Summary of Integrating Supertag Features Into Neural Discontinuous Constituent Parsing, by Lukas Mielczarek
Integrating Supertag Features into Neural Discontinuous Constituent Parsing
by Lukas Mielczarek
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Formal Languages and Automata Theory (cs.FL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to syntactic parsing, dubbed Transition-Based Parsing, has been proposed to efficiently handle non-local dependencies in languages like German. This method eliminates the need for an explicit grammar, instead relying on neural networks trained on large annotated corpora to produce trees given raw text input. The proposal, developed by Coavoux and Cohen (2019), successfully enables the derivation of any discontinuous constituent tree over a sentence in worst-case quadratic time. The approach has been demonstrated on datasets such as NeGra and TIGER for German and DPTB for English. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Syntactic parsing is important in natural language processing, allowing us to understand how words are related to each other. Traditionally, this was done by looking at adjacent words, but this doesn’t work well for languages that have non-local dependencies, like German. To solve this problem, some treebanks represent long-range dependencies using crossing edges. Other approaches use grammar formalisms to describe discontinuous trees, but these can be slow and inefficient. The Transition-Based Parsing method is a new way to do syntactic parsing that doesn’t need an explicit grammar. Instead, it uses neural networks trained on large datasets to produce trees from raw text input. |
Keywords
» Artificial intelligence » Natural language processing » Parsing
