NLP

Inspiration

The inspiration behind this code seems to be the exploration of Natural Language Processing (NLP) techniques, particularly in the context of text processing, language modeling, and interactive text generation (mad libs).

What it does

It preprocesses text data by removing HTML tags, converting text to lowercase, removing punctuation, and eliminating stopwords. It builds a bigram language model using Maximum Likelihood Estimation (MLE) based on the provided text data. It generates mad libs by prompting the user to input words for placeholders in a given template, then replacing the placeholders with the user inputs.

How we built it

The code is built using the Python programming language and leverages the Natural Language Toolkit (NLTK) library for various NLP tasks such as tokenization, stopwords removal, and language modeling. It follows a modular approach, with functions defined for each task.

Challenges we ran into

Ensuring proper tokenization and preprocessing of the text data. Handling edge cases, such as when no bigrams are found in the text data. Designing a user-friendly interface for mad libs generation.

Accomplishments that we're proud of

Successfully implementing text preprocessing techniques to clean and prepare text data for modeling. Building a language model capable of capturing the sequential nature of language using bigrams. Creating an interactive mad libs generator that engages users in providing input for text generation.

What we learned

Practical techniques for text preprocessing, including HTML tag removal, case normalization, and punctuation removal. How to build a simple language model using NLTK for tasks such as next word prediction. Interaction design principles for user input in text generation applications.

What's next for NLP

Advanced Language Models: Explore more advanced language models such as transformer-based models like BERT or GPT. Semantic Understanding: Dive deeper into techniques for understanding the meaning and context of text, including sentiment analysis, entity recognition, and coreference resolution. Multimodal Processing: Incorporate techniques for processing and understanding text alongside other modalities such as images or audio. Ethical Considerations: Consider the ethical implications of NLP technologies, including biases in training data and potential misuse of language models.

Built With

• python