Walloccur

Walloccur is a statistical concept used in corpus linguistics and data analysis to measure the local occurrence of elements within a bounded context window. It combines a windowed approach with a weighting scheme to emphasize nearby context more than distant context.

Definition and notation: Given a target item t in a text, a chosen window size and a weighting function defined by distance from t, the walloccur score between t and a context item c is the weight assigned to c whenever it appears within the window around t. This score accumulates across all occurrences of t in the corpus.

Calculation and normalization: The total walloccur around t can be normalized by the total number of context positions considered, or converted into a probability distribution across context items. Common weighting choices include linear decay, where weights decrease evenly with distance, or exponential decay, which gives rapidly diminishing importance to distant context.

Applications: Walloccur is used to analyze word co-occurrence patterns, inform distributional semantic representations, and compare language patterns across different corpora or domains. It can be extended to include multi-dimensional features such as part of speech, syntactic relations, or topics, enabling richer context-sensitive analyses.

Variants and relation to other measures: Walloccur relates to traditional co-occurrence counts and to measures like pointwise mutual information, while offering a flexible, distance-aware perspective. Variants may apply directional weighting (preceding versus following context) or domain-specific weights to reflect particular analytical goals.

History: The term walloccur describes a family of weighted local occurrence measures that have appeared in discussions of co-occurrence analysis and contextual semantics. It is used as a general reference in methodological descriptions and tool implementations for corpus analysis.