1. Statistical Learning Methods for Natural Language Processing on the Internet
徐丹云

2. CCF ADL 52
~200

3. Wikification and Beyond: The Challenges of Entity and Concept Grounding
Heng Ji，Rensselaer Polytechnic Institute
Motivation and Task Definition, A Skeletal View of Wikification Systems
Key Challenges and Recent Advances, New Tasks, Trends and Applications

4. Wikification and Beyond: The Challenges of Entity and Concept Grounding
Input: A text document d; Output: a set of pairs (mi, ti)
Identifying mentions mi in d
Local Inference
For each mi in d: (1)Identify a set of relevant titles T(mi); (2)Rank titles ti
Global Inference
For each document d: (1)Consider all mi and all ti; (2) Re-rank titles ti
Heng Ji，Rensselaer Polytechnic Institute

5. Wikification and Beyond: The Challenges of Entity and Concept Grounding
Improving Wikification by
Acquiring Rich Knowledge
Better Meaning Representation
Collaborative Title Collection
Global Inference Using the Additional Knowledge
Joint Mention Extraction and Linking
Collective Inference
Matches of Knowledge Graphs
Heng Ji，Rensselaer Polytechnic Institute

6. Wikification and Beyond: The Challenges of Entity and Concept Grounding
I don’t think Republican candidates like Romney, Newt, and Johnson have a real chance for the election.(Abstract Meaning Representation)
Heng Ji，Rensselaer Polytechnic Institute
candidate
Person:Johnson
Person:Romney
Person:Newt
and
Political-party:Republican

7. Big Learning with Bayesian Methods
Jun Zhu，Tsinghua University
Basics, Big Learning Challenges, and Regularized Bayesian Inference
Online Learning, Large-scale Topic Graph Learning and Visualization

8. Big Learning with Bayesian Methods
Jun Zhu，Tsinghua University
Concept of Baye’s Rule
Approximate Bayesian Inference
Markov chain Monte Carlo methods(MCMC)
Examples
A Bayesian Ranking Model
Latent Dirichlet Allocation

9. Big Learning with Bayesian Methods
Computationally efficient Bayesian models are becoming increasingly relevant in Big data era
RegBayes:
Bridges Bayesian methods, learning and optimization
Offers an extra freedom to incorporate rich side information
Many scalable algorithms have been developed:
Online/stochastic algorithms(e.g., online BayesPA)
Distributed inference algorithms(e.g, scalable CTM)
Jun Zhu，Tsinghua University

10. Sentiment Analysis: Mining Opinions， Sentiments and Emotions
Bing Liu, University Of Illinois at Chicago
Sentiment Analysis Essentials
Advanced Topics

11. Sentiment Analysis: Mining Opinions， Sentiments and Emotions
Definition
Opinion(entity, aspect, sentiment, holder, time)
Analysis
Document
Sentence
Entity
Aspect
Bing Liu, University Of Illinois at Chicago

12. Sentiment Analysis: Mining Opinions， Sentiments and Emotions
Aspect extraction
Finding frequent nouns and noun phrases
Exploiting opinion and target relations
Supervised learning
Topic modeling
Aspect sentiment classification
Lexicon-based approach
Bing Liu, University Of Illinois at Chicago

13. Sentiment Analysis: Mining Opinions， Sentiments and Emotions
Advanced Topics
Explicit and implicit aspects
“The picture quality of this phone is great” VS ”This car is so expensive”
Resource usage aspect and sentiment
“This washer uses a lot of water”
Coreference resolution
“This phone’s sound is great. It is cheap too.”
Bing Liu, University Of Illinois at Chicago

14. Semantic Matching in Search
Jun Xu, China Academy of Sciences
Semantic Matching between Query and Document
Approaches to Semantic Matching in Search

15. Semantic Matching in Search
Jun Xu, China Academy of Sciences
Semantic Matching between Query and Document
query
document
Term match
Semantic match
Seattle best hotel
Seattle best hotels
Partial
Yes
Pool schedule
Swimming pool schedule
Natural logarithm transform
Logarithm transform
China kong
China hong kong No
Why are windows so expensive
Why are macs so expensive

16. Semantic Matching in Search
Jun Xu, China Academy of Sciences
Aspects of Semantic Matching
Term: NY ->NY
Phrase: hot dog -> hot dog
Sense: utube -> youtube
Topic: Microsoft Office -> Microsoft, PowerPoint, Word, Excel…
Structure: how far is sun from earth -> distance between sun and earth

17. Semantic Matching in Search
Jun Xu, China Academy of Sciences
Query: michael jordan berkele
Term: michael jordan berkeley
Phrase: Michael Jordan
berkeley
Sense: michael i. jordan
Topic: machine learning, berkeley
Structure: michael jordan

18. Semantic Matching in Search
Jun Xu, China Academy of Sciences
Document: Homepage of Michael Jordan
Phrase: Michael Jordan, Berkeley, professor
Topic: machine learning, berkeley
Structure: michael jordan

19. Semantic Matching in Search
Jun Xu, China Academy of Sciences
Approaches to Semantic Matching in Search
Matching by Query Reformulation
Matching with Term Dependency Model
Matching with Translation Model
Matching with Topic Model
Matching Latent Space Model

20. From Simple Search to Search Intelligence: The Evolution of Search Engines
Jianyun Nie, University of Montreal
Traditional IR Models, Query and Document Expansion
Advanced Methods of Intelligent IR, Mining Relations in Documents and Query logs, Mining Search Intents

21. From Simple Search to Search Intelligence: The Evolution of Search Engines
Traditional IR Models, Query and Document Expansion
Indexing
Stopwords
stemming
Retrieval
Boolean model
Vector space model
Probabilistic model
Jianyun Nie, University of Montreal
Doucument
Query
indexing
indexing
Representation
(keywords)
Representation
(keywords)
Retrieval

22. From Simple Search to Search Intelligence: The Evolution of Search Engines
Jianyun Nie, University of Montreal
Advanced Methods of Intelligent IR, Mining Relations in Documents and Query logs, Mining Search Intents
Using Language Model in IR
Query Expansion
Inference

23. Machine Learning for Search Ranking and Ad Auction
Tieyan Liu, Microsoft Research
Machine learning for Web Search
Machine learning for computational advertising

24. Machine Learning for Search Ranking and Ad Auction
Machine learning for Web Search
Learn to rank methods
Regression
Classification
Pairwise regression
Listwise Ranking
Generalization theory
Tieyan Liu, Microsoft Research

25. Machine Learning for Search Ranking and Ad Auction
Machine learning for computational advertising
User Click Behavior Modeling
Advertiser Bidding Behavior Modeling
Auction Mechanism Optimization
Tieyan Liu, Microsoft Research

26. Thank You!