WordNET: Enhancing NLP Models with Semantic Relations - AITechTrend
WordNET in NLP Applications

WordNET: Enhancing NLP Models with Semantic Relations

If you’re familiar with the field of natural language processing (NLP), you know that it encompasses a wide range of tasks, including automatic text classification, sentiment analysis, and text summarization. These tasks heavily rely on sentence patterns and word meanings within different contexts. Words that appear different at first glance can actually share similarities to some degree. For example, consider the words “jog” and “run” – they are partially different yet also partially similar. To perform specific NLP-based tasks effectively, it is crucial to understand the nuances of words in different positions and recognize their similarities. This is where WordNET comes into play, offering solutions to linguistic challenges faced by NLP models.

What is WordNET?

WordNET serves as a comprehensive lexical database, containing semantic relations between words in over 200 languages. It groups adjectives, adverbs, nouns, and verbs into sets of cognitive synonyms called synsets. Each word in the database represents a distinct concept and is accompanied by lexical and semantic relations. WordNET is publicly available for download and offers a network of related words and concepts. You can explore WordNET and test its functionalities using the following link. Here are a few test images you’ll encounter when accessing WordNET through your browser:

The Distinction Between WordNET and Thesaurus

While a thesaurus helps us find synonyms and antonyms of words, WordNET takes us beyond that. WordNET interlinks words based on their specific senses, whereas a thesaurus links words solely based on their meanings. In WordNET, words are semantically disambiguated when they appear in close proximity to each other. Thesaurus provides a level of words in the network if they have similar meanings. In contrast, WordNET groups words into levels according to their semantic relations, providing a more comprehensive way of organizing words.

Structure of WordNET

The basic structure of WordNET consists of a network of words, where synonymous words share similar concepts and can be used interchangeably in various contexts. These words are grouped into synsets, which are unordered sets. Synsets are linked together if they possess even the slightest conceptual relation. Each synset in the network comes with a brief definition, and many synsets are illustrated with examples of their usage in sentences. It is this definition and example part that sets WordNET apart from other resources.

Relations in WordNET

WordNET features several essential relations between synsets. Let’s explore some of the most frequent ones:

Hyponymy

In linguistics, a word with a broad meaning represents a category into which words with more specific meanings fall. This broad meaning is known as a hypernym. For instance, “color” is a hypernym of “red.” Hyponymy signifies the relationship between a hypernym and a specific instance of a hyponym. A hyponym is a word or phrase that has a semantic field more specific than its hypernym. Hyponymy provides a hierarchical structure to words. In WordNET, the category “color” includes “purple,” which, in turn, includes “violet.” The root node of the hierarchy represents the highest-level concept for every noun. Thus, “violet” is a kind of “purple,” and “purple” is a kind of “color.” This transitive relationship is known as hyponymy.

WordNET also encompasses meronymy relations, which define the whole-part relationship between synsets. For example, a “bike” consists of two wheels, a handle, and a petrol tank. These components of a bike are inherited by their subordinates. If a bike has two wheels, a sports bike also has wheels. Linguistically, we use this kind of relationship to describe adverbs that represent the characteristics of a noun. The parts are inherited in a downward direction, as all bikes and types of bikes have two wheels. However, not all kinds of automobiles consist of two wheels.

Troponymy

Troponymy refers to the presence of a “manner” relation between two lexemes. Verbs describing events that necessarily and unidirectionally entail one another are linked through troponymy. For example, {buy}-{pay}, {succeed}-{try}, {show}-{see}, etc. Within the hierarchy, verbs towards the bottom represent mannerisms characterizing events. For instance, “communication” can be broken down into “talk” and further into “whisper.”

Antonymy

WordNET arranges adjective words into antonymy pairs. For instance, “wet” and “dry,” “smile” and “cry.” Each pair of antonyms is linked with sets of semantically similar words. “Cry” is linked to “weep,” “shed tears,” “sob,” “wail,” etc., making them indirect antonyms of “smile.”

Cross-PoS Relations

Most of the relations in WordNET exist within the same part of speech. However, there are cross-part-of-speech (PoS) pointers available in the network, including morphosemantic links that connect words with the same meaning and shared stems. For example, there are pairs like “reader” and “read,” where the noun has a semantic layer with respect to the verb.

5. Implementation of WordNET

Implementing WordNET is straightforward and requires just a few lines of code. Let’s take a look:

# Importing libraries
import nltk
from nltk.corpus import wordnet

# Downloading WordNET
nltk.download('wordnet')

# Trying WordNET by checking synonyms, antonyms, and similarity percentage
synonyms = []
antonyms = []

for synset in wordnet.synsets("evil"):
    for l in synset.lemmas():
        synonyms.append(l.name())
        if l.antonyms():
            antonyms.append(l.antonyms()[0].name())

print(set(synonyms))
print(set(antonyms))

# Checking word similarity feature
word1 = wordnet.synset('man.n.01')
word2 = wordnet.synset('boy.n

In the code snippet above, we import the necessary libraries and download WordNET using the NLTK package. Then, we demonstrate the functionalities of WordNET by checking synonyms, antonyms, and similarity percentage for the word “evil.” The results are printed, showcasing the synonyms and antonyms of “evil” and providing an estimation of the similarity between the synsets of “man” and “boy.”

Final Thoughts

In this article, we delved into the world of WordNET, exploring its structure, functionalities, and implementation. WordNET is a powerful resource that helps us unlock the potential of semantic relations in NLP tasks. By understanding the relationships between words and their conceptual connections, we can enhance the accuracy and effectiveness of NLP models. We also learned about important relations such as hyponymy, meronymy, troponymy, and antonymy, as well as cross-PoS relations. Lastly, we provided a brief code snippet showcasing how to implement WordNET using Python and the NLTK package.