New Frontiers in Natural Language Processing: Beyond Symbols, Towards Vector Space Models

Dear Colleagues,

Natural language processing (NLP) has been one of the most important fields in artificial intelligence from its early years. The ability of automatic language processing in a machine plays a key role in communication with, and understanding of, automatic machines. Recently, new ways to process language are emerging because of the affordable high-computational power available nowadays, and the large amount of unstructured data available on-line.

Traditionally, words and sentences have been treated as symbols and symbol sequences. One of the most promising lines developed in recent years is to replace symbols by vector space representations, allowing the encoding of words, phrases, and even sentences in this vector space. On top of this vector space representation, probabilistic models can be learned to do sentence probability computation, sentence classification, and translation among others. Neural networks language models are an example of models that learn smooth probability functions over vector space representations of words, but other new possibilities are being studied. This new frontier has shown success in different topics related with NLP, and this Special Issue opens discussion regarding these new possibilities.

Francisco Zamora-Martínez
Guest Editor

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