Sentence-Level Constructions

Pre-requisites

Before learning about the sentence-level constructions of NLP, let us first learn some basics about NLP itself.

• NLP stands for Natural Language Processing. In NLP, we analyze and synthesize the input and the trained NLP model then predicts the necessary output.
• NLP is the backbone of technologies like Artificial Intelligence and Deep Learning.
• In basic terms, we can say that NLP is nothing but the computer program's ability to process and understand the provided human language.
• The NLP process first converts our input text into a series of tokens (called the Doc object) and then performs several operations of the Doc object.
• A typical NLP processing process consists of stages like tokenizer, tagger, lemmatizer, parser, and entity recognizer. In every stage, the input is the Doc object and the output is the processed Doc.
• Every stage makes some kind of respective change to the Doc object and feeds it to the subsequent stage of the process.

Introduction

The sentence level construction NLP is a major task that is comprised of many small steps. In this article, we will be learning one of the major smalls step or structures involved to develop a sentence i.e. feature structure.

Now before diving deep into the feature structure, let us first know how a sentence is generally generated in NLP. Suppose we have a small grammar rule that helps us to generate the sentence: The gangster dies.

Grammar:

Now suppose that we want to generate a new sentence i.e. The gangsters die.. So, to do so, we need to extend the grammar and by extending the grammar, we need to add the plural forms of the words gangster and die. So, the new grammar comes out to be:

So, we can see how we need to proceed further step by step and make changes to the grammar rules and the structure to obtain the overall desired sentence structure. Now, this set of grammar properties is represented in the form of feature structure in terms of computational linguistics.

So, let us now learn more about the feature structures and one of their very important operations i.e. unification in detail in the next section.

Sentence Level Constructions

The sentence level construction NLP is mainly governed by the language's grammar rules. In the case of sentence construction, we refer to the phrase structure grammar. Some examples of phase structure grammar are generalized phrase structure grammar, lexical functional grammar, head-driven phrase structure grammar, etc. In these grammars, the construction is mainly comprised of many feature structures which are nothing but a set of attribute-value pairs.

Let us now learn more about the feature structures in grammar and how can we combine the feature structures (unification).

Feature Structures in Grammar

Features structures are matrix-like structures comprising attribute-value pairs (also known as feature-value pairs) that encode the various semantic and syntactic properties of any language. A feature includes the agreements like a number, gender, case, and person features as well as semantic features like tense, and aspects.

We use the attribute value matrices or AVM to denote the feature structure. For example:

The feature structures can also be denoted using a directed graph. The paths present in the directed graph correspond to a sequence of features. These features lead us to the feature structure having some value.

Feature Structure Unification

Let us now understand what is feature structure unification. We perform a lot of operations on the feature structures, one such operation is unification. Unification is a simple partial operation in which we combine two feature structures such that the newly created feature structure contains the information of the previous two original feature structures. The value of the feature or an attribute of the feature structure can be atomic or complex.

Let us take an example for more clarity.

Suppose we have two features. The first feature structure (F1) is denoted as:

The second feature structure (F2) is denoted as:

The unification of both features is written as:

So, F1 union F2 comes out to be:

The unification process is used to combine the included attribute-value pairs. Firstly, the unification process verifies the compatibility of various rules' encoded information of the feature-value pairs. After verifying the encoded information, the unification of the structure takes place and if the pairs are compatible then the unification takes place else it rejects the feature-values pairs.

Implementation in Python

Sentence-level constructions can be implemented in Python using Natural Language Processing (NLP) libraries such as NLTK, spaCy, and Stanford CoreNLP. Here is a step-by-step guide to implementing sentence-level constructions using NLTK:

1. Install NLTK:
   You can install NLTK using pip by running the command pip install nltk in your command prompt.

2. Import the NLTK library:
   After installation, you need to import the NLTK library into your Python script. You can do this by adding the following line at the beginning of your script:

3. Download the necessary data:
   Before you can use the NLTK library, you need to download the data required for sentence-level constructions. You can download the necessary data by running the following command:

   The first command downloads the data required for part-of-speech tagging, while the second command downloads the data required for sentence tokenization.

4. Sentence tokenization:
   The first step in implementing sentence-level constructions is to split the input text into sentences. You can use the sent_tokenize() function provided by the NLTK library to perform sentence tokenization. Here is an example:

   This will output the following list of sentences:

5. Part-of-speech tagging:
   The next step is to perform part-of-speech tagging on each sentence. You can use the pos_tag() function provided by the NLTK library to perform part-of-speech tagging. Here is an example:

   This will output the part-of-speech tags for each word in each sentence.

6. Extracting sentence-level constructions:
   Finally, you can use regular expressions to extract sentence-level constructions from the part-of-speech tags. For example, to extract all sentences that contain a noun phrase followed by a verb phrase, you can use the following regular expression:

   You can use the RegexpParser class provided by the NLTK library to perform regular expression parsing. Here is an example: