Parsers and syntax trees for natural languages
Say I have a string representing text in a natural language, for example:
She is an effective teacher. Most students have found her reasonably helpful.
Introduction
Is there a programming library or command line program that will take this as input and return a tree stucture, the kind that is similar to syntax trees produced by parsers for formal/programming languages?
There either already is, or the capability to make precisely what you want is manageable and feasible to put together.
POS taggers
I know of POS taggers, but these taggers only seem to analyse the text at the word level.
Possibly, but it depends which one / which technique is being used. Maybe someone can find a research review article on the state-of-the-art on this. My idea for classifying approaches would be, how much ‘knowledge’ or structure the program starts out with. Maybe it comes with a whole dictionary of tokens and their parts-of-speech - in which case, it’s a simple string matching algorithm. Maybe it’s a rule-based lemmatizer, in which case, the program comes with a lot of initial information about morphology and lemmatization rules based on different word forms. A syntax parser seems like a good approach, because one of the strongest relationships part of speech is commonly take to have, is to syntax rules dictating how POS elements (word kinds, or word classes) get arranged with one another. Or maybe semantics has a relationship to POS, and someone could think of a technique leveraging that. On the lowest level, there are algorithms that come without any strong relationship to the structures presumed to be in language, and can still identify patterns - for example, here is one highly cited paper from the 1980’s, which has been followed by more new techniques:
Schmidt, Helmut. “Probabilistic part-of-speech tagging using decision trees.” (1994).
Syntax parsing in research
I need the constituents of all other levels..
Then you’re looking for a complete syntax parse - another alive-and-well technique in modern NLP. Just try keyword-perusing Semantic Scholar or Google Scholar:
https://www.semanticscholar.org/search?q=Constituency%20parse&sort=relevance
Revisiting the Practical Effectiveness of Constituency Parse Extraction from Pre-trained Language Models
Taeuk Kim
Computer Science
International Conference on Computational…
15 September 2022
CPTAM: Constituency Parse Tree Aggregation Method
Adithya Kulkarni, Nasim Sabetpour, A. Markin, O. Eulenstein, Qi Li
Computer Science
SDM
19 January 2022
…
Syntax parsing code libraries
Hence, the tree structure should reflect the structure of the text at all levels
ie discourse, sentence, clause, phrase, group, word, even morpheme (both inflectional and derivational)
and I should be able to traverse this tree from the bottom-most node to the "root" node...
So traversing the whole tree of the above mentioned string sample should give something like:
Constituent/Element----Constituent/Element Type/Part Of Speech----Level
She----Pronoun----Word
Is----Verb----Word
An----Determiner----Word
Effective----Adjective----Word
teacher----Noun----Word
...
She----Subject/Noun Phrase----Phrase
Is----Predicate/Verb Phrase----Phrase
An effective teacher----Object/Noun Phrase----Phrase
...
She is an effective teacher.----Simple sentence----Sentence
Most students have found her reasonably helpful.----Simple sentence----Sentence
I would say there are libraries that come close, like Spacy, but given a somewhat custom vision you have, you might want to Do-It-Yourself using more fundamental techniques & foundational approaches.
Spacy
Spacy is a library that offers a “pipeline” of natural language processing layers. All you would do is submit a text to Spacy’s core language object, and its constructor will perform virtually all standard processing tasks which could be desired, which are then accessible as attributes. Here’s a demo:
import spacy
nlp = spacy.load(‘en_core_web_sm’)
doc = nlp(some_text)
print(doc.sents)
for sent in doc.sents:
for word in sent:
print(word.POS)
and the like.
Using a computable grammar (for the language)
Using a parser generator like ANTLER + BNF
Actually I have also thought of achieving the abovementioned by creating my own parser using a parser generator like ANTLR, which take in a BNF grammar as input, and produce a parser program/executable as output.. But I haven't been able to find BNFs for natural languages so far.. If one can provide BNF resources for natural languages that can be used with ANTLR, that can also be accepted..
Grammatical Framework
The closest thing I know of is Grammatical Framework.
Custom, DIY solution (w.i.p.)
I can try to update this post with a custom or authentic DIY solution instead, which would be better. I think I would prefer non-probabilistic feature discovery if possible.
