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Last Time
• WordNet (Miller @ Princeton University)– word-based semantic network – Logical Metonymy
• gap-filling that involves computing some telic role
• John enjoyed X– X some non-eventive NP, e.g. the wine
• John enjoyed [Ving] X– John enjoyed [drinking] the wine
• Generative Lexicon (GL) Model (wine: telic role: drink)
• WordNet structure
Last Time
• WordNet (Miller @ Princeton University)– some applications including:
• concept identification/disambiguation• query expansion• (cross-linguistic) information retrieval• document classification• machine translation• Homework 2: GRE vocabulary
– word matching exercise (from a GRE prep book)
• GRE as Turing Test– if a computer program can be written to do as well as humans on
the GRE test, is the program intelligent?
– e-rater from ETS (98% accurate essay scorer)
Today’s Lecture
• Guest mini-lecture by– Massimo Piattelli-Palmarini on – A powerful critique of semantic networks:
Jerry Fodor's atomism
Today’s Lecture
but first...
• Take a look at an alternative to WordNet– WordNet: handbuilt system– COALS:
• the correlated occurrence analogue to lexical semantics
• (Rohde et al. 2004)
• a instance of a vector-based statistical model for similarity – e.g., see also Latent Semantic Analysis (LSA)
– Singular Valued Decomposition (SVD)
» sort by singular values, take top k and reduce the dimensionality of the co-occurrence matrix to rank k
• based on weighted co-occurrence data from large corpora
COALS
• Basic Idea:– compute co-occurrence counts for (open class) words from
a large corpora– corpora:
• Usenet postings over 1 month
• 9 million (distinct) articles
• 1.2 billion word tokens
• 2.1 million word types– 100,000th word occurred 98 times
– co-occurrence counts• based on a ramped weighting system with window size 4
– excluding closed-class items
4 4
wi
332 2
11wi-1wi-2wi-3wi-4 wi+4wi+3wi+2wi+1
Task: Match each word in the first column with its definition in the second column
accolade
abate
aberrant
abscond
acumen
abscission
acerbic
accretion
abjure
abrogate
deviation
abolish
keen insight
lessen in intensity
sour or bitter
building up
depart secretly
renounce
removal
praise
Task: Match each word in the first column with its definition in the second column
accolade
abate
aberrant
abscond
acumen
abscission
acerbic
accretion
abjure
abrogate
deviation
abolish
keen insight
lessen in intensity
sour or bitter
building up
depart secretly
renounce
removal
praise3
2
3
2
2
2
2
COALS and the GRE
ACCOLADE
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
DEVIATIONINSIGHT ABOLISH LESSEN
SOURDEPART BUILD
RENOUNCEREMOVAL
PRAISE
Correlation
COALS and the GRE
ABERRANT
-0.04
-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
DEVIATIONINSIGHT ABOLISH LESSEN
SOURDEPART BUILD
RENOUNCEREMOVAL
PRAISE
Correlation
COALS and the GRE
ABATE
-0.1
-0.05
0
0.05
0.1
0.15
0.2
DEVIATIONINSIGHT ABOLISH LESSEN
SOURDEPART BUILD
RENOUNCEREMOVAL
PRAISE
Correlation
COALS and the GRE
ABSCOND
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08
0.1
DEVIATIONINSIGHT ABOLISH LESSEN
SOURDEPART BUILD
RENOUNCEREMOVAL
PRAISE
Correlation
COALS and the GRE
ACUMEN
-0.05
0
0.05
0.1
0.15
0.2
0.25
DEVIATIONINSIGHT ABOLISH LESSEN
SOURDEPART BUILD
RENOUNCEREMOVAL
PRAISE
Correlation
COALS and the GRE
ACERBIC
-0.1
-0.05
0
0.05
0.1
0.15
DEVIATIONINSIGHT ABOLISH LESSEN
SOURDEPART BUILD
RENOUNCEREMOVAL
PRAISE
Correlation
COALS and the GRE
ACCRETION
-0.05
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0.05
DEVIATIONINSIGHT ABOLISH LESSEN
SOURDEPART BUILD
RENOUNCEREMOVAL
PRAISECorrelation
COALS and the GRE
ABJURE
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
DEVIATIONINSIGHT ABOLISH LESSEN
SOURDEPART BUILD
RENOUNCEREMOVAL
PRAISE
Correlation
COALS and the GRE
ABROGATE
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
DEVIATIONINSIGHT ABOLISH LESSEN
SOURDEPART BUILD
RENOUNCEREMOVAL
PRAISE
Correlation
Task: Match each word in the first column with its definition in the second column
accolade
abate
aberrant
abscond
acumen
abscission
acerbic
accretion
abjure
abrogate
deviation
abolish
keen insight
lessen in intensity
sour or bitter
building up
depart secretly
renounce
removal
praise