Context aware sentence matching using ML techniques

Sentence matching or text matching is a common problem in natural language processing (NLP) and machine learning (ML) that involves comparing two or more sentences to determine if they are semantically similar or not. Sentence matching has several applications, such as plagiarism detection, information retrieval, question answering, chatbots, and many more. In this article, we will discuss various machine learning techniques that can be used for sentence matching.

Machine Learning Techniques for Sentence Matching:

1. Bag of Words (BoW) Model:

The bag of words model is a simple and widely used technique for sentence matching. In this model, each sentence is represented as a vector of word frequencies, where each dimension corresponds to a unique word in the corpus, and the value of each dimension represents the frequency of that word in the sentence. The similarity between two sentences is then measured using cosine similarity, Jaccard similarity, or some other similarity metric. The main advantage of the BoW model is its simplicity and ease of implementation. However, it has some limitations, such as it ignores the order of words in the sentence, it is sensitive to stop words, and it does not capture the meaning of words.

source:https://vitalflux.com/text-classification-bag-of-words-model-python-sklearn/

2. TF-IDF (Term Frequency-Inverse Document Frequency) Model:

The TF-IDF model is an extension of the BoW model that addresses some of its limitations. In this model, each dimension in the vector represents a unique word in the corpus, and the value of each dimension is the product of the term frequency (TF) and inverse document frequency (IDF) of that word in the sentence. The TF measures the frequency of a word in a sentence, and the IDF measures the importance of a word in the corpus. The similarity between two sentences is then measured using cosine similarity or some other similarity metric. The main advantage of the TF-IDF model is that it is less sensitive to stop words, and it captures the importance of words in the corpus. However, it still ignores the order of words in the sentence and does not capture the meaning of words.

3. Word Embeddings:

Word embeddings are a powerful technique for sentence matching that capture the meaning of words and their relationships. In this technique, each word in the sentence is represented as a dense vector of real numbers, where the values of the vector are learned through a neural network. The vectors are trained to capture the semantic relationships between words, such as synonymy, antonymy, and context. The vectors are then combined to represent the entire sentence, either by averaging or concatenating them. The similarity between two sentences is then measured using cosine similarity or some other similarity metric. The main advantage of word embeddings is that they capture the meaning of words and their relationships, and they can handle synonyms and paraphrases. However, they require a large amount of training data and computational resources, and they may not work well for rare or out-of-vocabulary words.

4. Sentence to Vector (Sent2Vec):

Sentence to Vector (Sent2Vec) is one more popular technique for sentence matching that have gained popularity in recent years.

Sentence to vector for text comparison is a technique that converts sentences into mathematical vectors, allowing them to be compared and measured for similarity. This method uses a neural network model that is trained to capture the context and meaning of entire sentences, rather than individual words.

The neural network takes the sentence as input and processes it through a series of layers that transform the sentence into a dense vector representation. The vector representation captures the meaning and context of the sentence, allowing for accurate text comparison and similarity measurement.

The advantage of this approach is that it takes into account the entire sentence and captures the relationships between words in a more holistic way than traditional methods like the bag of words or TF-IDF. Additionally, because the neural network is trained on a large corpus of text data, it can handle rare or out-of-vocabulary words that may not be present in traditional word embeddings.

Metrics:

Once we have created the vector representation of a sentence using the techniques mentioned earlier, we need to have a metric that can take these vector representations and measure how similar the sentences are. There are several metrics that can be used to compare sentence vectors. Here are some common ones:

1. Cosine Similarity:

Cosine similarity is a popular metric for measuring the similarity between two sentence vectors. It measures the cosine of the angle between the two vectors. The formula for cosine similarity is:

cosine_similarity(A, B) = dot(A, B) / (norm(A) * norm(B))

Where:

• A and B are the two sentence vectors being compared.
• dot(A, B) is the dot product of the two vectors, which is the sum of the products of their corresponding elements.
• norm(A) and norm(B) are the Euclidean norms of A and B, respectively.

The cosine similarity metric returns a score between 0 and 1. A score of 1 indicates that the two vectors are identical, while a score of 0 indicates that they are completely dissimilar.

2. Euclidean Distance:

Euclidean distance is a metric that measures the distance between two sentence vectors in Euclidean space. The formula for Euclidean distance is:

euclidean_distance(A, B) = sqrt(sum((A - B)^2))

Where:

• A and B are the two sentence vectors being compared.
• (A — B)² is the element-wise squared difference between A and B.
• sum((A — B)²) is the sum of the squared differences.
• sqrt(sum((A — B)²)) is the square root of the sum of squared differences.

The Euclidean distance metric returns a non-negative score. A score of 0 indicates that the two vectors are identical, while a larger score indicates that they are more dissimilar.

3. Manhattan Distance:

Manhattan distance is a metric that measures the distance between two sentence vectors. It measures the sum of the absolute differences between the corresponding elements of the two vectors. The formula for Manhattan distance is:

manhattan_distance(A, B) = sum(abs(A - B))

Where:

• A and B are the two sentence vectors being compared.
• abs(A — B) is the element-wise absolute difference between A and B.
• sum(abs(A — B)) is the sum of the absolute differences.

The Manhattan distance metric returns a non-negative score. A score of 0 indicates that the two vectors are identical, while a larger score indicates that they are more dissimilar.

These metrics can be used to compare sentence vectors and determine their similarity.

Code:

Here is a coding example of comparing two sentences using the SentenceTransformer library in Python:

from sentence_transformers import SentenceTransformer, util

# Load a pre-trained model
model = SentenceTransformer('all-MiniLM-L6-v2')

# Define two sentences to compare
sentence1 = "I love playing soccer with my friends."
sentence2 = "My friends and I enjoy playing soccer together."

# Encode the sentences using the pre-trained model
embedding1 = model.encode(sentence1, convert_to_tensor=True)
embedding2 = model.encode(sentence2, convert_to_tensor=True)

# Compute cosine similarity between the two sentence embeddings
cosine_similarity = util.cos_sim(embedding1, embedding2)

print("Cosine similarity:", cosine_similarity)

In this code, we first load a pre-trained model from the SentenceTransformer library. We then define two sentences to compare: “I love playing soccer with my friends” and “My friends and I enjoy playing soccer together”. We encode these sentences into dense vectors using the pre-trained model, and then compute the cosine similarity between the two vectors using the util.cos_sim() function.

The output of this code will be the cosine similarity score between the two sentences, which represents how similar the two sentences are in terms of their meaning. The cosine similarity score ranges from 0 (completely dissimilar) to 1 (identical). In this case, the output will look something like this:

Cosine similarity: 0.9138

This means that the two sentences are quite similar, with a cosine similarity score of 0.91.

The SentenceTransformer library makes it easy to compare sentences using pre-trained models. By encoding sentences into dense vectors, we can compare them using various metrics like cosine similarity, Euclidean distance, or Manhattan distance.

Summary:

We can compare sentences using machine learning techniques by taking their context into account, similar to how humans compare sentences. By converting sentences into vector representations, we can perform mathematical operations to measure their similarity. Techniques such as sentence-to-vector can capture various aspects of sentence meaning and context. Once we have created the vector representation of a sentence, we can use different metrics such as cosine similarity or Euclidean distance to compare and measure the similarity between sentences.

References:https://www.sbert.net/