Automated event detection from news corpora is a crucial task
towards mining fast-evolving structured knowledge. As real-world
events have different granularities, from the top-level themes to
key events and then to event mentions corresponding to concrete
actions, there are generally two lines of research: (1) theme detection
tries to identify from a news corpus major themes (e.g., “2019 Hong
Kong Protests” versus “2020 U.S. Presidential Election”) which have
very distinct semantics; and (2) action extraction aims to extract
from a single document mention-level actions (e.g., “the police hit
the left arm of the protester”) that are often too fine-grained for
comprehending the real-world event. In this paper, we propose a
new task, key event detection at the intermediate level, which aims
to detect from a news corpus key events (e.g., HK Airport Protest
on Aug. 12-14), each happening at a particular time/location and
focusing on the same topic. This task can bridge event understanding
and structuring and is inherently challenging because of (1) the
thematic and temporal closeness of different key events and (2) the
scarcity of labeled data due to the fast-evolving nature of news articles.
To address these challenges, we develop an unsupervised key
event detection framework, EvMine, that (1) extracts temporally
frequent peak phrases using a novel ttf-itf score, (2) merges peak
phrases into event-indicative feature sets by detecting communities
from our designed peak phrase graph that captures document cooccurrences,
semantic similarities, and temporal closeness signals,
and (3) iteratively retrieves documents related to each key event
by training a classifier with automatically generated pseudo labels
from the event-indicative feature sets and refining the detected key
events using the retrieved documents in each iteration. Extensive
experiments and case studies show EvMine outperforms all the
baseline methods and its ablations on two real-world news corpora.
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UCPhrase: Unsupervised Context-aware Quality Phrase Tagging
Identifying and understanding quality phrases from context is a
fundamental task in text mining. The most challenging part of this
task arguably lies in uncommon, emerging, and domain-specific
phrases. The infrequent nature of these phrases significantly hurts
the performance of phrase mining methods that rely on sufficient
phrase occurrences in the input corpus. Context-aware tagging
models, though not restricted by frequency, heavily rely on domain
experts for either massive sentence-level gold labels or handcrafted
gazetteers. In this work, we propose UCPhrase, a novel
unsupervised context-aware quality phrase tagger. Specifically, we
induce high-quality phrase spans as silver labels from consistently
co-occurring word sequences within each document. Compared
with typical context-agnostic distant supervision based on existing
knowledge bases (KBs), our silver labels root deeply in the input
domain and context, thus having unique advantages in preserving
contextual completeness and capturing emerging, out-of-KB
phrases. Training a conventional neural tagger based on silver labels
usually faces the risk of overfitting phrase surface names. Alternatively,
we observe that the contextualized attention maps generated
from a Transformer-based neural language model effectively reveal
the connections between words in a surface-agnostic way. Therefore,
we pair such attention maps with the silver labels to train a
lightweight span prediction model, which can be applied to new input
to recognize (unseen) quality phrases regardless of their surface
names or frequency. Thorough experiments on various tasks and
datasets, including corpus-level phrase ranking, document-level
keyphrase extraction, and sentence-level phrase tagging, demonstrate
the superiority of our design over state-of-the-art pre-trained,
unsupervised, and distantly supervised methods.
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- NSF-PAR ID:
- 10311080
- Date Published:
- Journal Name:
- KDD'21:The 27th {ACM} {SIGKDD} Conference on Knowledge Discovery and Data Mining, August 14-18, 2021
- Volume:
- 2021
- Issue:
- 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3447548.3467397
