Written by AI: Natural Language Processing (NLP)

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Yujian Tang

The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. 1950s: The Georgetown experiment in 1954 involved fully automatic translation of more than sixty Russian sentences into English. The authors claimed that within three or five years, machine translation would be a solved problem.  However, real progress was much slower, and after the ALPAC report in 1966, which found that ten-year-long research had failed to fulfill the expectations, funding for machine translation was dramatically reduced. 1960s: When the “patient” exceeded the very small knowledge base, ELIZA might provide a generic response, for example, responding to “My head hurts” with “Why do you say your head hurts?”. 1970s:  During this time, the first many chatterbots were written (e.g., PARRY). 1980s: An important development (that eventually led to the statistical turn in the 1990s) was the rising importance of quantitative evaluation in this period. 1990s: As a result, a great deal of research has gone into methods of more effectively learning from limited amounts of data. 2000s: These algorithms take as input a large set of “features” that are generated from the input data. The following is a list of some of the most commonly researched tasks in natural language processing. A coarse division is given below. Given a sound clip of a person or people speaking, determine the textual representation of the speech.  This is the opposite of text to speech and is one of the extremely difficult problems colloquially termed “AI-complete” (see above). Speech segmentation Given a sound clip of a person or people speaking, separate it into words.  A subtask of speech recognition and typically grouped with it. Text-to-speech Given a text, transform those units and produce a spoken representation. For a language like English, this is fairly trivial, since words are usually separated by spaces. Sometimes this process is also used in cases like bag of words (BOW) creation in data mining. Lemmatization Lemmatization is another technique for reducing words to their normalized form. But in this case, the transformation actually uses a dictionary to map words to their actual form. Morphological segmentation Separate words into individual morphemes and identify the class of the morphemes. Part-of-speech tagging Given a sentence, determine the part of speech (POS) for each word. For example, “book” can be a noun (“the book on the table”) or verb (“to book a flight”); “set” can be a noun, verb or adjective; and “out” can be any of at least five different parts of speech. Stemming The process of reducing inflected (or sometimes derived) words to a base form (e.g., “close” will be the root for “closed”, “closing”, “close”, “closer” etc.). Stemming yields similar results as lemmatization, but does so on grounds of rules, not a dictionary. Grammar induction Generate a formal grammar that describes a language’s syntax. Given a chunk of text, find the sentence boundaries. Distributional semantics For example, German capitalizes all nouns, regardless of whether they are names, and French and Spanish do not capitalize names that serve as adjectives. It is especially useful for identifying trends of public opinion in social media, for marketing. Terminology extraction The goal of terminology extraction is to automatically extract relevant terms from a given corpus. Entity linking Relationship extraction semantic roles).Coreference resolution Anaphora resolution is a specific example of this task, and is specifically concerned with matching up pronouns with the nouns or names to which they refer. etc.).Implicit Recognizing textual entailment Given two text fragments, determine if one being true entails the other, entails the other’s negation, or allows the other to be either true or false. Topic segmentation and recognition Given a chunk of text, separate it into segments each of which is devoted to a topic, and identify the topic of the segment. Argument mining Book generation The first machine-generated book was created by a rule-based system in 1984 (Racter, The policeman’s beard is half-constructed). The first published work by a neural network was published in 2018, 1 the Road, marketed as a novel, contains sixty million words. Both these systems are basically elaborate but non-sensical (semantics-free) language models. The first machine-generated science book was published in 2019 (Beta Writer, Lithium-Ion Batteries, Springer, Cham). Question answering Given a human-language question, determine its answer. , it is possible to extrapolate future directions of NLP. systems)Most Cognition refers to “the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses.” For example, consider the English word “big”. 

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