This paper presents a number of experiments to model changes in a historical Portuguese corpus composed of literary texts for the purpose of temporal text classification. Algorithms were trained to classify texts with respect to their publication date taking into account lexical variation represented as word n-grams, and morphosyntactic variation represented by part-of-speech (POS) distribution. We report results of 99.8 accuracy using word unigram features with a Support Vector Machines classifier to predict the publication date of documents in time intervals of both one century and half a century. A feature analysis is performed to investigate the most informative features for this task and how they are linked to language change.
The most recent initiative on temporal text classification is the Semeval 2015 Task 7 Diachronic Text Evaluation' (DTE). Results and methods are described in detail in the shared task report @cite_0 . In this shared task the organizers proposed three sub-tasks, two of them consisted of temporal text classification, and a third one dealing with the recognition of time specific phrases. For this task, the organizers compiled and released a test set containing English journalistic texts from 1700 to 2010. Texts were labeled with their approximate publication date in coarse, medium and fine-grained intervals representing six, twelve and twenty years respectively. The task proved to be a very challenging one and the only team to participate in all three sub-tasks was the IXA team @cite_23 who used external resources such as Google N-grams and Wikipedia Entity Linking to accomplish the task. The best performing system in the DTE task was the UCD team @cite_16 who achieved 54.2
@cite_0 @cite_16 @cite_23
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In this paper we describe a novel task, namely the Diachronic Text Evaluation task. A corpus of snippets which contain relevant information for the time when the text was created is extracted from a large collection of newspapers published between 1700 and 2010. The task, subdivided in three subtasks, requires the automatic system to identify the time interval when the piece of news was written. The subtasks concern specific type of information that might be available in news. The intervals come in three grades: fine, medium and coarse according to their length. The systems participating in the tasks have proved that this a doable task with very interesting possible continuations. This paper presents our contribution to the SemEval-2015 Task 7. The task was subdivided into three subtasks that consisted of automatically identifying the time period when a piece of news was written (1,2) as well as automatically determining whether a specific phrase in a sentence is relevant or not for a given period of time (3). Our system tackles the resolution of all three subtasks. With this purpose in mind multiple approaches are undertaken that use resources such as Wikipedia or Google NGrams. Final results are obtained by combining the output from all approaches. The texts used for the task are written in English and range from the years 1700 to 2000. This paper describes the SemEval-2014, Task 7 on the Analysis of Clinical Text and presents the evaluation results. It focused on two subtasks: (i) identification (Task A) and (ii) normalization (Task B) of diseases and disorders in clinical reports as annotated in the Shared Annotated Resources (ShARe) 1 corpus. This task was a follow-up to the ShARe CLEF eHealth 2013 shared task, subtasks 1a and 1b, 2 but using a larger test set. A total of 21 teams competed in Task A, and 18 of those also participated in Task B. For Task A, the best system had a strict F1-score of 81.3, with a precision of 84.3 and recall of 78.6. For Task B, the same group had the best strict accuracy of 74.1. The organizers have made the text corpora, annotations, and evaluation tools available for future research and development at the shared task website. 3 This paper presents a novel approach to the task of temporal text classification combining text ranking and probability for the automatic dating of historical texts. The method was applied to three historical corpora: an English, a Portuguese and a Romanian corpus. It obtained performance ranging from 83 to 93 accuracy, using a fully automated approach with very basic features.