CARTIC RAMAKRISHNAN MEENAKSHI NAGARAJAN AMIT SHETH. TEXT ANALYSIS FOR SEMANTIC COMPUTING. Preparing for a Tutorial. A Great way to find out…. How little you really know. Acknowledgements.
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TEXT ANALYSIS FOR SEMANTIC COMPUTING
A Great way to find out….
How little you really know
We have used material from several popular books, papers, course notes and presentations made by experts in this area. We have provided all references to the best of our knowledge. This list however, serves only as a pointer to work in this area and is by no means a comprehensive resource.
An Overview of Empirical Natural Language Processing, Eric Brill, Raymond J. Mooney
Finding more about what we already know
Ex. patterns that characterize known information
The search/browse OR ‘finding a needle in a haystack’ paradigm
Discovering what we did not know
Deriving new information from data
Ex. Relationships between known entities previously unknown
The ‘extracting ore from rock’ paradigm
Some Differences to note too..
WHAT CAN TM DO FOR HARRY PORTER?
A bag of words
UNDISCOVERED PUBLIC KNOWLEDGE
Discovering connections hidden in text
Associations between Migraine and Magnesium [Hearst99]
stress is associated withmigraines
stress can lead to loss of magnesium
calcium channel blockersprevent some migraines
magnesiumis a natural calcium channel blocker
spreading cortical depression (SCD) is implicated in some migraines
high levels of magnesiuminhibit SCD
migraine patientshave highplatelet aggregability
magnesium can suppressplatelet aggregability
Mining popularity from Social Media
Goal: Top X artists from MySpace artist comment pages
Traditional Top X lists got from radio plays, cd sales. An attempt at creating a list closer to listeners preferences
Mining positive, negative affect / sentiment
Slang, casual text necessitates transliteration
‘you are so bad’ == ‘you are good’
Mining text to improve existing information access mechanisms
IR [QA systems]
Mining text for
Discovery & insight [Relationship Extraction]
Creation of new knowledge
Ontology instance-base population
Ontology schema learning
A system that uses the result of dependency parses of sentences to train a Naïve Bayes classifier for Web-scale extraction of relationships
Does not require parsing for extraction – only required for training
Training on features – POS tag sequences, if object is proper noun, number of tokens to right or left etc.
This system is able to respond to queries like "What did Thomas Edison invent?"
Semi-automatically builds faceted hierarchical metadata structures from text
This is combined with Flamenco to support faceted browsing of content
Divide into facets
ice cream sundae
ice cream sundae
Domains used to prune applicable senses in Wordnet (e.g. “dip”)
Disease or Syndrome
Finding attribute “like”
Finding class instances
[Ramakrishnan et. al. 08]
Automatic acquisition of
SYNTAX, SEMANTICS, STATISTICAL NLP, TOOLS, RESOURCES, GETTING STARTED
POS Tags, Taggers, Ambiguities, Examples
WordPOS AdditionalPOS features
Understanding Phrase Structures, Parsing, Chunking
Constituency Parse - Nested Phrasal Structures
Dependency parse - Role Specific Structures
Natural Language Parsers, Peter Hellwig, Heidelberg
From Hearst 97
COLORLESS GREEN IDEAS SLEEP FURIOUSLY
Partial material from http://www.stanford.edu/class/cs224u/224u.07.lec2.ppt
So you want to build your own text miner!
“Your musics the shit,…lovve your video you are so bad”
‘The smile is so wicked!!’
TO COME: USAGE EXAMPLES OF WHAT WE COVERED THUS FAR
SAMPLE APPLICATIONS, SURVEY OF EFFORTS IN TWO SAMPLE AREAS
cyclin D1 protein expression
BRAF mutant cells
induction of G1 arrest
Information Extraction = segmentation+classification+association+mining
Text mining = entity identification+named relationship extraction+discovering association chains….
Named Relationship Extraction
This MEK dependency was observed in BRAF mutant cells regardless of tissue lineage, and correlated with both downregulation of cyclin D1 protein expression and the induction of G1 arrest.
*MEK dependency ISA Dependency_on_an_Organic_chemical
*BRAF mutant cells ISA Cell_type
*downregulation of cyclin D1 protein expression ISA Biological_process
*tissue lineage ISA Biological_concept
*induction of G1 arrest ISA Biological_process
cyclin D1 protein expression
BRAF mutant cells
induction of G1 arrest
"China International Trust and Investment Corp”
"Suspended Ceiling Contractors Ltd”
"Hughes“ when "Hughes Communications Ltd.“ is already marked as an organization
Several features about same word can affect parameters
U.S. NNP I-NP I-ORG
generalNN I-NP O
David NNP I-NP B-PER
Petraeus NNP I-NP I-PER
heads VBZ I-VP O
for IN I-PP O
Baghdad NNP I-NP I-LOC
. . O O
Carroll, J., G. Minnen and E. Briscoe (1999) `Corpus annotation for parser evaluation'. In Proceedings of the EACL-99 Post-Conference Workshop on Linguistically Interpreted Corpora, Bergen, Norway. 35-41. Also in Proceedings of the ATALA Workshop on Corpus Annotés pour la Syntaxe - Treebanks, Paris, France. 13-20.
Discovering informative subgraphs (Harry Potter)
Given a pair of end-points (entities)
Produce a subgraph with relationships connecting them such that
The subgraph is small enough to be visualized
And contains relevant “interesting” connections
We defined an interestingness measure based on the ontology schema
In future biomedical domain the scientist will control this with the help of a browsable ontology
Our interestingness measure takes into account
Specificity of the relationships and entity classes involved
Rarity of relationships etc.
Cartic Ramakrishnan, William H. Milnor, Matthew Perry, Amit P. Sheth: Discovering informative connection subgraphs in multi-relational graphs. SIGKDD Explorations 7(2): 56-63 (2005)
Two factor influencing interestingness
Model the Candidate graph as an electrical circuit
S is the source and T the sink
Edge weight derived from the ontology schema are treated as conductance values
Using Ohm’s and Kirchoff’s laws we find maximum current flow paths through the candidate graph from S to T
At each step adding this path to the output graph to be displayed we repeat this process till a certain number of predefined nodes is reached
Arnold schwarzenegger, Edward Kennedy
Other related work
Automating Creation of Hierarchical Faceted Metadata Structures Emilia Stoica, Marti Hearst, and Megan Richardson, in the proceedings of NAACL-HLT, Rochester NY, April 2007
Finding the Flow in Web Site Search, Marti Hearst, Jennifer English, Rashmi Sinha, Kirsten Swearingen, and Ping Yee, Communications of the ACM, 45 (9), September 2002, pp.42-49.
R. Kumar, U. Mahadevan, and D. Sivakumar, "A graph-theoretic approach to extract storylines from search results", in Proc. KDD, 2004, pp.216-225.
Michele Banko, Michael J. Cafarella, Stephen Soderland, Matthew Broadhead, Oren Etzioni: Open Information Extraction from the Web. IJCAI 2007: 2670-2676
Hearst, M. A. 1992. Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th Conference on Computational Linguistics - Volume 2 (Nantes, France, August 23 - 28, 1992).
Dat P. T. Nguyen, Yutaka Matsuo, Mitsuru Ishizuka: Relation Extraction from Wikipedia Using Subtree Mining. AAAI 2007: 1414-1420
"Unsupervised Discovery of Compound Entities for Relationship Extraction" Cartic Ramakrishnan, Pablo N. Mendes, Shaojun Wang and Amit P. Sheth EKAW 2008 - 16th International Conference on Knowledge Engineering and Knowledge Management Knowledge Patterns
Mikheev, A., Moens, M., and Grover, C. 1999. Named Entity recognition without gazetteers. In Proceedings of the Ninth Conference on European Chapter of the Association For Computational Linguistics (Bergen, Norway, June 08 - 12, 1999).
McCallum, A., Freitag, D., and Pereira, F. C. 2000. Maximum Entropy Markov Models for Information Extraction and Segmentation. In Proceedings of the Seventeenth international Conference on Machine Learning
Lafferty, J. D., McCallum, A., and Pereira, F. C. 2001. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In Proceedings of the Eighteenth international Conference on Machine Learning