Lecture 24: NER & Entity Linking

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Presentation transcript:

Lecture 24: NER & Entity Linking Kai-Wei Chang CS @ University of Virginia kw@kwchang.net Couse webpage: http://kwchang.net/teaching/NLP16 CS6501-NLP

Organizing knowledge It’s a version of Chicago – the standard classic Macintosh menu font, with that distinctive thick diagonal in the ”N”. Chicago was used by default for Mac menus through MacOS 7.6, and OS 8 was released mid-1997.. Chicago VIII was one of the early 70s-era Chicago albums to catch my ear, along with Chicago II. Slides are adapted from Dan Roth

Cross-document co-reference resolution It’s a version of Chicago – the standard classic Macintosh menu font, with that distinctive thick diagonal in the ”N”. Chicago was used by default for Mac menus through MacOS 7.6, and OS 8 was released mid-1997.. Chicago VIII was one of the early 70s-era Chicago albums to catch my ear, along with Chicago II.

Reference resolution: (disambiguation to Wikipedia) It’s a version of Chicago – the standard classic Macintosh menu font, with that distinctive thick diagonal in the ”N”. Chicago was used by default for Mac menus through MacOS 7.6, and OS 8 was released mid-1997.. Chicago VIII was one of the early 70s-era Chicago albums to catch my ear, along with Chicago II.

The “Reference” Collection has Structure It’s a version of Chicago – the standard classic Macintosh menu font, with that distinctive thick diagonal in the ”N”. Chicago was used by default for Mac menus through MacOS 7.6, and OS 8 was released mid-1997.. Chicago VIII was one of the early 70s-era Chicago albums to catch my ear, along with Chicago II. Is_a Is_a Used_In Released Succeeded

Analysis of Information Networks It’s a version of Chicago – the standard classic Macintosh menu font, with that distinctive thick diagonal in the ”N”. Chicago was used by default for Mac menus through MacOS 7.6, and OS 8 was released mid-1997.. Chicago VIII was one of the early 70s-era Chicago albums to catch my ear, along with Chicago II.

Wikipedia as a knowledge resource …. Is_a Is_a Used_In Released Succeeded

Wikification: The Reference Problem Cycles of Knowledge: Grounding for/using Knowledge Wikification: The Reference Problem 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. 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. Let’s start with what is wikification Read wiki titles slower

Challenging Dealing with Ambiguity of Natural Language Mentions of entities and concepts could have multiple meanings Dealing with Variability of Natural Language A given concept could be expressed in many ways Wikification addresses these two issues in a specific way: The Reference Problem What is meant by this concept? (WSD + Grounding) More than just co-reference (within and across documents) . We expect this idea to inspire a completely new philosophy for fact extraction which will be essential for the creation of a high-quality StructNet. Incorporate arbitrary global features and soft constraints; Features are automatically learned selectively Save some human knowledge engineering efforts Capture inter-dependency knowledge on the entire structure Efficient linear-time beam search by joint learning and decoding No need to model joint/conditional distribution with a large number of variables Extract all facts jointly in a unified framework Local predictions can be mutually improved Avoid error compounding problem

Wikification: Subtasks Wikification and Entity Linking requires addressing several sub-tasks: Identifying Target Mentions Mentions in the input text that should be Wikified Identifying Candidate Titles Candidate Wikipedia titles that could correspond to each mention Candidate Title Ranking Rank the candidate titles for a given mention NIL Detection and Clustering Identify mentions that do not correspond to a Wikipedia title Entity Linking: cluster NIL mentions that represent the same entity. . We expect this idea to inspire a completely new philosophy for fact extraction which will be essential for the creation of a high-quality StructNet. Incorporate arbitrary global features and soft constraints; Features are automatically learned selectively Save some human knowledge engineering efforts Capture inter-dependency knowledge on the entire structure Efficient linear-time beam search by joint learning and decoding No need to model joint/conditional distribution with a large number of variables Extract all facts jointly in a unified framework Local predictions can be mutually improved Avoid error compounding problem

High-level Algorithmic Approach. Input: A text document d; Output: a set of pairs (mi ,ti) mi are mentions in d; tj(mi ) are corresponding Wikipedia titles, or NIL. (1) Identify mentions mi in d (2) Local Inference For each mi in d: Identify a set of relevant titles T(mi ) Rank titles ti ∈ T(mi ) [E.g., consider local statistics of edges [(mi ,ti) , (mi ,*), and (*, ti )] occurrences in the Wikipedia graph] (3) Global Inference For each document d: Consider all mi ∈ d; and all ti ∈ T(mi ) Re-rank titles ti ∈ T(mi ) [E.g., if m, m’ are related by virtue of being in d, their corresponding titles t, t’ may also be related] Don’t focus on “our contribution” only – focus on describing an algorithmic approach (which is impressive and good)

Local approach A text Document Identified mentions Wikipedia Articles Local score of matching the mention to the title (decomposed by mi) Γ is a solution to the problem A set of pairs (m,t) m: a mention in the document t: the matched Wikipedia Title Changed—change in related slides.

Global Approach: Using Additional Structure Text Document(s)—News, Blogs,… Wikipedia Articles Adding a “global” term to evaluate how good the structure of the solution is. Use the local solutions Γ’ (each mention considered independently. Evaluate the structure based on pair-wise coherence scores Ψ(ti,tj) Choose those that satisfy document coherence conditions. Add a cartoon of edge cover to justify that the overall problem is hard.