Using temporal language models for document dating
A statistical language model is a probability distribution over sequences of words. The language model provides context to distinguish between words and phrases that sound similar. For example, in American English , the phrases "recognize speech" and "wreck a nice beach" sound similar, but mean different things. Data sparsity is a major problem in building language models. Most possible word sequences are not observed in training.
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 Neural Language Modeling for Contextualized Temporal Graph Generation
Existing modeling tools provide direct access to the most current version of a model but very limited support to inspect the model state in the past. This typically requires looking for a model version usually stored in some kind of external versioning system like Git roughly corresponding to the desired period and using it to manually retrieve the required data. This approximate answer is not enough in scenarios that require a more precise and immediate response to temporal queries like complex collaborative co-engineering processes or runtime models. In this paper, we reuse well-known concepts from temporal languages to propose a temporal metamodeling framework, called TemporalEMF , that adds native temporal support for models. In our framework, models are automatically treated as temporal models and can be subjected to temporal queries to retrieve the model contents at different points in time. Behind the scenes, the history of a model is transparently stored in a NoSQL database. Modeling tools and frameworks have improved drastically during the last decade due to the maturation of metamodeling concepts and techniques .
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Document Dating is the problem of automatically predicting the date of a document based on its content. Date of a document, also referred to as the Document Creation Time DCT , is at the core of many important tasks, such as, information retrieval, temporal reasoning, text summarization, event detection, and analysis of historical text, among others. For example, in the following document, the correct creation year is
A language model is a statistical tool to predict words. Where weather models predict the 7-day forecast, language models try to find patterns in the human language. They are used to predict the spoken word in an audio recording, the next word in a sentence, and which email is spam. Images have dominated the field of A. Images were more accessible for modelling tasks because they are easier to label, and they have computer-interpretable information already encoded in them, pixels.
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