A Dependency Parser for Tweets

What it is about

Tweets break the assumptions of newswire parsers: many tokens (retweet markers, usernames, hashtags, URLs, punctuation) have no syntactic role, a tweet can hold several utterances with their own roots, and multiword names and idioms are common. The authors annotate 929 POS-tagged tweets (Tweebank, about 12,000 tokens) directly as dependencies, with a graph-fragment notation that lets annotators leave tokens unselected, group multiword expressions, and mark several roots. Their parser, TweeboParser, is a second-order TurboParser adapted to skip unselected tokens (they stay as features), to allow several roots on a wall symbol, and to use a Penn Treebank parser's arc scores as a stacking feature, with Brown clusters from tweets. On the new test set of 201 tweets the parser reaches 80.9 unlabeled F1, against 76.1 on an older Penn-style test set, and its ablations show that the parsing adaptations are worth 1.7 points and the Penn Treebank feature 0.7 points on the new test set (3.5 points on the Penn-style set).

Ideas

• I1. en/src/parse.rs unselected tokens: exclude punctuation and other non-syntactic tokens from the arcs but keep them as features of neighbouring arcs, as the paper's decoder does; measure UAS and LAS on EWT test with and without punctuation arcs (the paper's Table 3 shows about 2 points on tweets, our number will differ).
• I2. Wall-symbol multi-root for utterance-level data: allow several roots in the en parser for CHILDES and chat lines, behind a dialect flag, while the registry gate still rejects them in edited text; measure the number of multi-root sentences rejected per 1k lines of CHILDES with the gate on and off.
• I3. en/src/morph.rs and en/seeds/lexicon: group multiword names and idioms as one unit, parse the unit internally, and reattach the arcs to its head, as the paper does; measure head attachment on EWT test sentences that contain such units.
• I4. Robustness check on Tweebank (test only, the data is non-commercial): UAS of the en parser trained on EWT and ESLspok, against the paper's 80.9 on its new test set; use it as a domain-shift number, not as a target.

For Mova

Touches languages/en/38-dialogue-and-discourse-markers, 19-lexicon-mwe-register and 37-register-and-variation, and the en crate (parse.rs, morph.rs, ctx.rs), tools/treeagree and the multi-root rule of the mova dialect. Tweebank is non-commercial: test only. Measure first the effect of excluding punctuation arcs on EWT test, which costs little and tests the idea behind I1.

Topics: Dependency parsing

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.3115/v1/d14-1108

@inproceedings{kong2014dependency,
  title = {{A Dependency Parser for Tweets}},
  author = {Kong, Lingpeng and Schneider, Nathan and Swayamdipta, Swabha and Bhatia, Archna and Dyer, Chris and Smith, Noah A.},
  booktitle = {Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2014},
  doi = {10.3115/v1/d14-1108},
  url = {https://doi.org/10.3115/v1/d14-1108}
}

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