Shallow parsing

chunkingChunking (computational linguistics)chunkerchunksshallow parse
Shallow parsing (also chunking, "light parsing") is an analysis of a sentence which first identifies constituent parts of sentences (nouns, verbs, adjectives, etc.) and then links them to higher order units that have discrete grammatical meanings (noun groups or phrases, verb groups, etc.).wikipedia
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Noun phrase

noun phrasesNPnominal phrase
Shallow parsing (also chunking, "light parsing") is an analysis of a sentence which first identifies constituent parts of sentences (nouns, verbs, adjectives, etc.) and then links them to higher order units that have discrete grammatical meanings (noun groups or phrases, verb groups, etc.).

Apache OpenNLP

OpenNLP
It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution.

Parsing

parserparseparsed
Shallow parsing (also chunking, "light parsing") is an analysis of a sentence which first identifies constituent parts of sentences (nouns, verbs, adjectives, etc.) and then links them to higher order units that have discrete grammatical meanings (noun groups or phrases, verb groups, etc.).
Shallow parsing aims to find only the boundaries of major constituents such as noun phrases.

Named-entity recognition

named entity recognitionentity extractionnamed entities
This segmentation problem is formally similar to chunking.

Sentence (linguistics)

sentencesentencesdeclarative sentence
Shallow parsing (also chunking, "light parsing") is an analysis of a sentence which first identifies constituent parts of sentences (nouns, verbs, adjectives, etc.) and then links them to higher order units that have discrete grammatical meanings (noun groups or phrases, verb groups, etc.).

Noun

nounssubstantiveabstract noun
Shallow parsing (also chunking, "light parsing") is an analysis of a sentence which first identifies constituent parts of sentences (nouns, verbs, adjectives, etc.) and then links them to higher order units that have discrete grammatical meanings (noun groups or phrases, verb groups, etc.).

Regular expression

regular expressionsregexregexp
While the most elementary chunking algorithms simply link constituent parts on the basis of elementary search patterns (e.g. as specified by Regular Expressions), approaches that use machine learning techniques (classifiers, topic modeling, etc.) can take contextual information into account and thus compose chunks in such a way that they better reflect the semantic relations between the basic constituents.

Machine learning

machine-learninglearningstatistical learning
While the most elementary chunking algorithms simply link constituent parts on the basis of elementary search patterns (e.g. as specified by Regular Expressions), approaches that use machine learning techniques (classifiers, topic modeling, etc.) can take contextual information into account and thus compose chunks in such a way that they better reflect the semantic relations between the basic constituents.

Topic model

topic modelingtopic modelling
While the most elementary chunking algorithms simply link constituent parts on the basis of elementary search patterns (e.g. as specified by Regular Expressions), approaches that use machine learning techniques (classifiers, topic modeling, etc.) can take contextual information into account and thus compose chunks in such a way that they better reflect the semantic relations between the basic constituents.

Natural language processing

NLPnatural languagenatural-language processing
It is a technique widely used in natural language processing.

Lexical analysis

tokenslexical analyzertoken
It is similar to the concept of lexical analysis for computer languages.

Second language

L2 speakersL2second-language
Under the name of the Shallow Structure Hypothesis, it is also used as an explanation for why second language learners often fail to parse complex sentences correctly.

Inside–outside–beginning (tagging)

Inside Outside Beginning
The IOB format (short for inside, outside, beginning) is a common tagging format for tagging tokens in a chunking task in computational linguistics (ex.

Conditional random field

conditional random fieldsCRF
Specifically, CRFs find applications in POS tagging, shallow parsing, named entity recognition, gene finding and peptide critical functional region finding, among other tasks, being an alternative to the related hidden Markov models (HMMs).

International Conference on Computational Linguistics and Intelligent Text Processing

International Conference on Intelligent Text Processing and Computational LinguisticsCICLingComputational Linguistics and Intelligent Text Processing
Their topics of interest include, but are not limited to: text processing, computational morphology, tagging, stemming, syntactic analysis, parsing and shallow parsing, chunking, recognizing textual entailment, ambiguity resolution, semantic analysis, pragmatics, lexicon, lexical resources, dictionaries and machine-readable dictionaries (MRD), grammar, anaphora resolution, word sense disambiguation (WSD), machine translation (MT), information retrieval (IR), information extraction (IE), document handling, document classification and text classification, text summarization, text mining (TM), opinion mining, sentiment analysis, plagiarism detection, and spell checking (spelling).

Automated planning and scheduling

automated planningplanningAI planning
An agent is not forced to plan everything from start to finish but can divide the problem into chunks.