Scoring Word Guesses

Welcome to the first lesson of our course, “Enhancing the Word Play Game with New Functionalities”. In this lesson, we will make our word prediction game more interactive and fair by adding a way to score player guesses. Instead of only rewarding exact matches, we want to give points for guesses that are close in meaning to the correct answer. This will make the game more fun and challenging, and it will feel more like how people actually use language.

By the end of this lesson, you’ll know how to compare two words for similarity and assign a score based on how close they are in meaning. This is a key step in building a smarter, more engaging game.

Semantic similarity is a way to measure how close two words are in meaning. For example, car and automobile mean almost the same thing, so they are semantically similar. On the other hand, car and banana are not similar at all.

Here’s a simple table to show some examples:

In our game, we want to reward players for making guesses that are close in meaning, not just exact matches. This makes the game fairer and more fun.

To compare word meanings, computers use something called word vectors or embeddings. You can think of a word vector as a list of numbers that represents the meaning of a word. Words with similar meanings have vectors that are close together.

For example, the word cat might be represented by a vector like [0.2, 0.5, 0.1, ...], and kitten might have a vector that is very close to it. The word banana would have a very different vector.

We can measure how close two vectors are using a mathematical formula called cosine similarity:

$\text{cos}(\theta) = \frac{\mathbf{A} \cdot \mathbf{B}}{\|\mathbf{A}\| \|\mathbf{B}\|}$

Let’s break down the formula:

• $\mathbf{A}$ and $\mathbf{B}$ are the word vectors for the two words you want to compare. Each vector is a list of numbers that represents the meaning of a word.
• $\mathbf{A} \cdot \mathbf{B}$ is the dot product of the two vectors. This is a way to combine the two lists of numbers into a single value that reflects how much they point in the same direction.
• $\|\mathbf{A}\|$ and $\|\mathbf{B}\|$ are the lengths (also called the “norms” or “magnitudes”) of each vector. You can think of this as how long each arrow is if you drew it on a graph.
• $\frac{\mathbf{A} \cdot \mathbf{B}}{\|\mathbf{A}\| \|\mathbf{B}\|}$ divides the dot product by the lengths of the two vectors. This ensures the similarity score is always between -1 and 1, where 1 means the vectors point in exactly the same direction (very similar), 0 means they are completely unrelated, and -1 means they are opposite (which doesn’t usually happen with word vectors).

In simple terms, this formula measures how close the two word meanings are by looking at the angle between their vectors: the smaller the angle, the more similar the words.

To make this possible, we use pre-trained word vectors that have already learned the relationships between words from large collections of text. This allows us to compare the meanings of words without having to train the vectors ourselves.

Let’s build our guess scorer step by step.

First, we need to import spaCy and load a model that has word vectors. We’ll use the en_core_web_md model, which is good for English and includes medium-sized word vectors.

import spacy

nlp = spacy.load('en_core_web_md')

• import spacy brings the spaCy library into our code.
• nlp = spacy.load('en_core_web_md') loads the English model with word vectors.

Next, we need to process the user’s guess and the correct word using spaCy. This turns each word into a Doc object, the class that spaCy uses to encode a sequence of tokens.

guess = nlp(user_guess.lower())
correct = nlp(correct_word.lower())

• nlp(user_guess.lower()) processes the guess and makes it lowercase (to avoid case mismatches).
• nlp(correct_word.lower()) does the same for the correct word.

Now, we can use spaCy’s .similarity() method to compare the two objects.

similarity = guess.similarity(correct)

• This gives us a number between 0 and 1, where 1 means the words are identical in meaning, and 0 means they are not similar at all.

To make the score easier to understand, we multiply it by 100 to get a value between 0 and 100.

score = similarity * 100

Here is the complete function in Python:

import spacy

nlp = spacy.load('en_core_web_md')


def score_guess(user_guess: str, correct_word: str) -> int:
    """
    Computes a semantic similarity score (0–100) between two words.
    """
    guess = nlp(user_guess.lower())
    correct = nlp(correct_word.lower())
    return guess.similarity(correct) * 100

Let’s see an example:

print(score_guess("cat", "kitten"))  # Output: 74.2 (your value may vary)
print(score_guess("car", "automobile"))  # Output: 87.5 (your value may vary)
print(score_guess("car", "banana"))  # Output: 12.3 (your value may vary)

• The function gives a higher score for words that are close in meaning.
• The output values are just examples; your results may be slightly different depending on the word vectors used.

In this lesson, you learned how to make your word prediction game smarter by scoring guesses based on their meaning, not just their spelling. We talked about semantic similarity, word vectors (embeddings), and how to compare words using these concepts. You also saw how to build a function that gives a score from 0 to 100 for any two words.

This new scoring system will make your game more fun and fair, rewarding players for close guesses. In the next practice exercises, you’ll get hands-on experience using and testing this function. Get ready to see how your game can understand language just a little bit more like a human!