that operates at(More)
Up to now, work on semantic relations has focused on relation classification: recognizing whether a given instance (a word pair such as virus:flu) belongs to a specific relation class (such as CAUSE:EFFECT). However, instances of a single relation class may still have significant variability in how characteristic they are of that class. We present a new(More)
Geographically annotated social media is extremely valuable for modern information retrieval. However, when researchers can only access publicly-visible data, one quickly finds that social media users rarely publish location information. In this work, we provide a method which can geolocate the overwhelming majority of active Twitter users, independent of(More)
This paper presents the SemEval-2013 task on multilingual Word Sense Disambiguation. We describe our experience in producing a multilingual sense-annotated corpus for the task. The corpus is tagged with BabelNet 1.1.1, a freely-available multilingual encyclopedic dictionary and, as a byproduct, WordNet 3.0 and the Wikipedia sense inventory. We present and(More)
Geolocated social media data provides a powerful source of information about place and regional human behavior. Because little social media data is geolocation-annotated, inference techniques serve an essential role for increasing the volume of annotated data. One major class of inference approaches has relied on the social network of Twitter, where the(More)
Most work on word sense disambiguation has assumed that word usages are best labeled with a single sense. However, contextual ambiguity or fine-grained senses can potentially enable multiple sense interpretations of a usage. We present a new SemEval task for evaluating Word Sense Induction and Disambiguation systems in a setting where instances may be(More)
Word Sense Induction (WSI) is an unsupervised approach for learning the multiple senses of a word. Graph-based approaches to WSI frequently represent word co-occurrence as a graph and use the statistical properties of the graph to identify the senses. We reinterpret graph-based WSI as community detection, a well studied problem in network science. The(More)
Large-scale knowledge bases are important assets in NLP. Frequently, such resources are constructed through automatic mergers of complementary resources, such as WordNet and Wikipedia. However, manually validating these resources is prohibitively expensive, even when using methods such as crowdsourcing. We propose a cost-effective method of validating and(More)