A “text span” is a continuous sequence of characters, words, or tokens within a larger body of text. In artificial intelligence and natural language processing (NLP), the term describes a segment of text that is often selected or referenced for further analysis, annotation, or processing. Text spans are fundamental to many NLP tasks, such as named-entity recognition (NER), information extraction, question answering, and text classification.
For example, in the sentence “Marie Curie won two Nobel Prizes,” the phrase “Marie Curie” is a text span that might be labeled as a PERSON entity in NER. Similarly, in a document retrieval system, a text span could refer to the exact portion of text that answers a user’s query. In these contexts, text spans help models focus attention on relevant portions of the data, making it possible to extract meaningful information.
Text spans can be represented in several ways. Most commonly, they are defined by their start and end indices in the text. For instance, a text span might be described by specifying that it starts at character 0 and ends at character 11, which would capture “Marie Curie” in the example above. In tokenized text, spans are often referenced by token indices rather than character indices.
In AI annotation workflows, human annotators or automated tools select text spans to tag with specific labels or categories. These labels might denote entities, sentiment, intent, or any other attribute relevant to the task. The quality and consistency of span annotations directly influence the performance of downstream models. Ambiguity about what constitutes a span (for example, whether to include punctuation or how to handle overlapping spans) is a common challenge and is often resolved by clear annotation guidelines and inter-annotator agreement.
Text span extraction forms the backbone of many advanced NLP models, especially those designed for tasks like extractive question answering. In such tasks, the model‘s job is to select the most relevant span from a source document as its answer. Recent large language models, such as those based on the transformer architecture, use sophisticated mechanisms to learn which spans are most likely to contain the information needed to complete a task.
Text spans are not restricted to single words or fixed-length phrases. They can vary in length depending on the use case. Some spans might be just a single word, while others could be entire sentences or paragraphs. The flexibility and importance of text spans make them a core abstraction in the design of annotation interfaces, data labeling tools, and NLP model architectures.
In summary, text spans are essential building blocks in AI and NLP systems, enabling targeted analysis, precise labeling, and effective information extraction from unstructured text. Understanding and handling text spans properly is key to building robust language models and annotation pipelines.
