---
title: "Comparing Knowledge-Intensive and Data-Intensive Models for"
description: "To represent the data-intensive side fairly, the authors build a new factorization-based parser. 80 references, cited by 3 papers here."
authors: ["Junjie Cao", "Zi Lin", "Weiwei Sun", "Xiaojun Wan"]
year: 2021
venue: "Computational Linguistics"
source: "https://aclanthology.org/2021.cl-1.3/"
doi: "10.1162/coli_a_00395"
licence: "CC BY-NC-ND 4.0 (Computational Linguistics, MIT Press)"
licence_url: "https://creativecommons.org/licenses/by-nc-nd/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/cao-2021-comparing-knowledge-intensive-data-intensive/"
---
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# Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing

**Authors:** [Junjie Cao](https://md.research.elamur.ai/free/en/authors/junjie-cao/), [Zi Lin](https://md.research.elamur.ai/free/en/authors/zi-lin/), [Weiwei Sun](https://md.research.elamur.ai/free/en/authors/weiwei-sun/) and [Xiaojun Wan](https://md.research.elamur.ai/free/en/authors/xiaojun-wan/) · **Year:** 2021 · **Venue:** [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/) ([Computational Linguistics 2021](https://md.research.elamur.ai/free/en/venues/cl/2021/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY-NC-ND 4.0 (Computational Linguistics, MIT Press)](https://creativecommons.org/licenses/by-nc-nd/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.cl-1.3/) · [PDF](https://aclanthology.org/2021.cl-1.3.pdf) · [DOI 10.1162/coli\_a\_00395](https://doi.org/10.1162/coli_a_00395) · [OpenAlex](https://openalex.org/W3133919387)

**Citations:** 3 citations (OpenAlex, 2026-10-14) · 3 in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks how two families of English Resource Semantics (ERS) parsers differ in the linguistic phenomena they handle well. One family is grammar-driven and knowledge-intensive: the ACE parser running the hand-built, HPSG-based ERG. The other is neural and data-intensive. To represent the data-intensive side fairly, the authors build a new factorization-based parser. It scores candidate parts of an Elementary Dependency Structure (EDS) graph using contextualized word and concept embeddings, and it uses no explicit grammar. Trained and tested on DeepBank, this parser reaches 95.05 SMATCH with gold tokenization, clearly beating earlier data-driven EDS parsers. The authors then go beyond a single overall score. They probe both parser types with construction-specific test suites covering lexical, phrasal and non-local dependency phenomena, including the ten constructions of Bender et al. (2011). They also run learning-curve experiments on down-sampled training data. Overall accuracy is similar, but the error profiles differ systematically, and the differences follow from each approach's design. This suggests concrete hybrid directions for future parsers.

## Key points
- The new parser scores 95.05 SMATCH on EDS. It outperforms the strongest transition-based system, from Buys and Blunsom (2017), by 8.05 points and the composition-based parser of Chen, Sun, and Wan (2018) by 4.19. Two top systems in the CoNLL 2019 MRP shared task later re-implemented or extended it.
- The ERG (release 1214) has 225 syntactic rules, 70 lexical rules and about 39,000 lemmata over 975 leaf lexical types. It yields full analyses for roughly 85–95% of sentences in real corpora.
- On the Bender et al. constructions, ACE dominates where the grammar fully covers the phenomenon. For itexpl, ACE scores 91 recall against at most 74 for the neural variants.
- The neural parser wins on local or grammar-uncovered cases. For absol(A), ELMo* scores 92 against 61 for ACE, and it also does better on barerel.
- All parsers struggle with long-distance dependencies such as rnr(B), absol(B) and argadj(B). Adding an LSTM layer on top of ELMo helps on several distant relations, for example tough(B) and control(B).

## Interesting ideas
- Evaluate a parser by linguistic phenomenon, not only by one aggregate score, because two systems with equal totals can fail on completely different constructions.
- Plot learning curves per construction on down-sampled training data to tell which phenomena are limited by data size and which stay hard however much data is added (here, valency-increasing constructions stayed low).
- Treat grammar-driven and data-driven parsers as complementary witnesses: the grammar is reliable where it has coverage, and the neural model handles peripheral or uncovered patterns.
- Align every semantic node to a span of the surface string, including abstract nodes such as passive, compound or temporal relations. This keeps the meaning graph explainable and traceable back to the words.
- Add explicit nodes for grammatical meanings like compounds and unspecified temporal location, alongside nodes for lexical predicates.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): Build per-construction test suites (passives, control, right node raising, compounds) so that regressions in a parser or repair rule show up where averages would hide them.
- **I2** (learn): Use such curves to decide where to spend silver data or LLM-teacher effort and where a hand-written rule is the better investment.
- **I3** (learn): Weight witness parsers per construction when agreeing silver data, trusting the rule-based witness on phenomena it covers and the neural one elsewhere.
- **I4** (frames): Give each frame element and each implicit relation (passive agent, temporal locative) an explicit token span, so that every role decision can be traced to the text.
- **I5** (world): Represent constructions such as noun compounds and bare temporal NPs as typed relations in the world model rather than dropping them.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Peking University](https://md.research.elamur.ai/free/en/universities/peking-university/) — University, Beijing, China · 244 works · 39,359 citations

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🧭 Topics (2): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,263 works · [🧾 Semantic parsing & meaning representation](https://md.research.elamur.ai/free/en/topics/semantic-parsing/) · 185 works

## Cite

Cite as: Junjie Cao, Zi Lin, Weiwei Sun and Xiaojun Wan. 2021. Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing. Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{cao2021comparing,
  title = {{Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing}},
  author = {Junjie Cao, Zi Lin, Weiwei Sun and Xiaojun Wan},
  journal = {Computational Linguistics},
  year = {2021},
  doi = {10.1162/coli\_a\_00395},
  url = {https://doi.org/10.1162/coli_a_00395}
}
```

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80 references, 31 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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