Sentence segmentation is a fundamental process in natural language processing. It involves breaking down a given text into individual sentences, a task that finds applications in various contexts. Whether you need to split a paragraph into sentences for further analysis or present sentence boundaries in a user-friendly frontend application, sentence segmentation is crucial.
At first glance, identifying sentence boundaries might seem straightforward – just look for a period or full stop. However, it quickly becomes complex when you consider cases where a period is used for abbreviations such as “Dr.” or in numerical values like “3.14.” This simple punctuation mark doesn’t always signal the end of a sentence.
In many languages, the period isn’t the standard sentence delimiter. For instance, Hindi uses a unique character called the ‘danda’ sign (।) to indicate the end of a sentence. Additionally, sentence segmentation must account for periods inside quotations, which don’t denote sentence boundaries, or periods within email addresses, which certainly aren’t sentence endings.
The challenge lies in finding sentence segmentation libraries that can handle these complexities across a wide range of languages while addressing language-specific intricacies. Most existing libraries fall short in this regard. They tend to focus on English or support only a limited number of languages. Furthermore, they may not be adequately performant or actively maintained.
In situations where ambiguity arises and linguistic insight is needed, our library errs on the side of caution, avoiding unnecessary splits rather than risking incorrect segmentations. Our performance benchmarks reveal that this library not only delivers impressive speed but also excels in evaluation datasets.
The name sentencex stands for sentence extraction or sentence✂️
Our overarching goal is to provide support for all languages that have a presence on Wikipedia. Instead of defaulting to English for languages not explicitly defined in the library, we’ve implemented a fallback chain mechanism. This means that the closest language included in the library will be utilized. We’ve defined fallbacks for approximately 244 languages, and we also have abbreviation data available for around 30 languages.
sentencex python library
sentencex js library
This library is already in use in MinT project. It is also replacing the minimal sentence segmentation libray we had in CX-cxserver project. As we start using it in more project, we hope to support more languages and existing languages better.
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