The other articles in the NL chapter of the Handbook include a historical sketch of machine translation from one language to another, which was the subject of the very earliest ideas about processing language with computers; technical articles on some of the grammars and parsing techniques that AI researchers have used in their programs; and an article on text generation, the creation of sentences by the program. Finally, there are several articles describing the NL programs themselves: the early systems of the 1960s and the major research projects of the last decade, including Wilks'S machine translation system, Winograd's SHRDLU, Woods's LUNAR, Schank's MARGIE, SAM, and PAM, and Hendrix's LIFER. Two other chapters of the Handbook are especially relevant to NL research. Speech understanding research attempts to build computer interfaces that understand spoken language. In the 197Os, speech and natural language understanding research were often closely linked.
Natural Language Processing (NLP) is a messy and difficult affair to handle. Word embeddings/representations – ever since they came in with great work of Mikolov et al, they have been revolutionary to say the least. The concept itself is very intuitive and motivates deeper understanding fora wide range of applications. The main advantage of the distributed representations is that similar words are close in the vector space, which makes generalization to novel patterns easier and model estimation more robust. Distributed vector representation is showed to be useful in many natural language processing applications such as Named Entity Recognition (NER), Word Sense Disambiguation (WSD), parsing, tagging and machine translation.
A huge amount of information is stored or communicated in the form of natural language. But it is difficult to make use of this information without asking people to read or listen it all. In our European research centre in Grenoble (France), we teach computers to read, understand and act. Our research in natural language processing (NLP) makes this information accessible, but also comprehensible, integrated, and actionable. Our algorithms and models are used in text analytics applications for healthcare, litigation, automation and finance.
However, besides co-occurrence, there is other valuable lexical, syntactic and semantic information in training corpora. For example, named entities, such as names, locations and organizations, could contain conceptual information. Sentence order and proximity between sentences would allow models to learn structure-aware representations. What's more, semantic similarity at the document level or discourse relations among sentences could train the models to learn semantic-aware representations. Hypothetically speaking, would it be possible to further improve performance if the model was trained to constantly learn a larger variety of tasks?
Have you ever wondered how your personal assistant (e.g: Siri) is built? Do you want to build your own? Let's talk about Natural Language Processing. NLP is an interdisciplinary field concerned with the interactions between computers and human natural languages (e.g: English) -- speech or text. Okay, now we get it, NLP plays a major role in our daily computer interactions, let's see more business-related NLP use-cases: NLP is divided into two fields: Linguistics and Computer Science.