Entity-oriented filtering of large streams
John R. [email protected]
Dan A. [email protected]
http://trec-kba.org
Date: Tue, 13 Mar 2012 02:45:40 +0000
From: Google Alerts <[email protected]>
Subject: Google Alert - "John R. Frank"
=== Web - 2 new results for ["John R. Frank"] ===
John R. Frank
SPOKANE, Wash. - John R. Frank, 55, died March 4, 2012, in Coeur d' Alene,
Idaho. Survivors include: his wife, Miki; daughter, Patricia Frank; ...
<http://www.hutchnews.com/obituaries/Frank--John-CP>
In Memory of John R Frank
Biography. John R. Frank, age 55, passed away at Sacred Heart Medical
Center in Spokane, WA, on March 4, 2012. John was born in Hutchison, KS, ...
<http://www.englishfuneralchapel.com/sitemaker/sites/Englis1/obit.cgi?user=583335Frank>
2012 Task:Filtering to Recommend Citations
1) Initialize with a target WP entity• state of WP from Jan 2012
2) Iterate over stream of text items• Oct-Dec 2011: train on labels
3) For each, output relevance between 0, 1• Jan-Apr 2012: labels hidden
Content Stream•462M texts, 40% English•4,973 hourly chunks of a 105 docs/hour•News, blogs, forums, and link shortening
Your KBA System
Entities in Wikipedia or another Knowledge Base
Automatically recommend
new edits
s3://aws-publicdatasets/trec/kba/kba-stream-corpus-2012/
Accelerate?
rate of assimilation << stream size
# editors << # entities << # mentions
(definition of a “large” KB)
How many days must a news article wait before being cited in Wikipedia?
Complex entity with many relationships and attributes.
Has many interests, including trying to takeover UK soccer teams.
His empire includes many entities…
Note: Usmanov not mentioned in this text!
Citation #18
Elaborate link trails…
Example KBA Rating Task
Published: March 31, 2012Impact of Thoughts on WaterBy Denis Gorce-Bourge
Water covers 70% of our Blue planet and our body is made of about 70% water.
Masaru Emoto is a Japanese Photographer and scientist. He is known over the world for his remarkable work on water and its deep connection with individual and collective consciousness.
For decades, Masaru took pictures of frozen crystals of water and tested the direct influence of the environment on the quality of those crystals.
Pollution has a direct impact on the beauty of a frozen crystal but as well words, music and thoughts. He tested the quality of water crystals by exposing it to various conditions: to written words like hate and violence and Love and gratitude. The results were just astonishing. The crystal exposed to Love and gratitude was beautiful and perfectly formed where the other one was severely degraded. He demonstrated as well the impact of Heavy Metal music versus Mozart or Beethoven and how the vibration of music impacts water.
The very shape of water crystals is modified by violence, aggression, and negative words.
Example KBA Rating Task
Published: March 31, 2012Impact of Thoughts on WaterBy Denis Gorce-Bourge
Water covers 70% of our Blue planet and our body is made of about 70% water.
Masaru Emoto is a Japanese Photographer and scientist. He is known over the world for his remarkable work on water and its deep connection with individual and collective consciousness.
For decades, Masaru took pictures of frozen crystals of water and tested the direct influence of the environment on the quality of those crystals.
Pollution has a direct impact on the beauty of a frozen crystal but as well words, music and thoughts. He tested the quality of water crystals by exposing it to various conditions: to written words like hate and violence and Love and gratitude. The results were just astonishing. The crystal exposed to Love and gratitude was beautiful and perfectly formed where the other one was severely degraded. He demonstrated as well the impact of Heavy Metal music versus Mozart or Beethoven and how the vibration of music impacts water.
The very shape of water crystals is modified by violence, aggression, and negative words.
97.6% +/- 1.4% (N=5365) coref
69.5% +/- 2.7% (N=1352) central
70.9% +/- 2.0% (N=2403) relevant
58.4% +/- 3.4% (N=884) neutral
84.9% +/- 2.0% (N=2599) garbage
82.6% +/- 1.8% (N=3200) central relevant
89.0% +/- 1.7% (N=3551) central relevant neutral
Interannotator Agreement
IR:•User task centric•Variation in interpretation•Scores cascading lists•Constructionist, emergence
NLP:•Data parsing centric•Universal annotation•Scores probabilities•Reductionist
TRECing the continental divide between NLP and IR
string matching
task generator 91% recall15% precision26% F1
KBA 2013More entity types with an emphasis on temporality in the stream.
Target Entities KB Centrally Relevant
Training Data Annotation
People and Organizations
Wikipedia ormaybe Freebase
Citation worthy Judgments from early stream
High recall on all mentioning docs.
Pharmaceutical Compounds
Merck KB? Reporting of Adverse Drug Reaction (ADR)(an event)
(same) Focus recall on first person reporting & negative reactions?
Event-type Entities
Defined by a cluster of entities and possibly a Type-of-Event from a taxonomy
WP/FB for cluster of entities, possibly also event itself.
Provides causality info
Judgments on docs for that Type-of-Event but different specific event.
Find training data from TDT?Use citations in Category:Current_events?
Judge post-hoc?
KBXCold Start queries
focused on:nil entities
related to target clusterand/or
causality of event
Pool top-K filtered docs, or use each KBA run as separate KBP input.
(1000x filter)
Must coordinate choice of KBA target entities with
desired content of KBs for Cold Start queries.
KBA Stream Corpus 2012 (or the new Stream Corpus 2013)•462M texts, 40% English•4,973 hourly chunks of a 105 docs/hour•News, blogs, forums, and link shortening
Output KB
Clusters of related entities
and/orevent-type
entities
KBP
KBA
Sponsors: Thank You.
Diffeo
Lessons Learned(in progress)
• 97% coref, but 70% rating agreement hard– one-in-twenty WP citations non-mentioning– Definition of “citable” varies across entities– Tension between IR “rating” and NLP “labeling”
• Lost >1 teams from challenges of crunching big data– Learn from Kaggle: score a run every day– AWS is really useful.
• KB feature mining & ML beat string matching– Too much training data?– Must exercise temporality in the stream… spikes & events.
NLP deriveslogical structures from messages.Might leverage O(n2) and more to explore the problem space. Limits corpus size to ~106 docs.
IR filters relevant messagesfrom large streams.Limit algorithmic complexity to O(n) and simpler by forcing large corpora, 108 docs and higher.
KBP
KBA
Future of Large
Knowledge Bases
volume
depth
As NLP and IR converge, relevance concepts are gaining structure and logical inference is spreading across documents.
Users query the tagged textand occasionally create more structure
Traditional Doc DB
pipeline of NLP taggers add metadata to doc DB
create structure once
docs
one-shot NLP pipeline(traditional approach)
Doc DBdocs
End users and adaptive tagging algorithms access the same content in the DB
Paradigm Shift:persistent, adaptive NLP
in the database
Methods in the Madnessm
ean
edit
inte
rval
(d
ays)
mean mention interval (hours)
Has many interests, including trying to takeover UK soccer teams.
His empire includes many entities…
Elaborate link trails…
