We explore the relationship between context and happiness scores in political tweets using word co-occurrence networks, where nodes network are words, weight of an edge is number corpus for which two connected words co-occur. In particular, we consider with hashtags #imwithher #crookedhillary, both relating to Hillary Clinton's presidential bid 2016. then analyze properties conjunction by compa...

objective: patients with bipolar mood disorder constitute a relatively large number of individuals hospitalized in psychiatric hospitals. this disorder is highly co-morbid with other psychiatric disorders and may effect their clinical course. the goal of this study was to determine the co-occurrence rate of anxiety disorders and substance abuse with bipolar mood disorders and their impact on cl...

We address the problem of embedding entities into Euclidean space over time based on co-occurrence data. We extend the CODE model of Globerson et al. (2004) to a dynamic setting. This leads to a non-standard factored state space model with real-valued hidden parent nodes and discrete observation nodes. We investigate the use of variational approximations applied to the observation model that al...

The analysis of the co-occurrence patterns between words allows to understand the use (and meaning) of words that are associated with different relationships. The quantification of these standards is a powerful tool in modern lexicography as well as in the construction of basic linguistic resources for the processing of natural language, or learning a language. The aim of this project is to dev...

This paper proposes a general probabilistic setting that formalizes the notion of textual entailment. In addition we describe a concrete model for lexical entailment based on web co-occurrence statistics in a bag of words representation.

http://dx.doi.org/10.1016/j.patrec.2014.07.014 0167-8655/ 2014 Elsevier B.V. All rights reserved. q This paper has been recommended for acceptance by G. Borgefors. ⇑ Corresponding author at: Department of Computer Languages and Systems, Universitat Jaume I, 12071 Castelló de la Plana, Spain. E-mail addresses: [email protected] (P. Agustí), [email protected] (V.J. Traver), pla@uji. es (F. Pla). Pau Ag...

This paper presents a new corpus-based method for calculating the semantic similarity of two target words. Our method, called Second Order Co-occurrence PMI (SOC-PMI), uses Pointwise Mutual Information to sort lists of important neighbor words of the two target words. Then we consider the words which are common in both lists and aggregate their PMI values (from the opposite list) to calculate t...