The distributional hypothesis is a concept in natural language processing derived from the idea that “You shall know a word by the company it keeps.” This principle suggests that words appearing in similar contexts tend to have similar meanings. This hypothesis is important as it provides a framework for understanding and modeling semantic relationships between words, which is crucial for various NLP tasks. This hypothesis was advanced by linguists Zellig Harris and J.R. Firth. Harris introduced this concept in the 1950s.

The distributional hypothesis suggests that the meaning of a word can be inferred from the context in which it appears. This principle allows us to create models that can understand the semantic similarities between words based on their context.

Applications in NLP

Word Embeddings

One of the main applications of the distributional hypothesis in NLP is the creation of word embeddings, like Word2Vec and GloVe. These embeddings represent words as vectors in a continuous vector space, capturing semantic similarities based on their contextual usage. For example, words like “king” and “queen” will have similar vector representations because they often appear in similar contexts.

Semantic Similarity and Clustering

We can also use the distributional hypothesis to calculate semantic similarity between words, cluster similar words together, and classify documents based on their content. These applications are very important for tasks like information retrieval, topic modelling, sentiment analysis, and more.

Named Entity Recognition (NER)

We can use the distributional hypothesis in context-based learning, which enhances Named Entity Recognition (NER) tasks by allowing models to identify and classify entities based on their neighbour words. This significantly improves the accuracy of NER systems.

Computational Methods

Co-occurrence Matrices

Co-occurrence matrices represent the context in which words appear. In these matrices, the frequency with which words co-occur is calculated within a specified window of text. The resulting matrix can be used to understand word relationships. Co-occurrence matrices are an important tool in distributional semantics, which is used to represent contexts in which words appear within the text.

To create this matrix, we will first import the necessary libraries, such as `defaultdict` from the `collections` module and `pandas`. Next, we will create a corpus of a few sentences. We will then tokenize this corpus by splitting each sentence into individual words. After that, we will define a window size and iterate through each word, counting the co-occurrences within that specified window. Finally, we will convert the matrix into a `pandas` DataFrame for better visualization.

from collections import defaultdict
import pandas as pd

corpus = [
    "I love natural language processing",
    "natural language processing is fun",
    "I love coding in Python"
]

tokenized_corpus = [sentence.split() for sentence in corpus]

window_size = 2  
co_occurrence_matrix = defaultdict(lambda: defaultdict(int))

for sentence in tokenized_corpus:
    for i, word in enumerate(sentence):
        for j in range(max(0, i - window_size), min(len(sentence), i + window_size + 1)):
            if i != j:
                co_occurrence_matrix[word][sentence[j]] += 1

co_occurrence_df = pd.DataFrame(co_occurrence_matrix).fillna(0).astype(int)
print("Co-occurrence Matrix:")
print(co_occurrence_df)

OUTPUT:

Co-occurrence Matrix:

from gensim.models import Word2Vec
from gensim.test.utils import common_texts

model = Word2Vec(sentences=common_texts, vector_size=100, window=5, min_count=1, workers=4)

word = 'computer'
similar_words = model.wv.most_similar(word, topn=5)

print(f"Words similar to '{word}': {similar_words}")

Words similar to 'computer': [('system', 0.21617139875888824), ('survey', 0.04468922317028046), ('interface', 0.015203381888568401), ('time', 0.0019510635174810886), ('trees', -0.03284316882491112)]