A burstiness-aware approach for document dating

Abstract

A large number of mainstream applications, like temporal search, event detection, and trend identification, assume knowledge of the timestamp of every document in a given textual collection. In many cases, however, the required timestamps are either unavailable or ambiguous. A characteristic instance of this problem emerges in the context of large repositories of old digitized documents. For such documents, the timestamp may be corrupted during the digitization process, or may simply be unavailable. In this paper, we study the task of approximating the timestamp of a document, so-called document dating. We propose a contentbased method and use recent advances in the domain of term burstiness, which allow it to overcome the drawbacks of previous document dating methods, e.g. the fix time partition strategy. We use an extensive experimental evaluation on different datasets to validate the efficacy and advantages of our methodology, showing that our method outperforms the state of the art methods on document dating.

Estimating spatiotemporal focus of documents using entropy with PMI

“…Niculae et al [10] provided a new approach to the task of classifying temporal texts by combining text order and probability in automatic dating of historical texts. In addition, a series of research articles have been published based on a heuristic method to automatically create or verify the temporal metadata of historical texts [11,12].…”

Dating Documents using Graph Convolution Networks

“…We draw inspiration from his work for exploiting temporal reasoning for document dating. Kotsakos et al (2014) propose a purely statistical method which considers lexical similarity alongside burstiness (Lappas et al, 2009) of terms for dating documents. To the best of our knowledge, NeuralDater, our proposed method, is the first method to utilize deep learning techniques for the document dating problem.…”

“…This embedding is then fed to a softmax classifier which produces a distribution over timestamps. Following prior research (Chambers, 2012;Kotsakos et al, 2014), we work with year granularity for the experiments in this paper. We, however, note that NeuralDater can be trained for finer granularity with appropriate training data.…”

“…Chambers (2012) shows significant improvement over these prior efforts through their discriminative models using handcrafted temporal features. Kotsakos et al (2014) Figure 2: Overview of NeuralDater. NeuralDater exploits syntactic and temporal structure in a document to learn effective representation, which in turn are used to predict the document time.…”

AD3: Attentive Deep Document Dater

“…A few generative approaches (de Jong et al, 2005b;Kanhabua and Nørvåg, 2008) as well as a discriminative model (Chambers, 2012) have been previously proposed for this task. Kotsakos et al (2014) employs term-burstiness resulting in improved precision on this task.…”