How to Identify "Sentences" in Transcripts📝?

Have you ever noticed that YouTube transcripts doesn’t have the conventional concept of "sentences"?

Transcripts generated from live video or audio lack prior knowledge of intended scripts with punctuations. As a result, there's no automatic delineation of "sentences" marked by punctuations.

The challenge arises when dealing with lengthy text transcripts. Imagine downloading transcripts from a video or audio without any punctuations. Wouldn’t reading a file like that make you dizzy?

In this blog post, we will explore some potential solutions to this problem, including natural language processing (NLP) techniques, machine learning models, and rule-based methods.

The Challenge

When faced with a long text with no punctuations, the task of identifying sentences becomes non-trivial. This lack of punctuation can occur in various scenarios, such as transcriptions of spoken language, informal communication, or historical texts.

The absence of sentence boundaries hinders downstream natural language processing tasks and hampers direct information consumption, making it essential to find robust methods for sentence identification in such contexts.

Importance and Use Cases

The ability to identify sentences in unpunctuated text is crucial for several applications:

1. Transcribing Audio Files: Speech-to-text transcription often yields unpunctuated text, requiring subsequent sentence boundary identification for readability and analysis.

2. Text Processing: Unpunctuated text is prevalent in social media posts, informal communication, and certain literary works. Automatic sentence identification facilitates information extraction and analysis in these domains.

3. Language Understanding Models: Training language models on diverse data, including unpunctuated text, necessitates accurate sentence boundary disambiguation to capture the underlying linguistic structure.

Potential Solutions

When dealing with a long text without any punctuations, there are two main approaches to identify sentences: adding punctuations first and then extracting sentences, or directly identifying sentences. Both methods have their own challenges and can be used based on the specific requirements of the task.

There are some common methods that each approach can utilize including:

• Natural language processing libraries: NLP libraries like NLTK (Natural Language Toolkit) or SpaCy, which has robust sentence boundary detection capabilities are good choices.

• Rule-Based Methods: Developing rule-based systems that leverage language-specific syntactic and semantic rules is another approach.

• Pre-Trained Models and APIs: Leveraging pre-trained models and third-party APIs specifically designed for punctuation restoration and sentence boundary detection can provide accurate and efficient solutions for handling unpunctuated text.

1. Adding Punctuations First

Adding punctuations to the text can help in identifying sentences more accurately, as punctuations such as periods, question marks, and exclamation points are used to mark the end of a sentence. This approach may involve using natural language processing tools or regular expressions to add appropriate punctuations to the text. Once the punctuations are added, sentences can be easily extracted using the newly added punctuations as delimiters.

Among the common solutions, using pre-trained models worked best so far from our experiments. Particularly, the Transformer model oliverguhr/fullstop-punctuation-multilang-large works extremely well. This model predicts the punctuation of English, Italian, French and German texts.

It’s very straightforward to use:
pip install deepmultilingualpunctuation

The code snippet below shows how to restore punctuation for a text. (An excerpt of the transcript is shown in the code below due to the length of the file.)

Punctuation transformer model

2. Directly Identifying Sentences

On the other hand, there are methods to directly identify sentences in a text without adding punctuations. This approach often involves using natural language processing techniques and machine learning models that are trained to recognize sentence boundaries based on the contextual and syntactic features of the text.