Sentence boundary determination

Sentence boundary determination

Sentence boundary determination is the task of identifying the boundaries between sentences within a body of text. This process is essential for various natural language processing tasks, including text segmentation, parsing, and information extraction. In brief, sentence boundary determination typically involves the following steps:

1. Tokenization: The text is segmented into individual tokens, usually words or punctuation marks, to delineate the boundaries between linguistic units.

2. Heuristic Rules: Various heuristic rules and patterns are applied to identify potential sentence boundaries. These rules may include recognizing punctuation marks such as periods, question marks, and exclamation points as sentence terminators. However, this approach may not be foolproof, as punctuation marks can appear within sentences in certain contexts, such as abbreviations, titles, or quotes.

3. Contextual Analysis: Contextual analysis techniques are employed to differentiate between legitimate sentence terminators and instances where punctuation marks occur within sentences. Contextual clues, such as capitalization patterns, abbreviations, and sentence structure, are utilized to determine sentence boundaries more accurately.

4. Machine Learning Approaches: Machine learning models, particularly sequence labeling models like conditional random fields (CRFs) or recurrent neural networks (RNNs), can be trained on annotated corpora to automatically detect sentence boundaries based on linguistic features and context.

5. Evaluation: The performance of sentence boundary determination systems is evaluated using metrics such as precision, recall, and F1-score, based on comparisons between the system-generated boundaries and manually annotated ground truth boundaries.