[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84267-en":3,"doc-seo-84267-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},84267,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Environment-Sensitive Lexicographic Disambiguation for Contextual Parsing","A deterministic algorithm resolves ambiguity in parse trees by maintaining a global mutable context during incremental Abstract Syntax Tree (AST) construction. Competing derivations at each non-terminal are compared via a tournament-style lexicographic selection, discarding unselected alternatives based on contextual decisions. Unlike post-processing methods, semantic state is preserved throughout parsing so earlier context influences later ambiguities. The approach performs document-environment-driven syntactic disambiguation, modeling complex relationships across previous constructs and alternative derivations while providing consistent procedural disambiguation.","Environment-Sensitive Lexicographic Disambiguation  \nfor Contextual Parsing  \nAlejandro Luis Vaz Mayato  \narXiv :2607 .07728v 1 [ cs .PL] 6 Jul 2026  \nAbstract  \nThis paper presents a deterministic algorithm for resolving ambiguity in parse trees using a global mutable context. The proposed method applies a tournament-style selection process to competing derivations at each non-terminal, systematically discarding alternatives whose non-terminal subtrees are not selected by the contextual decision mechanism.  \nUnlike approaches that rely on post-processing, the algorithm maintains semantic state throughout incremental Abstract Syntax Tree (AST) building, allowing earlier decisions to influence the resolution of future ambiguities. This context-aware strategy enables consistent and procedural disambiguation after parsing.  \nIt features syntactic disambiguation based on a document environment instead of relying on ad-hoc rules, thus able to model complex relationships between previous constructsand the different derivations for a same non-terminal.  \nACM Reference Format:  \nAlejandro Luis Vaz Mayato. 2026. Environment-Sensitive Lexicographic Disambiguation for Contextual Parsing. In . ACM, New York, NY, USA, 9 pages. [https://doi.org/10.1145/nnnnnnn.nnnnnnn](https://doi.org/10.1145/nnnnnnn.nnnnnnn)  \n1 Introduction  \n1.1 Goal  \nThis paper aims to introduce a contextual ambiguity solver algorithm[3] that clears ambiguity based on a shared mutable context.  \nThis mechanism is a generalization of top-down, left-toright, single-pass, context-dependent disambiguation; that can be used in grammars where ambiguity is bounded but cannot be resolved locally.  \nTo our knowledge, no previous work explicitly formulates a deterministic, incremental, bounded-ambiguity, lexicographic elimination procedure over SPPFs with formal guarantees on termination and complexity.  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific [permission and/or a fee. Request permissions from permissions@acm.org](permission and/or a fee. Request permissions from permissions@acm.org). Conference’17, Washington, DC, USA  \n© 2026 Copyright held by the owner/author(s) . Publication rights licensed to ACM.  \nACM ISBN 978-x-xxxx-xxxx-x/YYYY/MM [https://doi.org/10.1145/nnnnnnn.nnnnnnn](https://doi.org/10.1145/nnnnnnn.nnnnnnn)  \n1.2 Problem  \nProgramming language grammars often depend on contextual information in order to resolve the meaning of certain constructs. In those cases, deciding between two or more possible derivations is only possible with imported definitions from elsewhere in the document. Many languages, like C with typedef disambiguation or Rust’s function call versus tuple struct have devised clever methods or multi-stage intermediate representations in order to peek into the document context and decide for a correct interpretation of the ambiguous construct.  \nThroughout this paper, we’ll use a classic example of ambiguity that depends on an environment to be resolved to clarify some explanations. We’ll be working with a mathlike domain-specific-language, where we must distinguish between implicit multiplication of a variable with a nested expression and a function call:  \nf(0)  \nThe reason this is an ambiguous token sequence is that it can represent two different AST nodes and there is not enough information locally to decide.  \n1.3 Limitations of Current Approaches  \nWe distinguish three current approaches that try to overcome this obstacle:  \n• Multi-stage AST building  \n• Pre-selected choices  \n• N","cbCaik47JyuqKvCA","https://ap.wps.com/l/cbCaik47JyuqKvCA","pdf",597378,4,1,9,"English","en",105,"# Introduction\n## Goal\n## Problem\n## Limitations of Current Approaches\n## Core Idea\n## Contributions","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper addresses ambiguity in parse trees for programming language grammars where the correct choice between derivations depends on information from the surrounding document environment.\"},{\"question\":\"How does the proposed algorithm resolve ambiguities?\",\"answer\":\"It uses a global mutable context and a tournament-style lexicographic selection to choose among competing derivations at each non-terminal, discarding alternatives whose subtrees are not selected by contextual decisions.\"},{\"question\":\"How is the method different from approaches that use post-processing?\",\"answer\":\"The algorithm maintains semantic state throughout incremental AST building, allowing earlier contextual decisions to affect how future ambiguities are resolved, rather than deciding only after parsing is 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