Natural Language Processing

Natural language processing (NLP) is a field of computer science and linguistics concerned with the
interactions between computers and human (natural) languages. In theory, natural-language processing
is a very attractive method of human-computer interaction. Natural language understanding is
sometimes referred to as an AI-complete problem, because natural-language recognition seems to
require extensive knowledge about the outside world and the ability to manipulate it.

NLP has significant overlap with the field of computational linguistics, and is often considered a sub-field
of artificial intelligence.

Modern NLP algorithms are grounded in machine learning, especially statistical machine learning.
Research into modern statistical NLP algorithms requires an understanding of a number of disparate
fields, including linguistics, computer science, and statistics. For a discussion of the types of algorithms
currently used in NLP, see the article on pattern recognition.

Major tasks in NLP

The following is a list of some of the most commonly researched tasks in NLP. Note that 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. What distinguishes these tasks from other potential
and actual NLP tasks is not only the volume of research devoted to them but the fact that for
each one there is typically a well-defined problem setting, a standard metric for evaluating the
task, standard corpora on which the task can be evaluated, and competitions devoted to the
specific task.

 Automatic summarization: Produce a readable summary of a chunk of text. Often used to

provide summaries of text of a known type, such as articles in the financial section of a
newspaper.
 Coreference resolution: Given a sentence or larger chunk of text, determine which words
("mentions") refer to the same objects ("entities"). Anaphora resolution is a specific
example of this task, and is specifically concerned with matching up pronouns with the
nouns or names that they refer to. The more general task of coreference resolution also
includes identify so-called "bridging relationships" involving referring expressions. For
example, in a sentence such as "He entered John's house through the front door", "the
front door" is a referring expression and the bridging relationship to be identified is the
fact that the door being referred to is the front door of John's house (rather than of some
other structure that might also be referred to).
 Discourse analysis: This rubric includes a number of related tasks. One task is identifying
the discourse structure of connected text, i.e. the nature of the discourse relationships
between sentences (e.g. elaboration, explanation, contrast). Another possible task is
recognizing and classifying the speech acts in a chunk of text (e.g. yes-no question,
content question, statement, assertion, etc.).
 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.) in order to
solve properly.
 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,
however, such an approach is not possible, as each dictionary entry has thousands of
possible word forms.
 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). Note that, 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 word 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 refer to
names, and French and Spanish do not capitalize names that serve as adjectives.
 Natural language generation: Convert information from computer databases 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.
 Optical character recognition (OCR): Given an image representing printed text,
determine the corresponding text.
 Part-of-speech tagging: Given a sentence, determine the part of speech 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. Note that some languages have more such ambiguity than others. 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 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. In fact, perhaps surprisingly, for a typical sentence there may be thousands of
potential parses (most of which will seem completely nonsensical to a human).
 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?").
 Relationship extraction: Given a chunk of text, identify the relationships among named
entities (i.e. who is the wife of whom).
 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).
 Speech recognition: 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). In
natural speech there are hardly any pauses between successive words, and thus speech
segmentation is a necessary subtask of speech recognition (see below). Note also that in
most spoken languages, the sounds representing successive letters blend into each other
in a process termed coarticulation, so the conversion of the analog signal to discrete
characters can be a very difficult process.
 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.
 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 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.
 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 from an online
resource such as WordNet.

In some cases, sets of related tasks are grouped into subfields of NLP that are often considered
separately from NLP as a whole. Examples include:

 Information retrieval (IR): This is concerned with storing, searching and retrieving
information. It is a separate field within computer science (closer to databases), but IR
relies on some NLP methods (for example, stemming). Some current research and
applications seek to bridge the gap between IR and NLP.
 Information extraction (IE): This is concerned in general with the extraction of semantic
information from text. This covers tasks such as named entity recognition, coreference
resolution, relationship extraction, etc.
 Speech processing: This covers speech recognition, text-to-speech and related tasks.