1. Relational Inference for Wikification
Xiao Cheng and Dan Roth
Task Definition
Annotate input text with disambiguated Wikipedia titles:
Motivation
Current system already achieves the above performance, although the performance levels off at around 75%~80% F1
Limitation of Bag-of-words representation
Relational Inference
Identify Textual Relations
Retrieve Relational Knowledge
Formulate the inference problem
Rerank via constraints
Promote candidate pair:
e 3 1 = Slobodan_Milošević
e 4 21 = Socialist_Party_of_Serbia
Textual Relations
Demo:
Evaluations
Achieves significant improvement over the previous state-of-the-art systems
Run the Relational Inference Wikifier (RI) “as-is” without retraining on the target domain, still obtains significant performance gain over our previous submitted Entity Linking system (Cogcomp).
Discussion
We are interested in extracting high-precision textual relations that help with disambiguation. Specifically, we focus on the following types of relations:
Syntactico-semantic relations (Chan & Roth ‘10)
Coreference relations
Acronyms, partial names, nominal mentions
...ousted long time Yugoslav President Slobodan Milošević in October. Mr. Milošević's Socialist Party…
Blumenthal (D) is a candidate for the U.S. Senate seat now held by Christopher Dodd (D), and he has held a commanding lead in the race since he entered it. But the Times report has the potential to fundamentally reshape the contest in the Nutmeg State.
Chris Dodd
From Wikipedia, the free encyclopedia
The New York Times
Connecticut
Democratic Party (United States)
United States Senate
Richard Blumenthal
Type
Example
Premodifier
Iranian Ministry of Defense
Possessive
NYC’s stock exchange
Formulaic
Chicago, Illinois
Preposition
President of the US
Argument 1
Relation Type
Argument 2
Yugoslav President
apposition
Slobodan Milošević
coreference
Milošević
possessive
Socialist Party
Slobodan Milošević
From Wikipedia, the free encyclopedia
Socialist Party of Serbia
From Wikipedia, the free encyclopedia
Relation Retrieval
founded
Uses DBPedia and Wikipedia page link relations as our knowledge base
Retrieve lexically similar candidates and filter
q1=(Socialist Party of France,?, *Milošević*)
q2=(Slobodan Milošević,?,*Socialist Party*)
Mubarak, the wife of deposed Egyptian President Hosni Mubarak, …
Bag-of-words loses important relational information
Modeling constraining interaction between concepts
Need to link Mubarak to Suzanne Mubarak
Identify relation (Mubarak, wife, Hosni Mubarak)
Promote the pair of candidates coherent with text
Mubarak
wife
Egyptian President
Hosni Mubarak
Relation Inference
For each pair of entity candidates 𝑒 𝑖 𝑘 and 𝑒 𝑗 𝑙 , 𝑟 𝑖𝑗 (𝑘,𝑙) represents whether we found a relation in the text between their mentions AND a relation in our knowledge base
𝑤 𝑖𝑗 (𝑘,𝑙) either rewards or penalizes a relation 𝑟 𝑖𝑗 (𝑘,𝑙) for its coherency with the text.
Relation scoring
Relaxes constraint when ambiguity exists
Scores each retrieved relation 𝜎 for each query
𝛼 𝜎 is the relation weight for different knowledge source
𝑆𝑖 𝑚 𝜎,𝑞 is the lexical similarity between the query 𝑞 and the retrieved relation 𝜎
𝑍 is the normalization factor so that the weights for each pair of mentions is always between 0 and 1
Special handling of “local knowledge”
Creates NIL entity candidate for inference propagation, so that locally extracted high precision knowledge can be considered across long-range textual relations
Uses off-the-shelf Integer Linear Programming (ILP) packages to optimize the objective function
High-level algorithm description
ek3=Milošević
sk3
Slobodan_Milošević .7
Milošević_(surname) .1
Boki_Milošević
Alexander_Milošević
.05 …
ek4=Socialist Party
sk4
Socialist_Party_(France)
.23
Socialist_Party_(Portugal)
.16
Socialist_Party_of_America
.07
Socialist_Party_(Argentina)
.06 …
Socialist_Party_of_Serbia .0
We showed that both linguistic and world knowledge, specifically the ability to use relational information, are crucial in the task of Wikification. We also introduced an extensible and efficient inference framework that leverages better language understanding. It will be very interesting to accumulate our knowledge about NIL entities so that our system will be able to fully address the Entity Linking task and potentially move to encylopedia resources other than Wikipedia. The performance gains and error analysis also calls for joint entity typing, coreference and disambiguation.
𝑟 34 (1,21) =1
This research is sponsored by DARPA under agreement number FA , and partly supported by the IARPA under contract number D11PC20155, by the ARL under agreement W911NF , and by the Multimodal Information Access & Synthesis Center at UIUC.