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I am trying to implement a graph based dependency parser. Since I come from a computer engineering perspective, I have trouble finding the features. Assuming that we have a (head,dependent) relation for (x,y), here are some features that I found to be helpful:

POS Tags of x&y
Direction of the relation(right or left)
Distance between words x and y
Word itself information(x&y themselves)

But I should enhance the accuracy of my parser and will appreciate any help.

2
  • Is the parser stochastic?
    – Atamiri
    May 9, 2018 at 13:40
  • Yes, it is stochastic.
    – Tokugava
    May 9, 2018 at 15:21

1 Answer 1

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Dependency Parsing: Techniques and Features

Dependency parsing is a critical task in Natural Language Processing (NLP) that involves identifying grammatical relationships between words in a sentence. The goal is to construct a dependency tree where nodes represent words and directed edges represent grammatical dependencies, with the edge direction pointing from the head (or parent) to the dependent (or child). The inherent syntactic structure of a sentence is not always explicitly marked, necessitating the use of related grammatical features to infer these head-complement relations. These features include Part-of-Speech (POS) tags, syntactic features, semantic features, contextual features, morphological features, and statistical features.

Part-of-Speech (POS) Tags

POS tags categorize words based on their grammatical roles, such as noun, verb, adjective, etc. They provide crucial information for determining likely relationships in a dependency parse. For instance, a verb is often the head of an object noun, and a preposition is usually the head of its object. POS tagging can be implemented using Hidden Markov Models (HMMs), a statistical model where the system being modeled is assumed to be a Markov process with hidden states (Rabiner, 1989)(1).

In Python, POS tagging can be performed using libraries like NLTK or SpaCy. Here's a simple example using NLTK:

import nltk

sentence = "The cat sat on the mat"
tokens = nltk.word_tokenize(sentence)
pos_tags = nltk.pos_tag(tokens)

The advantage of using POS tags is that they capture essential grammatical information about words. However, POS tagging itself can be a challenging task, especially for languages with complex morphology or ambiguous word forms. Additionally, POS tags alone may not be sufficient to resolve all dependency relations, especially those influenced by semantics or context.

Syntactic Features

Syntactic features pertain to the roles words play in a sentence, such as subject, object, or modifier. They can help a parser identify these roles and form dependencies accordingly. Syntactic features can be represented using context-free grammars (CFGs) (Chomsky, 1956)(2).

In Python, syntactic parsing can be performed using libraries like NLTK. Here's a simple example:

import nltk

grammar = nltk.CFG.fromstring("""
  S -> NP VP
  NP -> Noun
  VP -> Verb NP NP
  Noun -> "John" | "Mary" | "book"
  Verb -> "gave"
""")

parser = nltk.ChartParser(grammar)
sentence = "John gave Mary a book"
tokens = nltk.word_tokenize(sentence)
for tree in parser.parse(tokens):
    print(tree)

The advantage of using syntactic features is that they capture the structural relationships between words. However, constructing accurate syntactic parsers can be complex and computationally intensive. Furthermore, syntactic parsing may not fully capture semantic or contextual relationships between words.

Semantic Features

Semantic features focus on the meanings of words and their semantic roles in a sentence. They can help resolve dependencies that are influenced by the semantic compatibility of words. Semantic roles can be represented using semantic role labeling (SRL) schemes, such as PropBank (Palmer et al., 2005) (3).

Semantic role labeling is a complex task that typically involves deep learning models. In Python, semantic role labeling can be performed using libraries like AllenNLP. Here's a simple example:

from allennlp.predictors.predictor import Predictor

predictor = Predictor.from_path("https://s3-us-west-2.amazonaws.com/allennlp/models/srl-model-2018.05.25.tar.gz")
sentence = "The cat ch

ases the mouse"
result = predictor.predict(sentence=sentence)

The advantage of using semantic features is that they capture meaning-related information, which can help resolve ambiguities and determine the roles of words in a sentence. However, semantic role labeling is a complex task that requires large annotated corpora and computationally intensive models.

Contextual Features

Contextual features consider the surrounding words and larger linguistic context. They can help resolve dependencies that are influenced by the larger linguistic context. Contextual features can be represented using n-grams, which are sequences of n words.

In Python, n-grams can be generated using libraries like NLTK. Here's a simple example:

import nltk

sentence = "I saw the man with the telescope"
tokens = nltk.word_tokenize(sentence)
bigrams = list(nltk.bigrams(tokens))

The advantage of using contextual features is that they capture the larger linguistic context, which can help resolve ambiguities and capture long-distance dependencies. However, n-grams can lead to a large feature space, especially for large n, which can increase the computational complexity of the parser.

Morphological Features

Morphological features capture the form of words, such as their number, gender, case, tense, aspect, etc. They can help in disambiguating dependencies, especially in morphologically rich languages.

In Python, morphological analysis can be performed using libraries like SpaCy. Here's a simple example:

import spacy

nlp = spacy.load('en_core_web_sm')
sentence = "Cats chase mice"
doc = nlp(sentence)
for token in doc:
    print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_)

The advantage of using morphological features is that they capture important grammatical information, especially in morphologically rich languages. However, morphological analysis can be a challenging task, especially for languages with complex morphology.

Statistical Features

Statistical features are derived from the frequency and probability of certain patterns in the training data. They can guide the parser towards more likely interpretations.

In Python, statistical features can be computed using libraries like NLTK or SciPy. Here's a simple example of computing bigram frequencies:

import nltk

corpus = ["The cat sat on the mat", "Cats chase mice"]
tokens = [nltk.word_tokenize(sentence) for sentence in corpus]
bigrams = [list(nltk.bigrams(t)) for t in tokens]
bigram_freq = nltk.FreqDist(b for b_list in bigrams for b in b_list)

The advantage of using statistical features is that they capture information about the frequency and probability of certain patterns, which can guide the parser towards more likely interpretations. However, statistical features require large annotated corpora to be effective, and they may not fully capture the complexities of human language.

In conclusion, each of these feature types has its advantages and drawbacks, and the optimal choice of features can depend on the specific language, domain, and parsing algorithm. As of June 2023, state-of-the-art dependency parsers often use a combination of these features, and they typically employ machine learning algorithms, such as neural networks, that can automatically learn useful features from data.

1: Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257-286. 2: Chomsky, N. (1956). Three models for the description of language. IRE Transactions on information theory, 2(3), 113-124. 3: Palmer, M., Gildea, D., & Kingsbury, P. (2005). The Proposition Bank: An Annotated Corpus of Semantic Roles. Computational Linguistics, 31(1), 71-106.

For further reading on dependency parsing features, you may find the following resources helpful:

  • "Dependency Parsing: A Comprehensive Introduction" by Gabriel H. Lopes and Sandra M. Aluísio
  • "Data-Driven Dependency Parsing" by Joakim Nivre
  • "Statistical Dependency Parsing of Modern Standard Arabic" by Mahmud A. Al-Badrashiny and Mona T. Diab

I hope this helps!

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