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A Word at a Time Computing Word Relatedness using Temporal Semantic Analysis Kira Radinsky, Eugene Agichteiny, Evgeniy Gabrilovichz, Shaul Markovitch

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Introduction 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)

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Introduction

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1. TSA

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Temporal Semantic Analysis 3 main steps: 1.Represent words as concepts vectors 1.Extract temporal dynamics for each concept 1.Extend static representation with temporal dynamics

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1. Words as concept vectors

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2. Temporal dynamics c : concept represented by a sequence of words wc 1,…,wc k 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 wc i, wc j Example: Great Fire of London

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2. Temporal dynamics t 1,…,t n : a sequence of consecutive discrete time points (days) H = D 1,…,D n : history represented by a set of document collections, where D i is a collection of documents associated with time t i the dynamics of a concept c is the time series of its frequency of appearance in H

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3. Extend static representation

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2. Using TSA for computing Semantic Relatedness

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

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Algorithm t 1, t 2 : words C(t 1 ) = {c 1,…,c n }and C(t 2 ) = {c 1,…,c m }: sets of concepts of t 1 and t 2 Q(c 1,c 2 ) : function that determines relatedness between two concepts c 1 and c 2 using their dynamics (time series)

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Algorithm

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

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Dynamic Time Warping 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

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3. Experimentations

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Experimentations: Setup New York Times archive (1863 – 2004) Each day: average of 50 abstracts of article 1.42 Gb of texts 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)

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TSA vs. ESA

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TSA vs. Temporal Word Similarity

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Word Frequency Effects

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Size of Temporal Concept Vector

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Conclusion Two innovations: o Temporal Semantic Analysis o A new 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

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