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A Fundamental Algorithm for Dependency Parsing (With Corrections)

Michael A. Covington

TL;DR

The paper advocates a fundamental, word-at-a-time dependency parsing framework that aligns with cognitive constraints and contrasts with constituency parsing. It starts from a brute-force left-to-right strategy and progressively introduces refinements—using list-based structures and explicit constraints to enforce unity, uniqueness, and (where appropriate) projectivity—culminating in the LSU and LSUP variants. It clarifies the formal equivalence between dependency and constituency representations under head-designation, discusses the practical advantages of dependency links for semantics, and provides a rigorous complexity assessment, including NP-completeness in the presence of lexical ambiguity. The work highlights both the theoretical foundations and algorithmic pathways for incremental, cognitively plausible parsing, with implications for implementing robust, efficient parsers in language processing systems.

Abstract

This paper presents a fundamental algorithm for parsing natural language sentences into dependency trees. Unlike phrase-structure (constituency) parsers, this algorithm operates one word at a time, attaching each word as soon as it can be attached, corresponding to properties claimed for the parser in the human brain. Like phrase-structure parsing, its worst-case complexity is $O(n^3)$, but in human language, the worst case occurs only for small $n$.

A Fundamental Algorithm for Dependency Parsing (With Corrections)

TL;DR

The paper advocates a fundamental, word-at-a-time dependency parsing framework that aligns with cognitive constraints and contrasts with constituency parsing. It starts from a brute-force left-to-right strategy and progressively introduces refinements—using list-based structures and explicit constraints to enforce unity, uniqueness, and (where appropriate) projectivity—culminating in the LSU and LSUP variants. It clarifies the formal equivalence between dependency and constituency representations under head-designation, discusses the practical advantages of dependency links for semantics, and provides a rigorous complexity assessment, including NP-completeness in the presence of lexical ambiguity. The work highlights both the theoretical foundations and algorithmic pathways for incremental, cognitively plausible parsing, with implications for implementing robust, efficient parsers in language processing systems.

Abstract

This paper presents a fundamental algorithm for parsing natural language sentences into dependency trees. Unlike phrase-structure (constituency) parsers, this algorithm operates one word at a time, attaching each word as soon as it can be attached, corresponding to properties claimed for the parser in the human brain. Like phrase-structure parsing, its worst-case complexity is , but in human language, the worst case occurs only for small .
Paper Structure (15 sections, 7 figures)

This paper contains 15 sections, 7 figures.

Table of Contents

  1. Overview.
  2. Dependency grammar.
  3. The key concept.
  4. Dependency trees.
  5. Generative power.
  6. The appeal of dependency parsing.
  7. The parsing task.
  8. Basic assumptions.
  9. Simplifying assumptions.
  10. The obvious parsing strategy.
  11. Refining the algorithms.
  12. Projectivity.
  13. Definition.
  14. Building projectivity into the parser.
  15. Complexity.

Figures (7)

  • Figure 1: A constituency tree.
  • Figure 2: A dependency tree is a set of links connecting heads to dependents.
  • Figure 3: This representation of a dependency tree preserves the word order while depicting the tree structure plainly. To get from a head to its dependents, go downhill.
  • Figure 4: Equivalent dependency and constituency trees.
  • Figure 5: Some projective (non-crossing) structures that any dependency parser should handle.
  • ...and 2 more figures