![Chunk tags IOB format
[('Chancellor', 'NNP', 'O'),
('of', 'IN', 'B-PP'),
('the', 'DT', 'B-NP'),
('Exchequer', 'NNP', 'I-NP'),
('Nigel', 'NNP', 'B-NP'),
('Lawson', 'NNP', 'I-NP'),
("'s", 'POS', 'B-NP'),
('restated', 'VBN', 'I-NP'),
('commitment', 'NN', 'I-NP'),
('to', 'TO', 'B-PP'),
('a', 'DT', 'B-NP'),
('firm', 'NN', 'I-NP'),
('monetary', 'JJ', 'I-NP'),
('policy', 'NN', 'I-NP'),
('has', 'VBZ', 'B-VP'),
('helped', 'VBN', 'I-VP'),
('to', 'TO', 'I-VP'),
('prevent', 'VB', 'I-VP'),
('a', 'DT', 'B-NP'),
('freefall', 'NN', 'I-NP'),
('in', 'IN', 'B-PP'),
('sterling', 'NN', 'B-NP'),
('over', 'IN', 'B-PP'),
('the', 'DT', 'B-NP'),
('past', 'JJ', 'I-NP'),
('week', 'NN', 'I-NP'),
('.', '.', 'O')]
The chunk tags use the IOB format. This notation
represents Inside, Outside, and Beginning. The B-
prefix before a tag indicates it is the beginning of a
chunk, and I- prefix indicates that it is inside a chunk.
The O tag indicates that the token does not belong to
any chunk.](https://image.slidesharecdn.com/unit-3part1-221102113729-b38ff31c/85/unit-3-part-1-ppt-14-320.jpg)
unit -3 part 1.ppt
- 2. Understanding Language Syntax and Structure For any language, syntax and structure usually go hand in hand, where a set of specific rules, conventions, and principles govern the way words are combined into phrases; phrases get combines into clauses; and clauses get combined into sentences. constituents include words, phrases, clauses, and sentences.
- 4. “The brown fox is quick and he is jumping over the lazy dog”, A bunch of unordered words don’t convey much information
- 5. Types of Parsing techniques Typical parsing techniques for understanding text syntax are mentioned below. Parts of Speech (POS) Tagging Shallow Parsing or Chunking Constituency Parsing Dependency Parsing
- 6. 1. PoS tagging sentence → clauses → phrases → words
- 7. Tagging Parts of Speech N(oun): This usually denotes words that depict some object or entity, which may be living or nonliving. Some examples would be fox , dog , book , and so on. The POS tag symbol for nouns is N. V(erb): Verbs are words that are used to describe certain actions, states, or occurrences. There are a wide variety of further subcategories, such as auxiliary, reflexive, and transitive verbs (and many more). Some typical examples of verbs would be running , jumping , read , and write . The POS tag symbol for verbs is V. Adj(ective): Adjectives are words used to describe or qualify other words, typically nouns and noun phrases. The phrase beautiful flower has the noun (N) flower which is described or qualified using the adjective (ADJ) beautiful . The POS tag symbol for adjectives is ADJ .
- 8. Adv(erb): Adverbs usually act as modifiers for other words including nouns, adjectives, verbs, or other adverbs. The phrase very beautiful flower has the adverb (ADV) very , which modifies the adjective (ADJ) beautiful , indicating the degree to which the flower is beautiful. The POS tag symbol for adverbs is ADV. each POS tag like the noun (N) can be further subdivided into categories like singular nouns (NN), singular proper nouns (NNP), and plural nouns (NNS).
- 9. The process of classifying and labeling POS tags for words called parts of speech tagging or POS tagging . POS tags are used to annotate words and depict their POS, which is really helpful to perform specific analysis, such as narrowing down upon nouns and seeing which ones are the most prominent, word sense disambiguation, and grammar analysis. both nltk and spacy which usually use the Penn Treebank notation for POS tagging.
- 11. 2. Shallow Parsing or Chunking Based on the hierarchy we depicted earlier, groups of words make up phrases. There are five major categories of phrases: Noun phrase (NP): These are phrases where a noun acts as the head word. Noun phrases act as a subject or object to a verb. Verb phrase (VP): These phrases are lexical units that have a verb acting as the head word. Usually, there are two forms of verb phrases. One form has the verb components as well as other entities such as nouns, adjectives, or adverbs as parts of the object. Adjective phrase (ADJP): These are phrases with an adjective as the head word. Their main role is to describe or qualify nouns and pronouns in a sentence, and they will be either placed before or after the noun or pronoun. Adverb phrase (ADVP): These phrases act like adverbs since the adverb acts as the head word in the phrase. Adverb phrases are used as modifiers for nouns, verbs, or adverbs themselves by providing further details that describe or qualify them. Prepositional phrase (PP): These phrases usually contain a preposition as the head word and other lexical components like nouns, pronouns, and so on. These act like an adjective or adverb describing other words or phrases.
- 13. conll2000 corpus for training our shallow parser model. This corpus is available in nltk with chunk annotations and we will be using around 10K records for training our model. 10900 48 (S Chancellor/NNP (PP of/IN) (NP the/DT Exchequer/NNP) (NP Nigel/NNP Lawson/NNP) (NP 's/POS restated/VBN commitment/NN) (PP to/TO) (NP a/DT firm/NN monetary/JJ policy/NN) (VP has/VBZ helped/VBN to/TO prevent/VB) (NP a/DT freefall/NN) (PP in/IN) (NP sterling/NN) (PP over/IN) (NP the/DT past/JJ week/NN) ./.)
- 14. Chunk tags IOB format [('Chancellor', 'NNP', 'O'), ('of', 'IN', 'B-PP'), ('the', 'DT', 'B-NP'), ('Exchequer', 'NNP', 'I-NP'), ('Nigel', 'NNP', 'B-NP'), ('Lawson', 'NNP', 'I-NP'), ("'s", 'POS', 'B-NP'), ('restated', 'VBN', 'I-NP'), ('commitment', 'NN', 'I-NP'), ('to', 'TO', 'B-PP'), ('a', 'DT', 'B-NP'), ('firm', 'NN', 'I-NP'), ('monetary', 'JJ', 'I-NP'), ('policy', 'NN', 'I-NP'), ('has', 'VBZ', 'B-VP'), ('helped', 'VBN', 'I-VP'), ('to', 'TO', 'I-VP'), ('prevent', 'VB', 'I-VP'), ('a', 'DT', 'B-NP'), ('freefall', 'NN', 'I-NP'), ('in', 'IN', 'B-PP'), ('sterling', 'NN', 'B-NP'), ('over', 'IN', 'B-PP'), ('the', 'DT', 'B-NP'), ('past', 'JJ', 'I-NP'), ('week', 'NN', 'I-NP'), ('.', '.', 'O')] The chunk tags use the IOB format. This notation represents Inside, Outside, and Beginning. The B- prefix before a tag indicates it is the beginning of a chunk, and I- prefix indicates that it is inside a chunk. The O tag indicates that the token does not belong to any chunk.
- 15. NGramTagChunker format NGramTagChunker that will take in tagged sentences as training input, get their (word, POS tag, Chunk tag) WTC triples, and train a BigramTagger with a UnigramTagger as the backoff tagger. The UnigramTagger , BigramTagger , and TrigramTagger are classes that inherit from the base class NGramTagger , which itself inherits from the ContextTagger class , which inherits from the SequentialBackoffTagger class .
- 16. Example
- 17. 3. Constituency Parsing Constituent-based grammars are used to analyze and determine the constituents of a sentence. phrase structure rules: They determine what words are used to construct the phrases or constituents. They determine how we need to order these constituents together.
- 18. A constituency parser can be built based on such grammars/rules, which are usually collectively available as context-free grammar (CFG) or phrase- structured grammar. The parser will process input sentences according to these rules, and help in building a parse tree.
- 19. The generic representation of a phrase structure rule is S → AB , which depicts that the structure S consists of constituents A and B , and the ordering is A followed by B . The phrase structure rule denotes a binary division for a sentence or a clause as S → NP VP where S is the sentence or clause, and it is divided into the subject, denoted by the noun phrase (NP) and the predicate, denoted by the verb phrase (VP).
- 20. using nltk and the StanfordParser here to generate parse trees.
- 21. The Stanford parser generally uses a PCFG (probabilistic context-free grammar) parser. A PCFG is a context-free grammar that associates a probability with each of its production rules. The probability of a parse tree generated from a PCFG is simply the production of the individual probabilities of the productions used to generate it.
- 23. 4. Dependency Parsing In dependency parsing, we try to use dependency- based grammars to analyze and infer both structure and semantic dependencies and relationships between tokens in a sentence.
- 25. Dependency Tag Description acl clausal modifier of a noun (adnominal clause) acl:relcl relative clause modifier advcl adverbial clause modifier advmod adverbial modifier advmod:emph emphasizing word, intensifier advmod:lmod locative adverbial modifier amod adjectival modifier appos appositional modifier aux auxiliary aux:pass passive auxiliary case case-marking cc coordinating conjunction cc:preconj preconjunct ccomp clausal complement clf classifier compound compound compound:lvc light verb construction compound:prt phrasal verb particle compound:redup reduplicated compounds compound:svc serial verb compounds conj conjunct cop copula csubj clausal subject csubj:pass clausal passive subject dep unspecified dependency det determiner det:numgov pronominal quantifier governing the case of the noun
- 26. The dependency tag det is pretty intuitive — it denotes the determiner relationship between a nominal head and the determiner. Usually, the word with POS tag DET will also have the det dependency tag relation. Examples include fox → the and dog → the. The dependency tag amod stands for adjectival modifier and stands for any adjective that modifies the meaning of a noun. Examples include fox → brown and dog → lazy. The dependency tag nsubj stands for an entity that acts as a subject or agent in a clause. Examples include is → fox and jumping → he. The dependencies cc and conj have more to do with linkages related to words connected by coordinating conjunctions . Examples include is → and and is → jumping.
- 27. The dependency tag aux indicates the auxiliary or secondary verb in the clause. Example: jumping → is. The dependency tag acomp stands for adjective complement and acts as the complement or object to a verb in the sentence. Example: is → quick The dependency tag prep denotes a prepositional modifier, which usually modifies the meaning of a noun, verb, adjective, or preposition. Usually, this representation is used for prepositions having a noun or noun phrase complement. Example: jumping → over. The dependency tag pobj is used to denote the object of a preposition . This is usually the head of a noun phrase following a preposition in the sentence. Example: over → dog.
- 29. solve “I prefer the morning flight through Denver.”