Temporal Logic in Natural Language Processing

Natural language processing would benefit from a better understanding of human processes: as it has been said by several researchers recently, traditional ma- chine learning

∗ Abbreviations: C, correct electromyographic activity latency; CS, correct – success trials; CT, correction time; EMG, electromyography; FP, foreperiod; I, incorrect

His research activity covers artificial in- telligence, natural language processing, intelligent interfaces, human-computer interaction, cognitive science, knowledge-based systems,

This work has been developed with the motivation of defining a formalism (OntoTimeFL) for annotating complex events in natural language texts and applying to

This module searches a file of code or documentation for blocks of text that look like an interactive Python session, of the form you have already seen many times in this book.

During the implementation of the above method, existing models for identifying the most informative components of the natural language text from the position of

Keywords: Natural Language Processing, Compositional Distributional Model of Meaning, Quantum Logic, Similarity Measures..

In this paper we show that is possible to represent NLP models such as Probabilistic Context Free Grammars, Probabilistic Left Corner Grammars and Hidden Markov Models