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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)
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 Disambigua-tion systems in a setting where instances may be(More)
Up to now, work on semantic relations has fo-cused 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)
Wikipedia is a collaborative setting with both combative and cooperative editing. We propose a new method for investigating the types of editor interactions using a novel representation of Wikipedia's revision history as a temporal, bi-partite network with multiple node and edge types for users and revisions. From this representation we identify significant(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)
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)
This paper introduces a new SemEval task on Cross-Level Semantic Similarity (CLSS), which measures the degree to which the meaning of a larger linguistic item, such as a paragraph, is captured by a smaller item, such as a sentence. High-quality data sets were constructed for four comparison types using multi-stage annotation procedures with a graded scale(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)
Word Sense Induction (WSI) is an unsu-pervised 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 rein-terpret graph-based WSI as community detection , a well studied problem in network science. The(More)