What are some simple NLP projects that a CS undergrad can try implementing?

Simple NLP Projects for Computer Science Undergraduates

Explore accessible Natural Language Processing projects perfect for CS undergraduates to build foundational skills and gain practical experience in AI.

Natural Language Processing (NLP) is a fascinating field at the intersection of computer science, artificial intelligence, and linguistics. For computer science undergraduates, diving into NLP can seem daunting, but many projects are perfectly suited for beginners. These projects not only introduce core NLP concepts but also provide hands-on experience with data manipulation, algorithm implementation, and model evaluation. This article outlines several simple yet impactful NLP projects that you can implement to kickstart your journey in this exciting domain.

1. Text Preprocessing and Analysis Toolkit

Before any meaningful NLP task can be performed, text data usually needs extensive cleaning and preparation. Building a basic text preprocessing toolkit is an excellent starting point. This project involves implementing functions for common tasks like tokenization, stemming, lemmatization, stop-word removal, and calculating basic statistics such as word frequency. It helps you understand the raw nature of text data and the necessity of these steps.

flowchart TD
    A[Raw Text] --> B{Tokenization}
    B --> C{Lowercasing}
    C --> D{Stop-word Removal}
    D --> E{Stemming/Lemmatization}
    E --> F[Cleaned Text]

Basic Text Preprocessing Workflow

import nltk
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer, WordNetLemmatizer
from nltk.tokenize import word_tokenize

nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')

def preprocess_text(text):
    # Tokenization
    tokens = word_tokenize(text.lower())
    
    # Remove stop words
    stop_words = set(stopwords.words('english'))
    filtered_tokens = [word for word in tokens if word.isalnum() and word not in stop_words]
    
    # Lemmatization (or Stemming)
    lemmatizer = WordNetLemmatizer()
    lemmatized_tokens = [lemmatizer.lemmatize(word) for word in filtered_tokens]
    
    return lemmatized_tokens

example_text = "Natural Language Processing is an exciting field for computer science students."
processed_words = preprocess_text(example_text)
print(processed_words)

Python code for basic text preprocessing using NLTK.

2. Simple Sentiment Analyzer

Sentiment analysis, or opinion mining, is the process of determining the emotional tone behind a piece of text. A simple sentiment analyzer can classify text as positive, negative, or neutral. For a beginner, a rule-based approach using lexicons (lists of words with associated sentiment scores) is a great starting point. You can use pre-existing sentiment lexicons like AFINN, VADER, or build a small custom one.

from nltk.sentiment.vader import SentimentIntensityAnalyzer
import nltk

nltk.download('vader_lexicon')

def analyze_sentiment(text):
    analyzer = SentimentIntensityAnalyzer()
    vs = analyzer.polarity_scores(text)
    
    if vs['compound'] >= 0.05:
        return 'Positive'
    elif vs['compound'] <= -0.05:
        return 'Negative'
    else:
        return 'Neutral'

print(analyze_sentiment("This movie was absolutely fantastic!"))
print(analyze_sentiment("I hated every minute of it."))
print(analyze_sentiment("The weather is okay today."))

Python code for a simple sentiment analyzer using NLTK's VADER.

3. Keyword Extractor

Keyword extraction is the task of automatically identifying the most important words or phrases in a document. This is useful for summarizing content, indexing documents, or creating tag clouds. A straightforward approach involves using TF-IDF (Term Frequency-Inverse Document Frequency) to score words based on their importance in a document relative to a corpus of documents. Another simple method is to extract the most frequent nouns and noun phrases after part-of-speech tagging.

from sklearn.feature_extraction.text import TfidfVectorizer

def extract_keywords_tfidf(documents, num_keywords=5):
    vectorizer = TfidfVectorizer()
    tfidf_matrix = vectorizer.fit_transform(documents)
    feature_names = vectorizer.get_feature_names_out()
    
    # Assuming we want keywords for the first document
    document_index = 0
    feature_scores = zip(feature_names, tfidf_matrix.toarray()[document_index])
    sorted_features = sorted(feature_scores, key=lambda x: x[1], reverse=True)
    
    return [word for word, score in sorted_features[:num_keywords]]

docs = [
    "Natural Language Processing is a subfield of artificial intelligence.",
    "Artificial intelligence aims to enable machines to think and learn.",
    "NLP deals with the interaction between computers and human language."
]

keywords = extract_keywords_tfidf(docs)
print(f"Keywords for the first document: {keywords}")

Python code for keyword extraction using TF-IDF.