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A Word at a Time

A Word at a Time. Computing Word Relatedness using Temporal Semantic Analysis. Kira Radinsky , Eugene Agichteiny , Evgeniy Gabrilovichz , Shaul Markovitch. A rich source of information can be revealed by studying the patterns of word occurrence over time Example: “peace” and “war”

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A Word at a Time

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  1. A Word at a Time Computing Word Relatedness using Temporal Semantic Analysis Kira Radinsky, EugeneAgichteiny, EvgeniyGabrilovichz, ShaulMarkovitch

  2. A rich source of information can be revealed by studying the patterns of word occurrence over time • Example: “peace” and “war” • Corpus: New York Times over 130 years • Word <=> time series of its occurrence in NYT articles • Hypothesis:Correlation between 2 words time series • Semantic Relation • Proposed method: Temporal Semantic Analysis (TSA) Introduction

  3. Introduction

  4. Introduction

  5. 1. TSA

  6. 3 main steps: • Represent words as concepts vectors • Extract temporal dynamics for each concept • Extend static representation with temporal dynamics Temporal Semantic Analysis

  7. 1. Words as concept vectors

  8. c : concept represented by a sequence of words wc1,…,wck • d : a document • ε : proximity relaxation parameter (ε = 20 in the experiments) • c appears in d if its words appear in d with a distance of at most ε words between each pair wci, wcj • Example: “Great Fire of London” 2. Temporal dynamics

  9. t1,…,tn : a sequence of consecutive discrete time points (days) • H = D1,…,Dn: history represented by a set of document collections, where Di is a collection of documents associated with time ti • the dynamics of a concept c is the time series of its frequency of appearance in H 2. Temporal dynamics

  10. 3. Extend static representation

  11. 2. Using TSA for computing Semantic Relatedness

  12. Compare by weighted distance between time series of concept vectors • Combine it with the static semantic similarity measure Using TSA for computing Semantic Relatedness

  13. t1, t2 : words • C(t1) = {c1,…,cn}and C(t2) = {c1,…,cm}: sets of concepts of t1 and t2 • Q(c1,c2) : function that determines relatedness between two concepts c1 and c2 using their dynamics (time series) Algorithm

  14. Algorithm

  15. Pearson's product-moment coefficient: • A statistic method for measuring similarity of two random variables • Example: “computer” and “radio” Cross Correlation

  16. Measure similarity between 2 time series that may differ in time scale but similar in shape • Used in speech recognition • It defines a local cost matrix • Temporal Weighting Function Dynamic Time Warping

  17. 3. Experimentations

  18. New York Times archive (1863 – 2004) • Each day: average of 50 abstracts of article • 1.42 Gb of texts • 565 540 distinct words • A new algorithm to automatically benchmark word relatedness tasks • Same vector representation for each method tested • Comparison to human judgment (WS-353 and Amazon MTurk) Experimentations: Setup

  19. TSA vs. ESA

  20. TSA vs. Temporal Word Similarity

  21. Word Frequency Effects

  22. Size of Temporal Concept Vector

  23. Two innovations: • Temporal Semantic Analysis • Anew method for measuring semantic relatedness of terms • Many advantages (robustness, tunable, can be used to study language evolution over time) • Significant improvements in computing words relatedness Conclusion

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