statistical nlp winter 2009 lecture 5: unsupervised text categorization roger levy many thanks to...
TRANSCRIPT
Statistical NLPWinter 2009
Lecture 5: Unsupervised text categorization
Roger Levy
Many thanks to Tom Griffiths for use of his slides
Where we left off
• Supervised text categorization through Naïve Bayes• Generative model: first generate a document category,
then words in the document (unigram model)
• Inference: obtain posterior over document categories using Bayes rule (argmax to choose the category)
Cat
w1 w2 w3 w4 w5 w6 …
€
P(Cat | w1...n ) =P(w1...n | Cat)P(Cat)
P(w1...n )
What we’re doing today
• Supervised categorization requires hand-labeling documents
• This can be extremely time-consuming• Unlabeled documents are cheap• So we’d really like to do unsupervised text
categorization• Today we’ll look at unsupervised learning within the
Naïve Bayes model
Compact graphical model representations
• We’re going to lean heavily on graphical model representations here.
• We’ll use a more compact notation:
Cat
w1 w2 w3 w4 wn…
Cat
w1
n
“generate a word from Cat n times”
a “plate”
• Now suppose that Cat isn’t observed• We need to learn two distributions:
• P(Cat)• P(w|Cat)
• How do we do this?• We might use the method of maximum likelihood (MLE)• But it turns out that the likelihood surface is highly non-
convex and lots of information isn’t contained in a point estimate
• Alternative: Bayesian methods
Cat
w1
n
Bayesian document categorization
Cat
w1nD
D
priors
P(w|Cat)
P(Cat)
Latent Dirichlet allocation(Blei, Ng, & Jordan, 2001; 2003)
Nd D
zi
wi
(d)
(j)
(d) Dirichlet()
zi Discrete( (d) ) (j) Dirichlet()
wi Discrete( (zi) )
T
distribution over topicsfor each document
topic assignment for each word
distribution over words for each topic
word generated from assigned topic
Dirichlet priors
Main difference: one topic per word
A generative model for documents
HEART 0.2 LOVE 0.2SOUL 0.2TEARS 0.2JOY 0.2SCIENTIFIC 0.0KNOWLEDGE 0.0WORK 0.0RESEARCH 0.0MATHEMATICS 0.0
HEART 0.0 LOVE 0.0SOUL 0.0TEARS 0.0JOY 0.0 SCIENTIFIC 0.2KNOWLEDGE 0.2WORK 0.2RESEARCH 0.2MATHEMATICS 0.2
topic 1 topic 2
w P(w|Cat = 1) w P(w|Cat = 2)
Choose mixture weights for each document, generate “bag of words”
{P(z = 1), P(z = 2)}
{0, 1}
{0.25, 0.75}
{0.5, 0.5}
{0.75, 0.25}
{1, 0}
MATHEMATICS KNOWLEDGE RESEARCH WORK MATHEMATICS RESEARCH WORK SCIENTIFIC MATHEMATICS WORK
SCIENTIFIC KNOWLEDGE MATHEMATICS SCIENTIFIC HEART LOVE TEARS KNOWLEDGE HEART
MATHEMATICS HEART RESEARCH LOVE MATHEMATICS WORK TEARS SOUL KNOWLEDGE HEART
WORK JOY SOUL TEARS MATHEMATICS TEARS LOVE LOVE LOVE SOUL
TEARS LOVE JOY SOUL LOVE TEARS SOUL SOUL TEARS JOY
Dirichlet priors
• Multivariate equivalent of Beta distribution
• Hyperparameters determine form of the prior
€
p(θ | Cat) =Γ(Tα )
Γ(α )Tθ j
α −1
j=1
T
∏
wor
ds
documents
wor
ds
topics
topi
cs
documents
P(w
|z)
P(z)
Matrix factorization interpretation
Maximum-likelihood estimation is finding the factorization that minimizes KL divergence
P(w)
(Hofmann, 1999)
STORYSTORIES
TELLCHARACTER
CHARACTERSAUTHOR
READTOLD
SETTINGTALESPLOT
TELLINGSHORT
FICTIONACTION
TRUEEVENTSTELLSTALE
NOVEL
MINDWORLDDREAM
DREAMSTHOUGHT
IMAGINATIONMOMENT
THOUGHTSOWNREALLIFE
IMAGINESENSE
CONSCIOUSNESSSTRANGEFEELINGWHOLEBEINGMIGHTHOPE
WATERFISHSEA
SWIMSWIMMING
POOLLIKE
SHELLSHARKTANK
SHELLSSHARKSDIVING
DOLPHINSSWAMLONGSEALDIVE
DOLPHINUNDERWATER
DISEASEBACTERIADISEASES
GERMSFEVERCAUSE
CAUSEDSPREADVIRUSES
INFECTIONVIRUS
MICROORGANISMSPERSON
INFECTIOUSCOMMONCAUSING
SMALLPOXBODY
INFECTIONSCERTAIN
FIELDMAGNETIC
MAGNETWIRE
NEEDLECURRENT
COILPOLESIRON
COMPASSLINESCORE
ELECTRICDIRECTION
FORCEMAGNETS
BEMAGNETISM
POLEINDUCED
SCIENCESTUDY
SCIENTISTSSCIENTIFIC
KNOWLEDGEWORK
RESEARCHCHEMISTRY
TECHNOLOGYMANY
MATHEMATICSBIOLOGY
FIELDPHYSICS
LABORATORYSTUDIESWORLD
SCIENTISTSTUDYINGSCIENCES
BALLGAMETEAM
FOOTBALLBASEBALLPLAYERS
PLAYFIELD
PLAYERBASKETBALL
COACHPLAYEDPLAYING
HITTENNISTEAMSGAMESSPORTS
BATTERRY
JOBWORKJOBS
CAREEREXPERIENCE
EMPLOYMENTOPPORTUNITIES
WORKINGTRAINING
SKILLSCAREERS
POSITIONSFIND
POSITIONFIELD
OCCUPATIONSREQUIRE
OPPORTUNITYEARNABLE
Interpretable topics
each column shows words from a single topic, ordered by P(w|z)
STORYSTORIES
TELLCHARACTER
CHARACTERSAUTHOR
READTOLD
SETTINGTALESPLOT
TELLINGSHORT
FICTIONACTION
TRUEEVENTSTELLSTALE
NOVEL
MINDWORLDDREAM
DREAMSTHOUGHT
IMAGINATIONMOMENT
THOUGHTSOWNREALLIFE
IMAGINESENSE
CONSCIOUSNESSSTRANGEFEELINGWHOLEBEINGMIGHTHOPE
WATERFISHSEA
SWIMSWIMMING
POOLLIKE
SHELLSHARKTANK
SHELLSSHARKSDIVING
DOLPHINSSWAMLONGSEALDIVE
DOLPHINUNDERWATER
DISEASEBACTERIADISEASES
GERMSFEVERCAUSE
CAUSEDSPREADVIRUSES
INFECTIONVIRUS
MICROORGANISMSPERSON
INFECTIOUSCOMMONCAUSING
SMALLPOXBODY
INFECTIONSCERTAIN
FIELDMAGNETIC
MAGNETWIRE
NEEDLECURRENT
COILPOLESIRON
COMPASSLINESCORE
ELECTRICDIRECTION
FORCEMAGNETS
BEMAGNETISM
POLEINDUCED
SCIENCESTUDY
SCIENTISTSSCIENTIFIC
KNOWLEDGEWORK
RESEARCHCHEMISTRY
TECHNOLOGYMANY
MATHEMATICSBIOLOGY
FIELDPHYSICS
LABORATORYSTUDIESWORLD
SCIENTISTSTUDYINGSCIENCES
BALLGAMETEAM
FOOTBALLBASEBALLPLAYERS
PLAYFIELD
PLAYERBASKETBALL
COACHPLAYEDPLAYING
HITTENNISTEAMSGAMESSPORTS
BATTERRY
JOBWORKJOBS
CAREEREXPERIENCE
EMPLOYMENTOPPORTUNITIES
WORKINGTRAINING
SKILLSCAREERS
POSITIONSFIND
POSITIONFIELD
OCCUPATIONSREQUIRE
OPPORTUNITYEARNABLE
Handling multiple senses
each column shows words from a single topic, ordered by P(w|z)
Natural statistics
Documents Semantic representation
MATHEMATICS KNOWLEDGE RESEARCH WORK MATHEMATICS RESEARCH WORK SCIENTIFIC MATHEMATICS WORK
SCIENTIFIC KNOWLEDGE MATHEMATICS SCIENTIFIC HEART LOVE TEARS KNOWLEDGE HEART
MATHEMATICS HEART RESEARCH LOVE MATHEMATICS WORK TEARS SOUL KNOWLEDGE HEART
WORK JOY SOUL TEARS MATHEMATICS TEARS LOVE LOVE LOVE SOUL
TEARS LOVE JOY SOUL LOVE TEARS SOUL SOUL TEARS JOY
HEARTLOVESOUL
TEARSJOY…
SCIENTIFICKNOWLEDGE
WORKRESEARCH
MATHEMATICS…?
Inverting the generative model
• Maximum likelihood estimation (EM)• e.g. Hofmann (1999)
• Deterministic approximate algorithms • variational EM; Blei, Ng & Jordan (2001; 2003)• expectation propagation; Minka & Lafferty (2002)
• Markov chain Monte Carlo• full Gibbs sampler; Pritchard et al. (2000)• collapsed Gibbs sampler; Griffiths & Steyvers (2004)
The collapsed Gibbs sampler
• Using conjugacy of Dirichlet and multinomial distributions, integrate out continuous parameters
• Defines a distribution on discrete ensembles z
ΦΦΦ=∫Δ
dpPPTW
)(),|()|( zwzw
ΘΘΘ= ∫Δ
dpPPDT
)()|()( zz
∑=
z
zzw
zzwwz
)()|(
)()|()|(
PP
PPP
∏ ∑∏
= +ΓΓ
Γ
+Γ=
T
jw
jw
Ww
jw
n
Wn
1)(
)(
)(
)(
)(
)(
β
β
β
β
∏ ∑∏
= +ΓΓ
Γ
+Γ=
D
dj
dj
T
j
dj
n
Tn
1)(
)(
)(
)(
)(
)(
α
α
α
α
The collapsed Gibbs sampler
• Sample each zi conditioned on z-i
• This is nicer than your average Gibbs sampler:• memory: counts can be cached in two sparse matrices• optimization: no special functions, simple arithmetic• the distributions on Φ and Θ are analytic given z and w,
and can later be found for each sample
Tn
n
Wn
nzP
i
i
i
i
i
d
dj
z
zw
ii +
+
+
+∝
••− )(
)(
)(
)(
),|( zw
Gibbs sampling in LDA
i wi di zi123456789
101112...
50
MATHEMATICSKNOWLEDGE
RESEARCHWORK
MATHEMATICSRESEARCH
WORKSCIENTIFIC
MATHEMATICSWORK
SCIENTIFICKNOWLEDGE
.
.
.JOY
111111111122...5
221212212111...2
iteration1
Gibbs sampling in LDA
i wi di zi zi123456789
101112...
50
MATHEMATICSKNOWLEDGE
RESEARCHWORK
MATHEMATICSRESEARCH
WORKSCIENTIFIC
MATHEMATICSWORK
SCIENTIFICKNOWLEDGE
.
.
.JOY
111111111122...5
221212212111...2
?
iteration1 2
Gibbs sampling in LDA
i wi di zi zi123456789
101112...
50
MATHEMATICSKNOWLEDGE
RESEARCHWORK
MATHEMATICSRESEARCH
WORKSCIENTIFIC
MATHEMATICSWORK
SCIENTIFICKNOWLEDGE
.
.
.JOY
111111111122...5
221212212111...2
?
iteration1 2
Gibbs sampling in LDA
i wi di zi zi123456789
101112...
50
MATHEMATICSKNOWLEDGE
RESEARCHWORK
MATHEMATICSRESEARCH
WORKSCIENTIFIC
MATHEMATICSWORK
SCIENTIFICKNOWLEDGE
.
.
.JOY
111111111122...5
221212212111...2
?
iteration1 2
Gibbs sampling in LDA
i wi di zi zi123456789
101112...
50
MATHEMATICSKNOWLEDGE
RESEARCHWORK
MATHEMATICSRESEARCH
WORKSCIENTIFIC
MATHEMATICSWORK
SCIENTIFICKNOWLEDGE
.
.
.JOY
111111111122...5
221212212111...2
2?
iteration1 2
Gibbs sampling in LDA
i wi di zi zi123456789
101112...
50
MATHEMATICSKNOWLEDGE
RESEARCHWORK
MATHEMATICSRESEARCH
WORKSCIENTIFIC
MATHEMATICSWORK
SCIENTIFICKNOWLEDGE
.
.
.JOY
111111111122...5
221212212111...2
21?
iteration1 2
Gibbs sampling in LDA
i wi di zi zi123456789
101112...
50
MATHEMATICSKNOWLEDGE
RESEARCHWORK
MATHEMATICSRESEARCH
WORKSCIENTIFIC
MATHEMATICSWORK
SCIENTIFICKNOWLEDGE
.
.
.JOY
111111111122...5
221212212111...2
211?
iteration1 2
Gibbs sampling in LDA
i wi di zi zi123456789
101112...
50
MATHEMATICSKNOWLEDGE
RESEARCHWORK
MATHEMATICSRESEARCH
WORKSCIENTIFIC
MATHEMATICSWORK
SCIENTIFICKNOWLEDGE
.
.
.JOY
111111111122...5
221212212111...2
2112?
iteration1 2
Gibbs sampling in LDA
i wi di zi zi zi123456789
101112...
50
MATHEMATICSKNOWLEDGE
RESEARCHWORK
MATHEMATICSRESEARCH
WORKSCIENTIFIC
MATHEMATICSWORK
SCIENTIFICKNOWLEDGE
.
.
.JOY
111111111122...5
221212212111...2
211222212212...1
…
222122212222...1
iteration1 2 … 1000
Effects of hyperparameters
• and control the relative sparsity of Φ and Θ• smaller , fewer topics per document• smaller , fewer words per topic
• Good assignments z compromise in sparsity
log Γ
(x)
x
∏ ∑∏
= +ΓΓ
Γ
+Γ=
T
jw
jw
Ww
jw
n
WnP
1)(
)(
)(
)(
)(
)()|(
β
β
β
βzw
∏ ∑∏
= +ΓΓ
Γ
+Γ=
D
dj
dj
T
j
dj
n
TnP
1)(
)(
)(
)(
)(
)()(
α
α
α
αz
Varying
decreasing increases sparsity
Varying decreasing
increases sparsity?
Number of words per topic
Topic
Num
ber
of w
ords
Nu U
zi
wi
(u)
(j)
(u)|su=0 Delta((u-1))(u)|su=1 Dirichlet()
zi Discrete( (u) )
(j) Dirichlet()
wi Discrete( (zi) )
T
Extension: a model for meetings
su
(u-1)…
(Purver, Kording, Griffiths, & Tenenbaum, 2006)
Sample of ICSI meeting corpus(25 meetings)
• no it's o_k. • it's it'll work. • well i can do that. • but then i have to end the presentation in the middle so i can go back to open up
javabayes. • o_k fine. • here let's see if i can. • alright. • very nice. • is that better. • yeah. • o_k. • uh i'll also get rid of this click to add notes. • o_k. perfect• NEW TOPIC (not supplied to algorithm)• so then the features we decided or we decided we were talked about. • right. • uh the the prosody the discourse verb choice. • you know we had a list of things like to go and to visit and what not. • the landmark-iness of uh. • i knew you'd like that. • nice coinage. • thank you.
Topic segmentation applied to meetings
Inferred Segmentation
Inferred Topics
Comparison with human judgments
Topics recovered are much more coherent than those found using random segmentation, no segmentation, or an HMM
Learning the number of topics
• Can use standard Bayes factor methods to evaluate models of different dimensionality• e.g. importance sampling via MCMC
• Alternative: nonparametric Bayes• fixed number of topics per document, unbounded
number of topics per corpus(Blei, Griffiths, Jordan, & Tenenbaum, 2004)
• unbounded number of topics for both (the hierarchical Dirichlet process)
(Teh, Jordan, Beal, & Blei, 2004)
The Author-Topic model(Rosen-Zvi, Griffiths, Smyth, & Steyvers, 2004)
Nd D
zi
wi
(a)
(j)
(a) Dirichlet()
zi Discrete( (xi) ) (j) Dirichlet()
wi Discrete( (zi) )
T
xi
A
xi Uniform(A (d) )
each author has a distribution over topics the author of each
word is chosen uniformly at random
Four example topics from NIPS
WORD PROB. WORD PROB. WORD PROB. WORD PROB.
LIKELIHOOD 0.0539 RECOGNITION 0.0400 REINFORCEMENT 0.0411 KERNEL 0.0683
MIXTURE 0.0509 CHARACTER 0.0336 POLICY 0.0371 SUPPORT 0.0377
EM 0.0470 CHARACTERS 0.0250 ACTION 0.0332 VECTOR 0.0257
DENSITY 0.0398 TANGENT 0.0241 OPTIMAL 0.0208 KERNELS 0.0217
GAUSSIAN 0.0349 HANDWRITTEN 0.0169 ACTIONS 0.0208 SET 0.0205
ESTIMATION 0.0314 DIGITS 0.0159 FUNCTION 0.0178 SVM 0.0204
LOG 0.0263 IMAGE 0.0157 REWARD 0.0165 SPACE 0.0188
MAXIMUM 0.0254 DISTANCE 0.0153 SUTTON 0.0164 MACHINES 0.0168
PARAMETERS 0.0209 DIGIT 0.0149 AGENT 0.0136 REGRESSION 0.0155
ESTIMATE 0.0204 HAND 0.0126 DECISION 0.0118 MARGIN 0.0151
AUTHOR PROB. AUTHOR PROB. AUTHOR PROB. AUTHOR PROB.
Tresp_V 0.0333 Simard_P 0.0694 Singh_S 0.1412 Smola_A 0.1033
Singer_Y 0.0281 Martin_G 0.0394 Barto_A 0.0471 Scholkopf_B 0.0730
Jebara_T 0.0207 LeCun_Y 0.0359 Sutton_R 0.0430 Burges_C 0.0489
Ghahramani_Z 0.0196 Denker_J 0.0278 Dayan_P 0.0324 Vapnik_V 0.0431
Ueda_N 0.0170 Henderson_D 0.0256 Parr_R 0.0314 Chapelle_O 0.0210
Jordan_M 0.0150 Revow_M 0.0229 Dietterich_T 0.0231 Cristianini_N 0.0185
Roweis_S 0.0123 Platt_J 0.0226 Tsitsiklis_J 0.0194 Ratsch_G 0.0172
Schuster_M 0.0104 Keeler_J 0.0192 Randlov_J 0.0167 Laskov_P 0.0169
Xu_L 0.0098 Rashid_M 0.0182 Bradtke_S 0.0161 Tipping_M 0.0153
Saul_L 0.0094 Sackinger_E 0.0132 Schwartz_A 0.0142 Sollich_P 0.0141
TOPIC 19 TOPIC 24 TOPIC 29 TOPIC 87
Who wrote what?
A method1 is described which like the kernel1 trick1 in support1 vector1 machines1 SVMs1 lets us generalize distance1 based2 algorithms to operate in feature1 spaces usually nonlinearly related to the input1 space This is done by identifying a class of kernels1 which can be represented as norm1 based2 distances1 in Hilbert spaces It turns1 out that common kernel1 algorithms such as SVMs1 and kernel1 PCA1 are actually really distance1 based2 algorithms and can be run2 with that class of kernels1 too As well as providing1 a useful new insight1 into how these algorithms work the present2 work can form the basis1 for conceiving new algorithms
This paper presents2 a comprehensive approach for model2 based2 diagnosis2 which includes proposals for characterizing and computing2 preferred2 diagnoses2 assuming that the system2 description2 is augmented with a system2 structure2 a directed2 graph2 explicating the interconnections between system2 components2 Specifically we first introduce the notion of a consequence2 which is a syntactically2 unconstrained propositional2 sentence2 that characterizes all consistency2 based2 diagnoses2 and show2 that standard2 characterizations of diagnoses2 such as minimal conflicts1 correspond to syntactic2 variations1 on a consequence2 Second we propose a new syntactic2 variation on the consequence2 known as negation2 normal form NNF and discuss its merits compared to standard variations Third we introduce a basic algorithm2 for computing consequences in NNF given a structured system2 description We show that if the system2 structure2 does not contain cycles2 then there is always a linear size2 consequence2 in NNF which can be computed in linear time2 For arbitrary1 system2 structures2 we show a precise connection between the complexity2 of computing2 consequences and the topology of the underlying system2 structure2 Finally we present2 an algorithm2 that enumerates2 the preferred2 diagnoses2 characterized by a consequence2 The algorithm2 is shown1 to take linear time2 in the size2 of the consequence2 if the preference criterion1 satisfies some general conditions
Written by(1) Scholkopf_B
Written by(2) Darwiche_A
Analysis of PNAS abstracts
• Test topic models with a real database of scientific papers from PNAS
• All 28,154 abstracts from 1991-2001• All words occurring in at least five abstracts,
not on “stop” list (20,551)• Total of 3,026,970 tokens in corpus
(Griffiths & Steyvers, 2004)
FORCESURFACE
MOLECULESSOLUTIONSURFACES
MICROSCOPYWATERFORCES
PARTICLESSTRENGTHPOLYMER
IONICATOMIC
AQUEOUSMOLECULARPROPERTIES
LIQUIDSOLUTIONS
BEADSMECHANICAL
HIVVIRUS
INFECTEDIMMUNODEFICIENCY
CD4INFECTION
HUMANVIRAL
TATGP120
REPLICATIONTYPE
ENVELOPEAIDSREV
BLOODCCR5
INDIVIDUALSENV
PERIPHERAL
MUSCLECARDIAC
HEARTSKELETALMYOCYTES
VENTRICULARMUSCLESSMOOTH
HYPERTROPHYDYSTROPHIN
HEARTSCONTRACTION
FIBERSFUNCTION
TISSUERAT
MYOCARDIALISOLATED
MYODFAILURE
STRUCTUREANGSTROM
CRYSTALRESIDUES
STRUCTURESSTRUCTURALRESOLUTION
HELIXTHREE
HELICESDETERMINED
RAYCONFORMATION
HELICALHYDROPHOBIC
SIDEDIMENSIONALINTERACTIONS
MOLECULESURFACE
NEURONSBRAIN
CORTEXCORTICAL
OLFACTORYNUCLEUS
NEURONALLAYER
RATNUCLEI
CEREBELLUMCEREBELLAR
LATERALCEREBRAL
LAYERSGRANULELABELED
HIPPOCAMPUSAREAS
THALAMIC
A selection of topics
TUMORCANCERTUMORSHUMANCELLS
BREASTMELANOMA
GROWTHCARCINOMA
PROSTATENORMAL
CELLMETASTATICMALIGNANT
LUNGCANCERS
MICENUDE
PRIMARYOVARIAN
Cold topics Hot topics
Cold topics Hot topics
2SPECIESGLOBALCLIMATE
CO2WATER
ENVIRONMENTALYEARS
MARINECARBON
DIVERSITYOCEAN
EXTINCTIONTERRESTRIALCOMMUNITYABUNDANCE
134MICE
DEFICIENTNORMAL
GENENULL
MOUSETYPE
HOMOZYGOUSROLE
KNOCKOUTDEVELOPMENT
GENERATEDLACKINGANIMALSREDUCED
179APOPTOSIS
DEATHCELL
INDUCEDBCL
CELLSAPOPTOTIC
CASPASEFAS
SURVIVALPROGRAMMED
MEDIATEDINDUCTIONCERAMIDE
EXPRESSION
Cold topics Hot topics
2SPECIESGLOBALCLIMATE
CO2WATER
ENVIRONMENTALYEARS
MARINECARBON
DIVERSITYOCEAN
EXTINCTIONTERRESTRIALCOMMUNITYABUNDANCE
134MICE
DEFICIENTNORMAL
GENENULL
MOUSETYPE
HOMOZYGOUSROLE
KNOCKOUTDEVELOPMENT
GENERATEDLACKINGANIMALSREDUCED
179APOPTOSIS
DEATHCELL
INDUCEDBCL
CELLSAPOPTOTIC
CASPASEFAS
SURVIVALPROGRAMMED
MEDIATEDINDUCTIONCERAMIDE
EXPRESSION
37CDNA
AMINOSEQUENCE
ACIDPROTEIN
ISOLATEDENCODING
CLONEDACIDS
IDENTITYCLONE
EXPRESSEDENCODES
RATHOMOLOGY
289KDA
PROTEINPURIFIED
MOLECULARMASS
CHROMATOGRAPHYPOLYPEPTIDE
GELSDS
BANDAPPARENTLABELED
IDENTIFIEDFRACTIONDETECTED
75ANTIBODY
ANTIBODIESMONOCLONAL
ANTIGENIGG
MABSPECIFICEPITOPEHUMANMABS
RECOGNIZEDSERA
EPITOPESDIRECTED
NEUTRALIZING