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# A Lexicalized Tree Adjoining Grammar for English

**Authors:** XTAG Research Group · **Year:** 1998 · **Venue:** arXiv:cs/9809024 (XTAG technical report, release of 31 August 1998; Institute for Research in Cognitive Science, University of Pennsylvania) **Link:** [arxiv.org/abs/cs/9809024](https://arxiv.org/abs/cs/9809024) · **Licence:** arXiv — no licence stated — summary only

[**8** citations](https://openalex.org/works?filter=cites:W4394660545) openalex · 2026-10-18[**32** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/group-1998-lexicalized-tree-adjoining-grammar-english/) of the 140673 papers whose bibliographies we read[arXiv:cs/9809024](https://openalex.org/S4306400194)

- [Publisher's page](https://arxiv.org/abs/cs/9809024)
- [PDF](https://arxiv.org/pdf/cs/9809024)
- [arXiv cs/9809024](https://arxiv.org/abs/cs/9809024)
- [OpenAlex](https://openalex.org/W4394660545)

**Authors (1)**

1. [XTAG Research Group](https://openalex.org/A5095459089) h 1 · 8 citations · 1 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

This is a long technical report (read in part: contents, the system chapter and the parsing pipeline; the verb-class and filter chapters were not read in full) describing the English XTAG grammar, written in lexicalized Tree Adjoining Grammar with feature structures. Each word selects one or more elementary trees from a tree database, and the trees are combined by substitution and adjunction. The grammar has 1,094 trees, grouped into 52 tree families (the subcategorisation classes, such as intransitive, transitive, ditransitive, sentential complement) and 218 single trees. Its morphological database has about 317,000 inflected items from over 90,000 stems, and the syntactic database has more than 30,000 entries that link stems to trees. The parsing pipeline reduces ambiguity in steps: the morphological analyser and tagger (the tagger alone cuts parse time by 93%), tree selection through the syntactic database, default trees for unknown words by POS, and filters. Structural filters remove trees with impossible spans, and a unigram filter trained on about 6,000 hand-corrected sentences removes unlikely trees; together they speed runtime by about 87%.

## Ideas

- I1. Verb frames as tree families: in `world/src/dictionary.rs`, give each verb a class from a small closed set (intransitive, transitive, ditransitive, ditransitive with PP, sentential complement) and show that the class is what the frame reader needs. Start with the verb chapter's classes, not the full tree set.
- I2. A structural filter before the graph parser (`gparse`): drop head-dependent arcs that cannot hold given the word order and the frame of the verb (for example, a second object after a PP complement). Measure the drop in candidate arcs and check that the gold-arc recall stays at 100% on UD EWT dev, twice.
- I3. A unigram filter on candidate frames, trained only on the training split, for the choice of frame when a verb has several. Report accuracy and the run time, with a negative control in which the counts are shuffled across verbs.
- I4. Default frames by POS for unknown words (the paper's default assignment), so that an unknown verb gets the most common frame and the choice is written out in the explanation.

## For Mova

The chapters are [13. Verbs](https://md.mova.elamur.ai/free/en/docs/languages/en/13-verbs/) (verb classes and complements), [21. The simple clause](https://md.mova.elamur.ai/free/en/docs/languages/en/21-clause-structure/) (clause patterns) and [23. Complementation](https://md.mova.elamur.ai/free/en/docs/languages/en/23-complementation/) (sentential complements). The code is `gparse` (the arc filter), `en/src/parse.rs`, and `world/src/dictionary.rs`. Measures: gold-arc recall after the filter, parse time, and LAS on UD EWT dev, run twice. The report's licence is not stated, so use the design and the list of classes as ideas and build all data from Mova's own sources.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [arXiv cs/9809024](https://arxiv.org/abs/cs/9809024)

```
@misc{group1998lexicalized,
  title = {{A Lexicalized Tree Adjoining Grammar for English}},
  author = {Group, XTAG Research},
  year = {1998},
  eprint = {cs/9809024},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/cs/9809024}
}
```

- [← previous Ma(r)king concessions in English and German](https://md.mova.elamur.ai/free/en/docs/papers/grote-1995-ma-r-king-concessions-english/)
- [next → Semantic Inference at the Lexical-Syntactic Level for Textual Entailment Recognition](https://md.mova.elamur.ai/free/en/docs/papers/haim-2007-semantic-inference-lexical-syntactic-level/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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