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A dictionary of legal English collocations as an aid for mastering the legal English genre

Paper · 2020 · Katarzyna Mroczyńska

Summary

Studies of legal language have mostly focused on terminology and paid much less attention to phraseology. Learners of legal English, such as Business English students, often cannot find the conventional word partner a context requires. Mroczyńska describes an ongoing project to build a dictionary of legal English collocations from judgments of the UK Supreme Court (2009–2018). The paper surveys three approaches to collocation: frequency-based, semantic (Mel'čuk's phrasemes and Lexical Functions) and pragmatic. It also covers Siepmann's attempt to unify them. The authors then loaded their own corpus into Sketch Engine and used the Word List function to pick the 100 most frequent specialist terms. Word Sketch, ranked by logDice, profiled the collocates of each term. Every entry groups collocates into six syntactic patterns and gives a corpus example for each. The main result so far is the corpus and the entry design; the finished dictionary is promised in a later article. The work matters because collocations show how legal concepts relate to one another, so knowing them helps learners use the genre correctly.

Key points

  • Corpus: 636 UK Supreme Court judgments from 2009–2018, totalling 9,505,800 tokens, 8,098,719 words and 260,419 sentences.
  • Tool: Sketch Engine, using Word List for the frequency list and Word Sketch for collocates. The ranking score is logDice, which is based on the Dice coefficient. Sketch Engine switched to logDice in 2006 from a modified MI-Score and AScore, and adjusted it again in 2015.
  • Headwords are the 100 most frequent specialist terms. A collocate must occur at least 5 times to enter the main entry.
  • Each entry has six patterns: a modifier placed before the term, the term followed by a noun, a verb preceding the term, the term followed by a verb, a preposition preceding the term, and the term followed by a preposition.
    • Collocates within each pattern are listed alphabetically.
    • An example sentence shows the collocation in bold.
    • Patterns with no significant collocates are dropped.
  • Association strength rests on four counts: corpus size, node frequency, collocate frequency and co-occurrences inside a window. Observed co-occurrences are compared with the number expected by chance. More than 50 association measures exist (Evert), including z-score, t-score, MI, log likelihood and Fisher's exact test.

Interesting ideas

  • I1. Group a word's collocates by syntactic slot (modifier, verb-as-head, verb-with-subject, prepositional links) rather than by raw window proximity; this gives a structured profile that is easier to use than a flat neighbour list.
  • I2. Score a pair by how far its observed co-occurrence exceeds what chance predicts from the two word frequencies, using a bounded measure like logDice; raw counts favour frequent words.
  • I3. Treat the collocate as chosen depending on the base word (Mel'čuk): the speaker picks the noun freely, but the verb or modifier that expresses a given meaning with it is conventional.
  • I4. Build a domain lexicon from a small, high-quality, single-source corpus and add a minimum frequency cut-off (here 5) so rare noise stays out.
  • I5. Pair every listed pattern with one real corpus sentence where the pattern is highlighted, so each claim comes with evidence.

For Mova

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

  • I1 (world): Compile per-lemma "sketches" over UD relations (amod, obj, nsubj, case) from parsed corpora, so each concept carries the verbs and modifiers it typically takes.
  • I2 (learn): Use logDice-style scores over head–dependent pairs in silver trees to flag improbable attachments as candidates for repair rules, with chance-level pairs serving as a negative control.
  • I3 (back): During generation, look up the conventional verb or adjective for a given noun and meaning instead of a generic synonym, so the output reads idiomatically.
  • I4 (world): Build domain packs (legal, school textbooks) from curated sources with a frequency threshold, keeping each entry traceable to its example sentences for explainability.
  • I5 (frames): Store an attested example sentence with each learned verb–argument pattern, so a frame decision can point to the usage that licenses it.

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

Also on the byline: University of Siedlce.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 worksPsychology · 798 worksDevelopmental and Educational Psychology · 260 works

🧭 Topics 3Research subjects and languages the work is about.English · 1,373 worksIdioms & multiword expressions · 155 worksLegal AI · 55 works

Cite

Cite as Katarzyna Mroczyńska. 2020. A dictionary of legal English collocations as an aid for mastering the legal English genre. LingBaW 6.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.31743/lingbaw.11836

@inproceedings{mroczynska2020dictionary,
  title = {{A dictionary of legal English collocations as an aid for mastering the legal English genre}},
  author = {Katarzyna Mroczyńska},
  booktitle = {LingBaW 6},
  year = {2020},
  doi = {10.31743/lingbaw.11836},
  url = {https://doi.org/10.31743/lingbaw.11836}
}

References

25 references, 1 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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