1. CS224N Section 3: Corpora, etc.
Helen Kwong
April 24, 2009
(Thanks to Bill MacCartney and Pi-Chuan Chang for these materials)

2. Final Project Proposal due in 2 weeks - Wed. 5/6 Project ideas
Final project guide
Go through the syllabus
Projects from previous years:

3. Corpora Corpora@Stanford LDC (Linguistic Data Consortium)
Some are on AFS (/afs/ir/data/linguistic-data/); some are available on DVD/CDs in the linguistic department
LDC (Linguistic Data Consortium)
Links to many resources
Previous years’ notes:

4. Treebanks Most widely used: Penn Treebank
There's PTB2 and PTB3. Use PTB3, i.e. Treebank-3
Contains:
50,000 sentences (1,000,000 words) of WSJ text from 1989
30,000 sentences (400,000 words) of Brown corpus
Parsed WSJ trees:
/afs/ir/data/linguistic-data/Treebank/3/parsed/mrg/wsj/
See Bill’s notes for more details
BLLIP: like PTB, WSJ text, but 30m words, parsed automatically by Charniak
Switchboard: telephone conversations

5. Parsed corpora in other languages
Penn Arabic Treebank Corpus
734 stories (140,000 words)
Penn Chinese Treebank Corpus
50,000 sentences
German (newspaper text):
NEGRA
TIGER
Tueba-D/Z

6. Part-of-speech tagged corpora
POS tags from treebanks
British National Corpus (BNC)
100m words
wide sample of British English: newspapers, books, letters

7. Named Entity Recognition (NER)
Message Understanding Conference (MUC)
We have MUC-6 and MUC-7
Example: /afs/ir/data/linguistic-data/MUC_7/muc_7/data/training.ne.eng.keys
CoNLL shared tasks: Language-Independent Named Entity Recognition (I), (II)
2002:
2003:

8. Anaphora resolution Data: MUC-6 and MUC-7
Example: Pam went home because she felt sick
Demo:
Unsolved problem
Harder example:
We gave the bananas to the monkeys because they were hungry
We gave the bananas to the monkeys because they were ripe.

9. Semantics WordNet Website: http://wordnet.princeton.edu/
Browse online:
150,000 nouns, verbs, adjectives, adverbs
Groups words into “synsets” with short, general definitions, and records various relations between synsets, e.g. hypernym (kind-of) hierarchy.
Good tutorial:
Neat visual interface:
Problems with WordNet:
fine-grained senses
sense ordering sometimes funny (see "airline")

10. Semantic Role Labeling
Detection of semantic arguments associated with each verb in a sentence
Example: “I [agent] sold you [patient] a book [theme]”
CoNLL shared task 2004, 2005
PropBank
Adds predicate-argument relations to PTB syntax trees
FrameNet:
Demo from UIUC:

11. More corpora for specific tasks
Word Sense Disambiguation (WSD)
Senseval:
Question Answering
e.g. "What film introduced Jar Jar Binks?"
TREC competition, Question Answering track
Textual Entailment
Recognizing Textual Entailment (RTE) challenges
Events, temporal relations
TimeBank corpus:

12. More corpora for specific tasks
Topic Detection and Tracking
Given documents, separate into different topics

13. Speech & Dialogue Speech Dialogue BNC: 10m words Switchboard corpus
Conversations of two speakers recorded over the phone
Transcriptions of their speech, with speakers labeled
Example:

14. Email/Spam Enron corpus TREC Spam track
/afs/ir/data/linguistic-data/Enron- -Corpus/maildir/skilling-j/
Annotated subsets:
TREC Spam track

15. Tools Many links to tools on the StatNLP page Parsers POS taggers
Parsers
Stanford Parser (English, Chinese, German and Arabic)
Online parser:
Collin’s parser, Charniak’s parser, MiniPar, etc.
POS taggers
Named entity recognizers
Language modeling toolkits

16. Machine learning tools
Stanford classifier
conditional loglinear (aka maximum entropy) model
Weka
Java library containing (nearly) every machine learning algorithm -Naive Bayes, perceptron, decision tree, MaxEnt, SVM, etc.
Mallet
Java; useful for statistical NLP, document classification, clustering, topic modeling, information extraction…