Wikipedia provides an enormous amount of background knowledge to reason about the semantic relatedness between two entities. In this work, we present a distributional semantics based approach for computing entity relatedness, and a focused related entities explorer based on this approach.

One question in word production is how the presence of a semantically related word affects the naming process. It has been suggested that semantic effects in picture-word interference tasks are a net result of both inhibitory and facilitatory processes that take place at different processing levels. Finkbeiner and Caramazza (2006) argued that masking distractor words removes the inhibitory comp...

This paper presents our approach to semantic relatedness and textual entailment subtasks organized as task 1 in SemEval 2014. Specifically, we address two questions: (1) Can we solve these two subtasks together? (2) Are features proposed for textual entailment task still effective for semantic relatedness task? To address them, we extracted seven types of features including text difference meas...

To judge how much a pair of words (or texts) are semantically related is a cognitive process. However, previous algorithms for computing semantic relatedness are largely based on co-occurrences within textual windows, and do not actively leverage cognitive human perceptions of relatedness. To bridge this perceptional gap, we propose to utilize free association as signals to capture such human p...

Morphological analysis as applied to English has generally involved the study of rules for inflections and derivations. Recent work has attempted to derive such rules from automatic analysis of corpora. Here we study similar issues, but in the context of the biological literature. We introduce a new approach which allows us to assign probabilities of the semantic relatedness of pairs of tokens ...

The quantification of lexical semantic relatedness has many applications in NLP, and many different measures have been proposed. We evaluate five of these measures, all of which use WordNet as their central resource, by comparing their performance in detecting and correcting real-word spelling errors. An information-content–based measure proposed by Jiang and Conrath is found superior to those ...

Wikipedia has been used as a knowledge source in many areas of natural language processing. As most studies only use a certain Wikipedia snapshot, the influence of Wikipedia’s massive growth on the results is largely unknown. For the first time, we perform an in-depth analysis of this influence using semantic relatedness as an example application that tests a wide range of Wikipedia’s propertie...

Many tasks in NLP stand to benefit from robust measures of semantic similarity for units above the level of individual words. Rich semantic resources such as WordNet provide local semantic information at the lexical level. However, effectively combining this information to compute scores for phrases or sentences is an open problem. Our algorithm aggregates local relatedness information via a ra...

Semantic attributes have been proposed to bridge the semantic gap between low-level feature representation and high-level semantic understanding of visual objects. Obtaining a good representation of semantic attributes usually requires learning from high-dimensional low-level features, which not only significantly increases the time and space requirement but also degrades the performance due to...

We propose and study a novel supervised approach to learning statistical semantic relatedness models from subjectively annotated training examples. The proposed semantic model consists of parameterized co-occurrence statistics associated with textual units of a large background knowledge corpus. We present an efficient algorithm for learning such semantic models from a training sample of relate...