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Major Life Event Extraction from Twitter based on Congratulations/Condolences Speech Acts

Paper · 2014 · Jiwei Li, Alan Ritter, Claire Cardie et al.

Summary

The paper tackles a problem earlier Twitter event work had largely ignored: finding the private milestones of ordinary users, such as weddings, graduations, new jobs or births, and describing them in structured form. Such posts are rare, phrased in many ways, and mixed with wishes, gossip about others and past events, so labelled data is scarce. The authors avoid labelling the posts directly. Instead they look at replies: congratulations or condolences are expressed in a small set of phrasings and can be detected with high precision, and the message they answer is taken as a likely life event. Starting from seed replies, they cluster the answered conversations with LDA, label the coherent topics by hand, and bootstrap new reply patterns and topics. A three-stage pipeline then classifies the event type, checks that the speaker is personally involved, and extracts event properties with a CRF. On real-world identification the system reaches 0.62 precision and 0.48 recall, well above the compared approaches. The work shows that social reactions can serve as cheap supervision for rare, open-ended categories.

Key points

  • The data were drawn from CMU's 2011 Twitter Warehouse, a collection covering roughly 10% of all tweets posted that year. Each whole conversation, with the seed reply masked, was modelled as one LDA document (120 topics). Each sentence received a single topic, only verbs and nouns were kept, and inference used collapsed Gibbs sampling.
  • Bootstrapping ran for 4 iterations and used streaming LDA to preserve topics already labelled. In each round, a person screened the top 20 replies to newly harvested tweets. The process ended with 30 congratulation/condolence patterns and 42 coherent life-event types.
  • The pipeline has three stages:
    • Pipeline1 identifies the event category and filters out irrelevant tweets.
    • Pipeline2 keeps only self-reported events, so "my friend got married" is filtered out.
    • Pipeline3 extracts event properties, such as the spouse or the university, using a CRF.
  • In end-to-end real-world identification, the system scored precision 0.62 and recall 0.48. The two compared approaches scored 0.13/0.20 and 0.25/0.18.
  • Training the event classifier on topics from successive bootstrap steps (24, 38 and 42 classes) kept precision roughly flat (0.65, 0.64, 0.62). Recall rose steadily (0.36, 0.43, 0.48).

Interesting ideas

  • I1. Label a hard-to-recognise message by the easy-to-recognise reaction it provokes; constrained reply speech acts act as a high-precision proxy annotator for a diverse, open-ended target class.
  • I2. Mask the trigger signal before clustering, so that topics reflect the content of the event rather than the seed words used to collect it.
  • I3. Restrict the topic model to verbs and nouns and assign one topic per sentence, which the authors found gives clearer, more coherent clusters than full bag-of-words.
  • I4. Break the task into a cascade that separates "is this an event?", "is the speaker involved?" and "what are its details?", with the involvement stage explicitly rejecting wishes, third parties and past mentions.
  • I5. Measure each bootstrap round separately to show that precision holds while recall grows, which justifies the iterations empirically.

For Mova

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

  • I1 (learn): silver data for event or state-change frames could be harvested by finding texts followed by formulaic responses (thanks, apologies, congratulations) rather than labelling the events themselves.
  • I2 (learn): when building silver sets from seed patterns, removing the seed cue from features would act as a built-in guard against the model learning the harvesting trick instead of the phenomenon.
  • I3 (world): event-type induction over the world of a text could cluster predicate–argument heads from UD trees instead of raw tokens, keeping the clusters explainable.
  • I4 (frames): speech-act and frame analysis could carry explicit agent-is-speaker, modality (wish vs. fact) and tense checks before an event enters the text's world.
  • I5 (learn): self-induced repair rules or silver-data expansion rounds could be accepted only when per-round evaluation shows stable precision against a fixed held-out set.

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

🎓Stanford UniversityStanford · United States310 works · 284,480 citations🎓Carnegie Mellon UniversityPittsburgh · United States365 works · 158,048 citations🎓Cornell UniversityIthaca · United States89 works · 58,422 citations🎓The Ohio State UniversityColumbus · United States57 works · 17,793 citations

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 worksInformation Systems · 523 works

🧭 Topics 2Research subjects and languages the work is about.Information extraction · 109 worksSpeech act theory · 160 works

Cite

Cite as Jiwei Li, Alan Ritter, Claire Cardie and Eduard Hovy. 2014. Major Life Event Extraction from Twitter based on Congratulations/Condolences Speech Acts. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.3115/v1/d14-1214

@inproceedings{li2014major,
  title = {{Major Life Event Extraction from Twitter based on Congratulations/Condolences Speech Acts}},
  author = {Jiwei Li, Alan Ritter, Claire Cardie and Eduard Hovy},
  booktitle = {Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2014},
  doi = {10.3115/v1/d14-1214},
  url = {https://doi.org/10.3115/v1/d14-1214}
}

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