#phrase structure

[airasora]

Anyone out there who can recommend any good websites or apps to help with phrase structure or grammar?

[mediantriaad]

update: still not free of phrase structure!

[ayearinlanguage]

A Year in Language, Day 143: Phrase Structure Phrase Structure is a method of mapping the, well, structure of language. In a nutshell phrase structure looks at utterances like "the dog" and "a big red dog" and "some dog who's come all this way just to say hi" and categorizes all of them as "noun phrases" or NP's which, while differing in their composition, all can be moved as a unit to any slot a noun can occupy in the sentence (subject, object, etc.). Phrase structure diagrams are generally associated with the transformational grammar theories of Noam Chomsky. The importance of phrase structure is twofold. For one it is a great way to "chunk" languages, i.e. to properly assign which components of a sentence are units. For example in the sentence "an angry goose has followed me home", "an angry goose" is a unit, "me home" is not, even though on the surface they are all just adjacent words. This is most useful when looking at more complex or ambiguous statements like "I saw the man with the telescope". "with the telescope" can either be a part of the noun phrase beginning with "man" or a part of the verb phrase "saw". The other important element of phrase structure is that, through the magic of sentence diagrams, gives linguists a great way to visualize grammatical structure, especially transformations. You may recall from my earlier post on patterns that languages are generally understood to have an "underlying" form and a "surface" form, created by processing the underlying form through the grammar rules of a language. With tree diagrams and phrase structure linguists have been able to develop many theories, elegant and complex, surrounding these transformations. Sadly I cannot draw a sentence diagram on a facebook post, which is what this originally was. I could update it for this better suited platform, however I will, probably foolishly, still attempt to parse a sentence as I originally had to. Let's take "the big orange cat wants to eat hot lasagna tomorrow". Collectively this is, of course, a sentence, which we will designate S. S has two components: A noun phrase (NP1), and a verb phrase (VP1). Written in, ahem, shorthand we now have S[[NP1[the big orange cat]][VP1[wants to eat hot lasagna tomorrow]]] Why is everything not the subject categorized as part of VP1? Well, lots of reasons but for now just take my word that all the objects and other parts of speech are subservient to that verb. Lets reexamine NP1. We know in total it has four parts "the", "big", orange", and "cat". We just need to classify and organize them. Luckily for us English is a generally convenient language for this, it stacks most things to the left, more core elements pushing less core elements out. So the determiner "the" is first out. This divides NP1 into a determiner phrase (DP1) and another noun phrase (NP2), like so: NP1[[DP1[the]][NP2[big orange cat]]] NP2 itself consists of a, you guessed it, adjective phrase and yet another noun phrase. In phrase structure the stacking of adjectives matter, so we have to parse out "big" from "orange cat" and the "orange" from "cat". At long last we have: NP1[DP1[the]][NP2[AdjP1[big]][NP3[AdjP2[orange]][NP4[cat]]]] Bet you're wishing I could draw this like a tree, or maybe even that I would just stop. No dice buck-o. Its time for VP1. VP1 has three main components, the verb "wants to eat", the object "hot lasagna" and the adverb "tomorrow". As I said earlier everything after the verb is subservient to it, so lets start there. "wants to eat", now VP2, has two actual verbs, the modal verb "want" and "to eat" which has been pushed into its infinitive form by "want". This is our hint, along with Englishes love for stacking on the left, that "to eat" is under "want" thus: VP2[wants[VP3[to eat]]] The "to" might be parseable as another subservient preposition. For convenience and mercy's sake I will not. Everything that remains is a part of VP3. the word "tomorrow" is describing when the verb takes place, not the temporal status of the lasagna, so "hot lasagna" and "tomorrow" are both on equal status under VP3: VP2[wants[VP3[to eat[NP5[hot lasagna]][AdvP1[tomorrow]]]]] All that's left now is to split "hot" and "lasagna". We already know how to do that so lets just go for the finale: S[NP1[[DP1[the]][NP2[[Adj1[big]][NP3[[AdjP2[orange]]NP4[ cat]]]]]]][VP1[wants[VP2[to eat[NP5[[AdjP3[hot]][NP6[lasagna]]]][AdvP1[tomorrow]]]]]]] Yes, it's just that simple and elegant.

[ijtsrd]

Phrase Structure Identification and Classification of Sentences using Deep Learning

by Hashi Haris | Misha Ravi "Phrase Structure Identification and Classification of Sentences using Deep Learning" 

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, 

URL: https://www.ijtsrd.com/papers/ijtsrd23841.pdf

Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23841/phrase-structure-identification-and-classification-of-sentences-using-deep-learning/hashi-haris

international journals in engineering, engineering journal, paper publication for engineering,

Phrase structure is the arrangement of words in a specific order based on the constraints of a specified language. This arrangement is based on some phrase structure rules which are according to the productions in context free grammar. The identification of the phrase structure can be done by breaking the specified natural language sentence into its constituents that may be lexical and phrasal categories. These phrase structures can be identified using parsing of the sentences which is nothing but syntactic analysis. The proposed system deals with this problem using Deep Learning strategy. Instead of using Rule Based technique, supervised learning with sequence labelling is done using IOB labelling. This is a sequence classification problem which has been trained and modeled using RNN LSTM. The proposed work has shown a considerable result and can be applied in many applications of NLP. 

[kalimbamagic]

The System of the Mbira, Part 1: The chords and phrase structure behind most mbira music.

The image shows the basic form of the mbira system - or mbira cycle, as I often call it. Each of the colored dots actually represents an entire chord, and the note the dot falls on (Do = 1, Re = 2, etc) is the root note of the chord. The entire cycle is made up of four phrases, and each phrase is made up of three chords. When you get to the end of the fourth phrase, you jump right back to the start of the first phrase without dropping a beat - that is, you loop it.

Read the entire article, which includes sound files.

[rebelpoet88]

I can't figure out the phrase structure rule for this sentence in Manyika

Fanta be daga sigi English translation = Fanta is setting down the pot

I have S-NP-N then maybe it's VP but I don't know. Can anyone help me understand this language please?