Evaluation of the current NLP status

In this project we want to evaluate the current state of NLP.

The following is a list of some of the most commonly researched tasks in natural language processing. Some of these tasks have direct real-world applications, while others more commonly serve as subtasks that are used to aid in solving larger tasks.

Syntax

• Grammar induction
  • Generate a formal grammar that describes a language's syntax.
• Lemmatization
  • The task of removing inflectional endings only and to return the base dictionary form of a word which is also known as a lemma.
• Morphological segmentation
  • Separate words into individual morphemes and identify the class of the morphemes. The difficulty of this task depends greatly on the complexity of the morphology (i.e. the structure of words) of the language being considered. English has fairly simple morphology, especially inflectional morphology, and thus it is often possible to ignore this task entirely and simply model all possible forms of a word (e.g. "open, opens, opened, opening") as separate words. In languages such as Turkish or Meitei, a highly agglutinated Indian language, however, such an approach is not possible, as each dictionary entry has thousands of possible word forms.
• Part-of-speech tagging
  • Given a sentence, determine the part of speech (POS) for each word. Many words, especially common ones, can serve as multiple parts of speech. 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. Some languages have more such ambiguity than others.[dubious – discuss] Languages with little inflectional morphology, such as English, are particularly prone to such ambiguity. Chinese is prone to such ambiguity because it is a tonal language during verbalization. Such inflection is not readily conveyed via the entities employed within the orthography to convey the intended meaning.
• Parsing
  • Determine the parse tree (grammatical analysis) of a given sentence. The grammar for natural languages is ambiguous and typical sentences have multiple possible analyses. Perhaps surprisingly, for a typical sentence, there may be thousands of potential parses (most of which will seem completely nonsensical to a human). There are two primary types of parsing, Dependency Parsing, and Constituency Parsing. Dependency Parsing focuses on the relationships between words in a sentence (marking things like Primary Objects and predicates), whereas Constituency Parsing focuses on building out the Parse Tree using a Probabilistic Context-Free Grammar (PCFG). See also: Stochastic grammar.
• Sentence breaking (also known as sentence boundary disambiguation)
  • Given a chunk of text, find the sentence boundaries. Sentence boundaries are often marked by periods or other punctuation marks, but these same characters can serve other purposes (e.g. marking abbreviations).
• Stemming
  • The process of reducing inflected (or sometimes derived) words to their root form. (e.g. "close" will be the root for "closed", "closing", "close", "closer" etc.).
• Word segmentation
  • Separate a chunk of continuous text into separate words. For a language like English, this is fairly trivial, since words are usually separated by spaces. However, some written languages like Chinese, Japanese and Thai do not mark word boundaries in such a fashion, and in those languages text segmentation is a significant task requiring knowledge of the vocabulary and morphology of words in the language. Sometimes this process is also used in cases like Bag of Words (BOW) creation in data mining.
• Terminology extraction
  • The goal of terminology extraction is to automatically extract relevant terms from a given corpus.

Semantics

• Lexical semantics
  • What is the computational meaning of individual words in context?
• Distributional semantics
  • How can we learn semantic representations from data?
• Machine translation
  • Automatically translate text from one human language to another. This is one of the most difficult problems, and is a member of a class of problems colloquially termed "AI-complete", i.e. requiring all of the different types of knowledge that humans possess (grammar, semantics, facts about the real world, etc.) to solve properly.
• Named entity recognition (NER)
  • Given a stream of text, determine which items in the text map to proper names, such as people or places, and what the type of each such name is (e.g. person, location, organization). Although capitalization can aid in recognizing named entities in languages such as English, this information cannot aid in determining the type of named entity, and in any case, is often inaccurate or insufficient. For example, the first letter of a sentence is also capitalized, and named entities often span several words, only some of which are capitalized. Furthermore, many other languages in non-Western scripts (e.g. Chinese or Arabic) do not have any capitalization at all, and even languages with capitalization may not consistently use it to distinguish names. For example, German capitalizes all nouns, regardless of whether they are names, and French and Spanish do not capitalize names that serve as adjectives.
• Natural language generation
  • Convert information from computer databases or semantic intents into readable human language.
• Natural language understanding
  • Convert chunks of text into more formal representations such as first-order logic structures that are easier for computer programs to manipulate. Natural language understanding involves the identification of the intended semantic from the multiple possible semantics which can be derived from a natural language expression which usually takes the form of organized notations of natural language concepts. Introduction and creation of language metamodel and ontology are efficient however empirical solutions. An explicit formalization of natural language semantics without confusions with implicit assumptions such as closed-world assumption (CWA) vs. open-world assumption, or subjective Yes/No vs. objective True/False is expected for the construction of a basis of semantics formalization.
• Optical character recognition (OCR)
  • Given an image representing printed text, determine the corresponding text.
• Question answering
  • Given a human-language question, determine its answer. Typical questions have a specific right answer (such as "What is the capital of Canada?"), but sometimes open-ended questions are also considered (such as "What is the meaning of life?"). Recent works have looked at even more complex questions.
• 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.
• Relationship extraction
  • Given a chunk of text, identify the relationships among named entities (e.g. who is married to whom).
• Sentiment analysis (see also multimodal sentiment analysis)
  • Extract subjective information usually from a set of documents, often using online reviews to determine "polarity" about specific objects. It is especially useful for identifying trends of public opinion in social media, for marketing.
• 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.
• Word sense disambiguation
  • Many words have more than one meaning; we have to select the meaning which makes the most sense in context. For this problem, we are typically given a list of words and associated word senses, e.g. from a dictionary or an online resource such as WordNet.

In the project we will also look into some programming languages frequently used for NLP tasks, like Python, Java, Julia, Haskell, and Lisp.

Source: https://en.wikipedia.org/wiki/Natural_language_processing#Major_evaluati...

Keywords