AI News, Introduction to Computational Linguistics and Dependency Trees in data science
- On Thursday, December 14, 2017
- By SHUBHAM JAIN
Introduction to Computational Linguistics and Dependency Trees in data science
Example – Problem with Neural Networks For example, a conversation system which is trained using recurrent neural network produces the following results in two scenarios: User: Hi, I took a horrible picture in a museum, can you tell where is it located?
Another way to represent this tree is following: -> community-NN (root) -> AnalyticsVidhya-NNP (nsubj) -> is-VBZ (cop) -> the-DT (det) -> largest-JJS (amod) -> scientists-NNS (pobj) -> of-IN (prep) -> data-NNS (case) -> and-CC (cc) -> provides-VBZ (conj) -> resources-NNS (dobj) -> best-JJS (amod) -> understanding-VBG (pcomp) -> for-IN (mark) -> data-NNS (dobj) -> and-CC (cc) -> analytics-NNS (conj) In this graphical representation of sentence, each term is represented in the pattern of “ -> Element_A – Element_B (Element_C) “.
Element_A represents the word, Element_B represents the part of speech tag of word, Element C represents the grammar relation among the word and its parent node, and the indentation before the symbol “ -> “ represents the level of a word in the tree.
These trees can be generated in python using libraries such as NLTK, Spacy or Stanford-CoreNLP and can be used to obtain subject-verb-object triplets, noun and verb phrases, grammar dependency relationships, and part of speech tags etc for example – -> scientists-NNS (pobj) -> of-IN (prep) -> data-NNS (nn) Grammar: <prep> <nn> <pobj> POS: IN – NNS – NNS Phrase: of data scientist -> understanding-VBG (pcomp) -> for-IN (prep) -> data-NNS (dobj) -> and-CC (cc) -> analytics-NNS (conj) Grammar: <dobj> <cc> <conj> POS: NNS – CC – NNS Phrase: data and analytics
Applications of Dependency Trees Named Entity Recognition Named-entity recognition (NER) is the process of locating and classifying named entities in a textual data into predefined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.
For example – Donald Trump will be visiting New Delhi next summer for a conference at Google -> visiting-VBG (root) -> Trump-NNP (nsubj) -> Donald-NNP (compound) -> will-MD (aux) -> be-VB (aux) -> Delhi-NNP (dobj) -> New-NNP (compound) -> summer-NN (tmod) -> next-JJ (amod) -> conference-NN (nmod) -> for-IN (case) -> a-DT (det) -> Google-NNP (nmod) -> at-IN (case) In the above trees, following noun phrases are are detected from the grammar relations of noun family such as: Trump <-> visiting <by> nsubj Delhi <-> visiting <by> dobj summer <-> Delhi <by> nmod conference <-> visiting <by> nmod Google <-> conference <by> nmod Named entities can be obtained by identifying the NNP (proper noun) part of speech tag of the root node.
Using the grammar rules, following named entities are obtained: <compound> <nsubj> : Donald Trump <compound> <dsubj> : New Delhi <nmod> : Google Other tasks such as phrase chunking and entity wise sentiment analysis can be performed using similar processes.
Np -> noun phrases nodes Hw -> headwords Rl -> grammar relations Lv -> level in the tree Pos -> part of speech tag Gen -> gender of part of speech tag Identify the tokens having the part of speech tag of pronoun family, (identified by PRP tag) and the Proper Nouns or Named entities.
3.1 map the tokens with same gender of pronoun and named entity 3.2 map the tokens with same singularity / plurality 3.3 map the tokens with same grammar relations This paper from Soochow University describes the use of dependency trees in coreference resolution.
>>> The, the, NOUN, nsubj >>> team, team, Noun, nsubh >>> Is, is, VERB, aux >>> Not, not, ADV, neg >>> Performing, perform, VERB, root >>> Well, well, ADV, advmod >>> In, in, ADP, prep >>> The, the, Noun, pobj >>> Match, match ,Noun, pobj </nmod></dsubj></compound></nsubj></compound></by></by></by></by></by></conj></cc></dobj></pobj></nn></prep>
Linguistic FeaturesUsing spaCy to extract linguistic features like part-of-speech tags, dependency labels and named entities, customising the tokenizer and working with the rule-based matcher.
It's also an incredibly fast way to gather first insights into your data – with about 1 million tweets, you'd be looking at a processing time of under 1 minute.
RelEx—Relation extraction using dependency parse trees
1 View largeDownload slide The work-flow of RelEx is subdivided into preprocessing, relation extraction and relation filtering leading from the original free-text sentences to directed, qualified relations.
In linguistics, the head of a phrase is the word that determines the syntactic category of that phrase.
The other elements of the phrase or compound modify the head, and are therefore the head's dependents. Headed phrases and compounds are called endocentric, whereas exocentric ('headless') phrases and compounds (if they exist) lack a clear head.
References Basic examples Examine the following expressions: big red dog birdsong The word dog is the head of big red dog since it determines that the phrase is a noun phrase, not an adjective phrase.
Because the adjectives big and red modify this head noun, they are its dependents. Similarly, in the compound noun birdsong, the stem song is the head since it determines the basic meaning of the compound.
For instance, substituting a single word in place of the phrase big red dog requires the substitute to be a noun (or pronoun), not an adjective.
These trees tend to be organized in terms of one of two relations: either in terms of the constituency relation of phrase structure grammars or the dependency relation of dependency grammars.
The a-trees identify heads by way of category labels, whereas the b-trees use the words themselves as the labels. The noun stories (N) is the head over the adjective funny (A).
In the dependency trees on the right, the noun projects only a single node, whereby this node dominates the one node that the adjective projects, a situation that also identifies the entirety as an NP.
The next four trees are additional examples of head-final phrases: The following six trees illustrate head-initial phrases: And the following six trees are examples of head-medial phrases: The head-medial constituency trees here assume a more traditional n-ary branching analysis.
head-final languages Some language typologists classify language syntax according to a head directionality parameter in word order, that is, whether a phrase is head-initial (= right-branching) or head-final (= left-branching), assuming that it has a fixed word order at all.
For instance, in the English possessive case, possessive marking ('s) appears on the dependent (the possessor), whereas in Hungarian possessive marking appears on the head noun: English: the man's house Hungarian: az ember ház-a (the man house-POSSESSIVE) Prosodic head In a prosodic unit, the head is the part that extends from the first stressed syllable up to (but not including) the tonic syllable.
A high head is the stressed syllable that begins the head and is high in pitch, usually higher than the beginning pitch of the tone on the tonic syllable.
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