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Index
ACE-1, 164
ACE-2
annotations, 164, 165
evaluation, 164–166
acquisition bottleneck, 64
activity networks, 199
AdaBoost algorithm, 77, 78, 120
agglomerative algorithms, 85
Agrawal, C.C., 9, 24
AI tasks, 64
algorithm(s). See also Apriori algorithm; Borders algorithm
(LP)2, 120
3-D rendering, 220
AdaBoost, 77, 78, 120
agglomerative, 85
association generating, 26
BASILISK, 173–174
bootstrapping, 171
brute force, 85
Buckshot, 86, 88
BWI, 119–120
classic graph analysis, 262
clustering, 85–88
convex optimization, 72
cores and, 260–261
covering, 121
CRF, 121
Delta, 36, 41
documents structured by, 57
EM, 78, 90–91
EM-based mixture resolving, 85, 87
episode based, 41
evaluating, 121
FACT’s, 49
force-directed graph layout, 247, 248
forward–backward, 134, 141
frequent concept set, 24, 25
FUP, 36, 41
FUP2, 36, 41
general graphs fast, 250
HAC, 85, 87–88
HMM, 121
Hobbs, 112
human-language processing, 60
IE, 98, 119
incremental, 30, 36
inductive, 119, 121
ISO-DATA, 86
KK, 249
K-means, 85, 86, 88
knowledge discovery, 2, 17, 193
layout, 247, 248
learning, 68
MEMM, 121
metabootstrapping, 170
mixture-resolving, 87
ML, 70
Naive, 112
optimization, 248
O-Tree, 124–125
pattern-discovery, 1, 5
preprocessing methodologies, 57
probabilistic extraction, 121
Ripper, 74, 300
salience, 114
search, 36, 178, 238
sequential patterns mining, 30
shuffling, 85
SOMs generated, 214, 217–218
spring-embedder, 247
SVM, 76–77
tasks for, 58
text mining, 5, 8
Viterbi, 133–134, 138, 141
WHISK, 119
alone, maximal associations and, 27
ambiguities
part-of-speech, 58
analysis
banking, 282
critical path, 199, 236
data, 64
dependency, 61
domain, 105
Industry Analyzer corporate, 294–296
lexical, 105, 106, 107
linguistic, 109
morphological, 59, 105
patent, 297, 300
sentence, 109
syntactic, 105
textual, 146–152
time-based, 30
trend, 9, 30–31, 41, 301, 305
anaphora
NLP, 118
one-, 111
ordinal, 111
pronominal, 110
anaphora resolution, 109–119, 332–333
approaches to, 109, 112, 113–114, 116, 117–119
annotations
ACE-2, 164, 165
corpus, 109, 166
answer-sets, 1, 23
antecedent
closest, 118
most confident, 118
nonpronominal preceding, 118
application. See also Document Explorer application; GeneWays; knowledge discovery in text language, application; Patent Researcher; text mining applications
area, 203
business intelligence, 282
creating custom, 287
horizontal, 297
KDD, 13
patent analysis, 297
TC, 64, 65–66
text mining, xi, 8
apposition, 110
Apriori algorithm, 24, 36
associations generated with, 37
textual application of, 39
architects. See system, architects
architecture
considerations, 192–194
FACT’s, 47
functional, 13, 192
GeneWays’, 310–312
IE, 104–109
Industry Analyzer, 283–290
open-ended, 116
preprocessing, 58
system, 47, 186
text mining system’s, 13–18
articulation points, 262
assignment function F, 66
association(s), 19, 25–26. See also ephemeral associations; maximal associations
algorithm for generating, 26
Apriori algorithm generation of, 37
browsing tools for, 181
clustering, 181
concept, 9
concept sets and, 25
constraints, 181–183
discovery of, 24, 45, 46
displaying/exploring, 180–182
ephemeral, 30, 32
generating, 40
graphs, 199–201
left-hand side (LHS) of, 26, 45, 181
M-, 28
market basket type, 24, 25, 39
overwhelming, 181
partial ordering of, 203
query, 45, 46
right-hand side (RHS) of, 26, 45, 201
association rules, 24, 25, 27, 182
circle graphs and, 209
definitions for, 40
discovering, 26–27
modeling, 211
search results, 36
for sets, 201
attribute(s)
extracting, 96
relationship rules, 42
auditing environment, 94
automata. See finite-state automata
automated categorization, 67
AutoSlog-TS system, 166–168
average concept
distribution, 22, 23
proportion, 22
background
constraints, 186
states, 149
background knowledge, 8, 42, 276
access to, 8
concept synonymy and, 45
constraints crafted by, 45
creating, 16
document collections and, 46
domains and, 8
FACT’s exploitation of, 46
forms of, 42
generalized v. specialized, 276–278
GeneWays’ sources of, 310
Industry Analyzer implementation of, 283
integrating, 45
large amounts of, 277
leveraging, 8
maintenance requirement, 278
pattern abundance limited by, 45
polysemy and, 45
preservation of, 16
sources of, 277
specialized, 277
text mining systems and, 8, 16, 42, 45
back-propagation, 75
backward variable, 133, 141
Bacon, Kevin. See Kevin Bacon game
bagging, 77–78
bag-of-words model, 68, 89
banking analysts, 282
baseline classifiers, 80
BASILISK algorithm. See Bootstrapping Approach to Semantic Lexicon Induction using Semantic Knowledge
Baum–Welsh reestimation formulas, 135, 136, 147, 151
Bayesian approximation, 120
Bayesian logistic regression (BLR), 71–72
benchmark collections, 79–80
best-first clustering, 118
betweeness centrality, 254–255, 258
bigrams, 5
binary
categorization, 67
matrix, 245
predicates, 16
relation, 244, 245
SVM classifiers, 76
tree, 73
binary-valued trigger function, 139
bins, 66
BioGen Idec Inc., 285, 293
biological pathways
information, 276
text mining,
biological pathways information, 276
biotech industry, 290–291
BioWisdom company, 277
BioWorld, xi, 41, 283
Bioworld Online, 296
block modeling, 264–268, 272
hijacker network and, 268–272
pajek, 270
BLR. See Bayesian logistic regression
Blum, A., 172
Bonacich, P., 256
Boolean constraints, 328
Boolean expressions, 179
boosted wrapper induction (BWI) algorithm, 119–120
boosting, 77–78
classifiers, 77
indicators, 115
boosting classifiers, 77
boosting indicators, 115
bootstrapping
algorithm, 171
categorization, 174–175
IE and, 166
introduction to, 166–168
meta-, 169
multi-class, 174
mutual, 168
problems, 172
single-category, 174
Bootstrapping Approach to Semantic Lexicon Induction using Semantic Knowledge (BASILISK) algorithm, 173–174
border sets, 36
Borders algorithm, 36
benefits of, 37
notational elements of, 37
Property 1 of, 37
Property 2 of, 37
Stage 1 of, 37
Stage 2 of, 38
Borgatti, S.P., 264
Brown Corpus tag set, 60
Brown, R.D., 229
browsers, 177. See also Title Browser
character-based, 191
distribution, 240
Document Explorer, 240–241
interactive distribution, 240
browsing. See also scatter/gather browsing method
defined, 177–185
distributions, 179
hierarchical, 23
interface, 179, 189
interfaces, 278
methods, 179
navigational, 10
pattern, 14
result-sets for, 11
software for, 177
support operations, 204
text mining system, 10
tools, 181
tree, 15
user, 10, 13
brute force
algorithm, 85
search, 9
Buckshot algorithm, 86, 88
business
intelligence, 281, 282
sector, 282
BWI. See boosted wrapper induction
C4.5 procedure, 73
Cardie, Claire, 118
Carnegie group, 70
CART procedure, 73
categorization. See also text categorization
attributes, 45
automated, 67
binary, 67
bootstrapping, 174–175
category-pivoted, 67
document-pivoted, 67
hard, 67
hierarchical Web page, 66
manual-based, 6, 12
methodologies of, 6
multilabel, 67
online, 67
patent analysis and, 300
POS tag set, 60
POS word, 60
preprocessing methodology, 57
problems, 82
relationship based, 45
rule-based, 6
single-label, 67
soft (ranking), 67
systems, 91
categorization status value (CSV), 67
category connecting maps, 212–213, 242
category domain knowledge, 42
CDM-based methodologies, 7, 12
centrality, 251
betweeness, 254–255, 258
closeness, 253
definitions used for, 251
degree, 251–253, 257
eigenvector, 255–256
measures of, 251
natural language text, 1
power, 256–257
centralization, network, 257–258
centroids, medoids v., 90
character(s), 5, 8
classes, 327
representations of, 5
character-level regular expressions, 326
chi-square measures, 69, 201
chunking, NP, 154
CIA World Factbook, 46, 48, 50
circle graphs, 190, 209–214, 288
click-sensitive jumping points of, 211
controls of, 212
data modeling by, 214
interactivity, 190
mouse-overs and, 211
multiple, 213–214
nodes, 294
style elements of, 211
usefulness of, 209
visualization, 294
classes
character, 327
equivalence, 203
classification
line, 153
schemes, 131
classifier(s)
baseline, 80
binary SVM, 76
boosting, 77
building, 66
common, 68
comparing, 80
continuous, 67
decision rule, 73–74
decision tree, 72–73
example-based, 75–76
ith, 77
k different, 77
kNN, 75
machine learning, 70
ME, 153
NB, 71, 78, 90–91
probabilistic, 71, 78
Rocchio, 74–75
stateless ME, 153
symbolic, 72
text, 76, 79–80
training, 79
classifier committees, 77–78
ClearForest Corporation, 296
ClearForest Text Analytics Suite, 296, 298
ClearResearch, 232
closeness centrality, 253
cluster(s)
chain-like, 87
complete-link, 87, 88
gathering, 83
k, 88
labels, 91
postprocessing of, 86
scattering, 83
single link, 87, 88
cluster hypothesis, 82
cluster-based retrieval, 84
clustering. See also nearest neighbor clustering
algorithms, 85–88
associations, 181
best-first, 118
defined, 70, 82
disjoint, 75
documents grouped by, 83
flat (partial), 85
good, 84
hard, 85
hierarchical, 83
optimization, 85
problem, 84–85, 89
quality function, 84, 92
query specific, 83
soft, 85
tasks, 82–84
term, 69
text, xi, 89, 91–92
tools, 11, 184–185
unsupervised, 185
usefulness of, 82
users and, 83
of vertices, 266
CO. See coreference task
CogNIAC, 113
collections, benchmark, 79–80
color
assigning of, 281
coding, 46, 291
GUI palette of, 281
Columbia University, 309, 312–313
column-orthonormal, 90
combination graphs, 213–214
command-line query interpreters, 10
committees
building, 77
classifier, 77–78
components
bi-connected, 262
presentation layer, 14
strong, 262
weak, 262
computational linguistics, 1, 3
concept(s), 5–8
associations of, 9
context, 33
co-occurrence, 9, 23
DIAL language, 317
distribution, 21
distribution distance, 29
documents, 23
extraction, 7
features, 12
graphs, 204–205
guards, 328–329
hierarchy node, 20
identifiers, 6, 197
interdocument association, 9
keywords v., 12
link analysis, 227
names, 326
occurrence, 19
output, 156
patterns, 10
proportion, 22, 29
proportion distance, 29
proportion distribution, 21, 22
representations, 7
selection, 19, 20
sentences, 321
subsets of, 203
synonymy, 45
concept hierarchies, 43
editing tools, 184
maintaining, 183
navigation/exploration by, 182
node, 20
roles of, 182
taxonomy editors and, 183–184
concept set(s), 22
associations and, 25
cosine similarity of, 202
display of, 196
graphs, 196, 197
concision, 191
conditional models, 140
conditional probability, 71, 142
computing, 143
conditional random fields (CRFs), 142–144
algorithm, 121
chunk tagger, 155
chunker, 154
development of, 153
formalism, 153
linear chain, 142
part-of-speech tagging with, 153–154
problems relating to, 143
shallow parsing with, 154–155
textual analysis and, 153–155
training of, 144
conditions, 120
confidence, 25
M-, 27, 28
threshold, 181
constants. See also string, constants
Boolean, 328
rule, 327–328
constituency grammars, 60–61
constraint(s), 42
accessing, 185–186
association, 181–183
background, 186
background knowledge crafting of, 45
comparison, 328
controls, 191
FACT’s exploitation of, 46
functions, 139
leveraging, 278
logic of, 186
parameters, 45
Patent Researcher’s, 300–301
quality, 186
query, 280
redundancy, 186
refinement, 11, 14, 19–41, 191, 286–287, 300–301
search, 178, 204
syntactical, 186
types of, 186
CONSTRUE system, 70, 73
contained matches, 101
context. See also temporal context relationships
concept, 33
DIAL language, 321
focus with, 191
phrase, 33
relationships, 32, 33
context equivalence, 203
context graphs, 30, 32, 33–35
components of, 33
defined, 33
context-dependent probabilities, 149, 152
continuous real-valued functions, 74
control elements, 191
controlled vocabulary, 65
convex optimization algorithms, 72
co-occurrence
concept, 9, 23
frequency of, 24
relationships, 12
core
algorithm for finding, 260–261
vertices of, 260
core text mining operations, 14, 19–41, 286–287
Patent Researcher and, 300–301
coreference
function–value, 111
part–whole, 112
proper names, 110
resolution, 109, 112
coreference task (CO), 99
coreferring phrases, 109
corporate finance, 275
business intelligence performed in, 281
text mining applications, 286
corpus
annotated, 109, 166
MUC-4, 170
cosine similarity, 90, 201, 202
Costner, Kevin, 250
cotraining, 78, 172
cover equivalence, 203
covering algorithm, 121
σ-cover sets, 24. See also singleton, σ-covers
FACT’s generation of, 49
CRFs. See conditional random fields
critical path, 235
analysis, 199, 236
diagrams, 235
graphs, 236
Croft, W.B., 76
cross-referencing, 6
CSV. See categorization status value
Cui, Y., 197, 199
CUtenet, 312
Cutting, D.R., 92
cycle, 245
graph, 250–251
transmission/emission, 131
DAGs. See directed acyclic graphs
Daisy Analysis, 226, 237
Daisy Chart, 226
DAML, 277
DARPA, 96
data
abstraction, 91
analyzing complex, 64
Apriori algorithm and textual, 39
clustering, 14, 88–92
color-coding of, 46
comparing, 29
currency, 36
discovering trends in textual, 30
dynamically updated, 36
exploration, 184–185
GeneWays’ sources of, 310
identifying trends in, 9
inter-document’s relationships with, 2
modeling, 214
Patent Researcher, 299
patterns of textual, 40
preparing, 57
scrubbing/normalization of, 1
sparseness, 136–137, 148
textual, 88–92, 189
thresholds for incremental, 39
unlabeled, 78
unstructured, 194
visualization of, 218
data mining
analysis derived from, 10
border sets in, 36
pattern-discovery algorithms, 1
preprocessing routines, 1
presentation-layer elements, 1
text mining v., 1, 11
visualization tools, 1
database
GenBank, 310
MedLine, 11, 78, 277
OLDMEDLINE, 12
relational, 4
Swiss-Prot, 310
decision
rule classifier, 73–74
tree (DT) classifiers, 72–73
decomposition, singular value, 89–91
definite noun phrases, 117
definiteness, 115
degree centrality, 251–253, 257
Delta algorithms, 36, 41
demonstrative noun phrases, 117
dense network, 246
dependency
analysis, 61
grammars, 61
detection. See deviation, detection
deviation
detection, 10, 13, 30, 32, 41
sources of, 32, 41
diagrams
critical path, 235
fisheye, 228–232
DIAL language, 285, 299
code, 320–322
concept, 317
concept declaration, 317
context, 321
Discovery Module, 319
engines, 317
examples, 329–330, 331–332, 333–336
information extraction, 318–319
module, 318–319
plug-in, 321
scanner properties, 320
searches, 321
sentence concept, 321
text pattern, 317–318
text tokenization, 320
dictionaries, 106
dimension reduction, 69, 89
LSI with, 89
SVD and, 90
dimensionality
document collection’s high, 216
document reduction, 89
feature, 4, 12
direct ephemeral association, 31
directed acyclic graphs (DAGS), 43, 61, 197
activity networks and, 199
ontological application of, 197
visualization techniques based on, 199
directed networks, 251, 262
disambiguation, 156
disconnected spring graphs, 235
discovery
association rules, 26–27
of associations, 24, 45, 46
ephemeral associations, 10, 13
frequent concept sets, 24, 39, 40
methods, 24
Discovery Module, 319
disjoint clusters, 75
dissimilarity matrix, 249
distance, referential, 116
distribution(s), 9, 19–23
average, 23
average concept, 22
Boolean expressions generation of, 179
browsing, 179
comparing specific, 23
concept, 21
concept co-occurrence, 23
concept proportion, 21, 22
conditional probability, 142
interestingness and, 29–30
patterns based on, 29, 32, 303
queries, 206, 294
text mining systems and, 22, 29
topic, 31
divide-and-conquer strategy, 58
DNF rules, 73
document(s)
algorithm’s structuring of, 57
association of, 9
bag-of-words, 89
binary, 73
binary vector of features as, 4
bins, 66
clustering of, 83
collections of, 4
concept-labeled, 23
correlating data across, 2
co-training, 78
data relationships and, 2
defined, 2–4
dimensionality reduction of, 89
document collection’s adding of, 36
features of, 4–8, 12
field extraction, 59, 146–148
free format, 3
good, 66
IE representation of, 95
irrelevant, 66
managing, 64
manually labeling, 78
meaning, 59
native feature space of, 5
natural language, 4
news feed, 32
O-Tree, 125
patent, 306
portraying meanings of, 5
proportion of set of, 20
prototypical, 3
quantities for analyzing, 20
relevant, 66
representations of, 4, 5, 6, 7, 58, 68
retrieval of, 179
scope of, 3
semistructured, 3–4
sorting, 65–66
sources of, 59
tagging, 94
task structuring of, 57
test sets of, 79
text, 3
typographical elements of, 3
unlabeled, 78
weakly structured, 3, 4
document collection(s)
analyzing, 30
application area of, 203
background knowledge and, 46
defined, 2–3, 4
documents added to, 36
dynamic, 2
high-dimensionality, 216
Industry Analyzer, 283
processed, 15
PubMed as real-world, 2
scattering, 83
static, 2
subcollection, 19, 30
Document Explorer application, 18
browsers, 240–241
development of, 237
knowledge discovery toolkit of, 239–241
modules, 237
pattern searches by, 237
term hierarchy editor, 239
visualization tools of, 237, 241
domain(s), 8
analysis, 105
background knowledge and, 8
customization, 278
defined, 42
domain hierarchy with, 43
hierarchy, 43
knowledge, 42, 58, 59
lexicons, 44
ontology, 42–43, 44, 51
scope of, 8, 42
semistructured, 121
single application, 12
terminology preference, 116
text mining system’s, 16
DT classifier. See decision tree (DT) classifiers
Eades, P., 232, 248
edges, 33, 35
Eigenvector centrality, 255–256
EM. See expectation maximization
e-mail, 3
energy minimization, 250
engineering
knowledge, 70, 155
engines
DIAL, 317
GeneWays’ parsing, 310
IE, 95
pronoun resolution, 113
query, 16
search, 82, 200
entities
choosing query, 277
content-bearing, 94
equivalence between, 262–263
extracting, 96, 149, 150, 156, 164
hierarchy, 95
IE process and relevant, 95
links between, 244
multiple, 101
real world, 309
ephemeral associations, 30
defined, 31–32
direct, 31
discovery, 10, 13
examples, 31
inverse, 32
episodes, algorithms based on, 41
equivalence
classes, 203
context, 203
cover, 203
entity, 262–263
first, 203
regular, 263
structural, 263
Erdös number, 250
Erdös, Paul, 250
error(s)
false negative, 74
false positive, 74
matrix, 270
precision, 66
recall, 66
non-Euclidean plane, 218
evaluation workbench, 116
event
example of, 94
extraction, 96
Everett, M.G., 264
exact matches, 101
example-based classifiers, 75–76
expectation maximization (EM), 78, 87, 90–91
mixture resolving algorithm, 85, 87
Explora system, 18
exploration
concept hierarchy, 182
external ontologies, 100
extraction. See also field extraction; Nymble
algorithms, 121
attribute, 96
concept, 7
DIAL examples of, 329–330, 331
domain-independent v. domain-dependent, 98
entities, 96, 149, 150, 156, 164
event, 96
fact, 96
feature, 69, 84, 285
grammars, 138
HMM field, 146–148
information, 2, 11, 61–62, 119
literature, 190
Los Alamos II-type concept, 7
relationship, 156, 164–166
ST, 99
structural, 122
TEG, 164
term, 6, 12, 95, 285
text, 96
TR, 99
visual information, 122
F assignment function, 66
FACT. See Finding Associations in Collections of Text
facts, 94
extracting, 96
FAQ file, 153
FBI Web site, 246
feature(s). See also native feature space
concept-level, 12
dimensionality, 4, 12
document, 4–8, 12
extraction, 69, 84, 285
linguistic, 100
markable, 117
orthographic, 100
relevance, 69
selection, 68–69, 84, 100
semantic, 100
space, 85
sparsity, 4
state, 142
synthetic, 69
transition, 142
Feldman, R., 46
field extraction, 59, 146
location, 149
speaker, 149, 152
files
PDF, 3
word-processing, 3
filters, 14
fisheye view, 230–231
information, 14
personalized ad, 66
redundancy, 203
simple specification, 185–186
text, 65–66
finance. See corporate finance
Finding Associations in Collections of Text (FACT), 18, 46–50
algorithm of, 49
background knowledge exploitation by, 46
constraints exploited by, 46
σ-cover sets generated by, 49
designers of, 50
implementing, 47–49
performance results of, 50
query language, 46
system architecture of, 47
Findwhat, 200
finite-state automata, 156
first equivalence, 203
fisheye diagrams, 228–232
fisheye interface, 231
fisheye views
distorting, 229, 231
effectiveness of, 231–232
filtering, 230–231
fixed thresholding, 67
focus with context, 191
force-directed graph layout algorithms, 247, 248
formats, converting, 59
formulas
Baum–Welsh reestimation, 135, 136
network centralization, 257
forward variable, 132, 141
forward–backward algorithm, 134, 141
forward–backward procedure, 132–133
FR method, 248–250
fractal approaches, 230
fragments, text, 109
frames
hierarchy, 95
structured objects as, 95
Freitag, D., 146
frequent concept sets, 9, 23–24
algorithm for generating, 24, 25
Apriori-style, 36
discovery methods for, 24, 39, 40
generating, 24
identifying, 25
natural language in, 24
near, 25
σ-cover sets as, 24
σ-covers as, 24
front-end, de-coupled/loosely coupled, 193
Fruchterman, T., 232, 248. See also FR method
function(s)
binary-valued trigger, 139
clustering quality, 84
constraint, 139
continuous real-valued, 74
similarity, 84, 201–202
trigger-constraint, 153
functional architecture, 13, 192
functionality
GeneWays’, 310–312
Industry Analyzer system, 292
Patent Researcher’s, 298–302
types of, 10
function–value coreference, 111
FUP algorithm, 36, 41
FUP2 algorithm, 36, 41
Furnas, G., 229
fuzzy search, 184
game. See Kevin Bacon game
Gaussian priors, 71
Gelbukh, A., 9
GenBank database, 310
Gene Ontology Consortium, 43, 277
Gene OntologyTM knowledge base, 43, 51, 197
generalized iterative scaling, 140, 144
generative models, 140
generative process, 131
generic noun phrases (GN), 171. See also noun phrases; phrases, coreferring; pronoun resolution engine; proper noun phrases
GeneWays, xi, 309
architecture/functionality of, 310–312
background knowledge sources, 310
core mining operations of, 312
core mission of, 309
CUtenet of, 312
data sources, 310
GUI, 312
implementation/usage, 312
Industry Analyzer comparison with, 309
Parsing Engine, 310
Patent Researcher comparison with, 309
preprocessing operations, 310–312
presentation layer elements, 312
Relationship Learner module, 311
specialized nuances of, 310
Synonym/Homonym Resolver, 311
GENomics Information Extraction System (GENIES), 310
giveness, 115
GN. See generic noun phrases
Google, 200. See also search
grammars. See also stochastic context-free grammars
ambiguity of, 137
canonical, 137
constituency, 60–61
dependency, 61
extraction, 138
nonstochastic, 137
TEG, 157, 158
graph(s). See also circle graphs; line graphs; singleton, vertex
analysis algorithm, 262
combination, 213–214
concept, 204–205
concept set, 196, 197
connected spring, 235
connection, 201
context, 30, 32, 33–35
critical path, 236
cycles in, 250–251
disconnected spring, 235
drawing large, 250
fast algorithm, 250
general undirected, 249
histogram-based trend, 309
multivertex, 199
node-and-edge, 228
paths, 250–251
simple concept, 195–206, 241, 288, 296
simple concept association, 199–201
spring embedded network, 232
temporal context, 30, 32, 35
theory, 244
trend, 30, 32, 35, 242
graphical user interface (GUI), 14, 46. See also interface
developers, 227
display modalities of, 178
easy to use controls of, 177
GeneWays, 312
histogrammatic representations situated in, 206
palette of colors, 281
Patent Researcher’s, 301–302
queries, 286
text mining application’s, 177
grouping, perceptual, 59, 123–124
GUI. See graphical user interface
HAC algorithm. See hierarchical agglomerative clustering algorithm
Hadany, R., 250
Harel, D., 250
heuristics. See syntactic heuristics
hidden Markov model algorithm, 121
hidden Markov models (HMMs), 131–137. See also maximum entropy Markov model
assumptions of, 132
characteristics, 147
classes of states, 147
classic, 151
defined, 131–137
document field extraction by, 146–148
entity extractor, 164
field extraction, 146–148
fully connected, 153
MEMM outperformed by, 153
Nymble and, 150
optimal, 148
POS tagging and, 156
problems related to, 132
single-state, 148
textual analysis and, 146–152
topology, 147, 148
training, 135–136
hierarchical agglomerative clustering (HAC) algorithm, 85, 87–88
hierarchy. See also trees, hierarchical
clustering, 83
concept, 43
domain, 43
editor, 239
entity/frame, 95
internal nodes of, 23
IS_A-type, 284
object’s, 123
ontology, 46
shrinkage, 148
Web page, 66
hijacker(s)
network, 268–272
9/11, 246
histogram(s), 206–208, 288
distribution pattern demonstration in, 303
graphs, 309
interactivity of, 208
link analysis and, 227
HMMs. See hidden Markov models
Hobbs algorithm, 112
holonymy, 112
homonymy, 69
Honkela, T., 217
Hooke’s Law, 248
HSOM. See hyperbolic self-organizing map
HTML
Web pages, 3, 66
WYSIWYG editor, 3
human(s)
knowledge discovery view by, 13, 17, 177
language processing, 60
hybrid system
introduction to, 155–156
TEG as, 156
hybrid tools, 222–225
hyperbolic non-Euclidean plane, 218
hyperbolic self-organizing map (HSOM), 226
hyperbolic trees, 218–220
hypernyms, 184
hyperplanes, 76
hypertext, 66
hyponyms, 184
hypothesis
cluster, 82
weak, 77
IBM, 200
ID3 procedure, 73
identical sets, 111
identifiers, 6, 197
IdentiFinder. See Nymble
IE. See information extraction
Imielinski, T., 24
immediate reference, 115
incremental algorithms, 30, 36
incremental update schemes, 38
indefinite noun phrases, 117
indexing, 65, 83. See also latent semantic indexing
indicating verbs, 115
inductive algorithm, 119, 121
inductive rule learning, 73
Industry Analyzer system, 282
architecture/functionality of, 283–290
background knowledge implementation, 283
ClearForest Text Analytic’s Suite and, 299
color coding, 291
core mining operations, 286–287
corporate analysis and, 294–296
document collection, 283
ease-of-use features of, 287
event-type query, 292
functionality, 292
GeneWays comparison with, 309
graphical menus, 290
implementation of, 284
merger activity with, 290
preprocessing operations, 284–286
presentation layer, 287–290
refinement constraints, 286–287
scope of, 282
search results, 293
term extraction, 285
visualization tools, 294
inferencing, 108–109
influence, 251
information
age, x
biological pathways, 276
control of, 230
exploring, 294–296
filtering, 14
flow, 105–109
gain, 69
retrieval, 1, 2, 62, 82
information extraction (IE), 2, 11, 61–62, 122
algorithms, 98, 119
architecture, 104–109
auditing environment, 94
benchmarks, 155
bootstrapping approach to, 166
DIAL language, 318–319
documents represented by, 95
engine, 95
evaluation, 100, 101
evolution of, 96–101
examples, 101, 102, 104
hybrid statistical, 155–166
information flow in, 105–109
knowledge-based, 155–166
MEMM for, 152–153
relevant entities and, 95
SCFG rules for, 155–166
schematic view of, 95
specialized dictionaries for, 106
statistical/rule-based, 156
structured, 122
symbolic rules of, 119
usefulness of, 94, 104
input–output paradigm, 13
inquiry, analytical, 275
Inxight Software, 218
interactivity
circle graph, 190
concept graph, 204–205
facilitating, 195
histogram, 208
user, 179, 189
interestingness
defining, 29
distributions and, 29–30
knowledge discovery and, 40
measures of, 9, 179
proportions and, 29–30
interface. See also graphical user interface
browsing, 179, 189, 278
fish-eye, 231
Patent Researcher’s Taxonomy Chooser, 299
query language, 10
visualization, 191
WEBSOM’s, 216
interpreters, 10
Iossifov, I., 312
IS_A-type hierarchies, 284
ISO-DATA algorithm, 86
ith classifier, 77
Jones, R., 168, 169
k clusters, 88
k different classifiers, 77
Kamada, T., 232, 248. See also KK method
Kawai, S., 232, 248. See also KK method
KDTL. See knowledge discovery in text language
Kevin Bacon game, 250
keywords
assigning, 65
concepts v., 12
KK method, 248, 249
K-means algorithm, 85, 86, 88
k-nearest neighbor (kNN) classifier, 75
kNN classifier. See k-nearest neighbor (kNN) classifier
knowledge
base, 17
category domain, 42
distillation, 14
domain specific, 58, 59
engineering, 64, 70, 155
knowledge discovery
algorithms, 2, 17, 193
distribution-type pattern’s, 32
Document Explorer toolkit for, 239–241
human-centered view of, 13, 17, 177
interestingness and, 40
overabundance problems of, 179
Patent Researcher’s, 301
patterns of, 14
problem-sets, 194
supplementing, 31
knowledge discovery in text language (KDTL), 18, 52
application, 18, 237
queries, 52–54, 55, 237
Kohonen maps, Kohonen networks. See self-organizing maps
Kohonen, T., 214
Koren, Y., 250
Kuhn–Tucker theorem, 139, 140
label
bias problem, 142
sequence, 144
Lafferty, J., 153
Lagrange multipliers, 139
language. See also natural language processing; sublanguages
DIAL, 285
FACTS’s query, 46
natural, 4
processing human, 60
query, 10, 14, 51–52, 177
soft mark-up, 3
Laplace
priors, 71, 72
smoothing, 136
Lappin, S., 113
Larkey, L.S., 76
latent semantic indexing (LSI), 69, 89
dimension reduction with, 89
layout
algorithms, 247, 248
force-directed, 248
network, 246–250, 277
learner, weak, 77
learning. See also machine learning
algorithms, 68
inductive rule, 73
rules, 74
supervised, 70
Leass, H.J., 113
left-hand sides (LHSs), 26, 45, 181
lemmas, 60
lemmatization, 6, 285
Lent, B., 9
lexical analysis, 105, 106, 107
lexical reiteration, 115
lexicons, 8, 42, 44
domain, 44
external, 285
GN, 171
PNP, 171, 172
semantic, 169, 170
LHSs. See left-hand sides
libraries. See also National Library of Medicine
graphing software, 208
integration/customization of, 208
life sciences, 275
business sector, 282
research,
LINDI project, 18
line graphs, 208–209
link analysis and, 227
multi-, 209
as prototyping tools, 208
linear least-square fit (LLSF), 74
linguistic(s)
computational, 1, 3
features, 100
processing, 285
sentence analysis, 109
link(s)
detection, 231–232
between entities, 244
operations, 204–205
link analysis, 226, 237
concepts, 227
histograms and, 227
line graphs and, 227
software packages for, 273–274
literature, extraction of, 190
LLSF. See linear least-square fit
Los Alamos II-type concept extraction, 7
loss ratio parameter, 74
Louis-Dreyfus, Julia, 250
(LP)2 algorithm, 120
LSI. See latent semantic indexing
Lycos, 200
machine learning (ML), 64, 70–78, 166
algorithms, 70
anaphora resolution and, 117–119
classifier, 70
techniques, 70–71
MacKechnie, Keith, 250
mapping, structural, 125–127
maps, category connecting, 212–213, 242
marginal probability, 71
markables, 117
market basket
associations, 24, 25, 39
problems, 25, 39
M-association, 28
matches
contained, 101
exact, 101
overlapped, 101
matrix
binary, 245
dissimilarity, 249
error, 270
transmission, 143
maximal associations. See also M-association; M-confidence; M-frequent; M-support
alone and, 27
M-factor of, 40
rules, 27, 40
maximal entropy (ME), 131, 138–140, 153
maximal entropy Markov model (MEMM), 140–141
algorithm, 121
comparing, 153
HMM and, 153
information extraction and, 152–153
training, 141
maximum likelihood estimation, 91
McCallum, A., 146
M-confidence, 27, 28
ME. See maximal entropy
measures
centrality, 251
chi-square, 69, 201
interestingness, 9, 179
network centralization, 257
performance, 79
similarity, 85
uniformity, 152
MedLEE medical NLP system, 311
MedLine, 11, 78, 277
medoids, centroids v., 90
MEMM. See maximal entropy Markov model
merger activity, 290–291
meronymy, 112
MeSH, 277
MESH thesaurus, 65
Message Understanding Conferences (MUC), 96–101
metabootstrapping, 169, 170
methodologies. See also preprocessing methodologies
categorization, 6
CDM-based, 7, 12
information extraction, 11
term-extraction, 6, 12, 95, 285
text mining, 9
M-factor, 40
M-frequent, 28
Microsoft, 200
middle-tier, 193
Miller, James, 226
minconf thresholds, 26, 40
minimal spanning tree (MST), 88
minimization, 250
minsup thresholds, 26, 40
Mitchell, T.M., 172
mixture-resolving algorithms, 87
ML. See machine learning
models
block, 264–268, 272
conditional v. generative, 140
data, 214
Document Explorer, 237
ME, 138–140
probabilistic, 131
module. See also Discovery Module
DIAL language, 318–319
Document Explorer application, 237
GeneWays Relationship Learner, 311
Montes-y-Gomez, M., 8–10
morphological analysis, 59, 105
most probable label sequence, 144
MSN, 200
MST. See minimal spanning tree
M-support, 27, 28
MUC. See Message Understanding Conferences
MUC-4 corpus, 170
MUC-7 Corpus Evaluation, 164
multilabel categorization, 67
Murphy, Joseph, 309
multivertex graphs, 199
Mutton, Paul, 232
Naive algorithm, 112
Naive Bayes (NB) classifiers, 71, 78, 90–91
named entity recognition (NER), 96, 164
names
concept, 326
identifying proper, 106–107
proper, 97
thesaurus, 325
wordclass, 324–325
NASA space thesaurus, 65
National Cancer Institute (NCI), 284
National Cancer Institute (NCI) Metathesaurus, 296
National Center for Biotechnology Information (NCBI), 11
National Institute of Health (NIH), 11
National Library of Medicine (NLM), 2, 11, 277, 284
native feature space, 12
natural language processing (NLP), 4
anaphoric, 118
components, 60
elements, 117
field extraction and, 146
frequent sets in, 24
general purpose, 58, 59–61
MedLEE medical, 311
techniques, 58
natural language text, 1
navigation, concept hierarchy, 182
NB classifiers. See Naive Bayes classifiers
NCBI. See National Center for Biotechnology Information
NCI. See National Cancer Institute
near frequent concept sets, 25
nearest neighbor clustering, 88
negative borders, 36
NER. See named entity recognition
NetMap, 210
NetMiner software, 274
network(s). See also spring embedding, network graphs
activity, 199
automatic layout of, 246–250, 277
centralization, 251, 257–258
clique, 246
complex, 75
dense, 246
directed, 251, 262
formulas, 257
hijacker, 268–272
layered display of, 261
layout, 246–250, 277
neural, 75
nonlinear, 75
partitioning of, 259–272
pattern matching in, 272
patterns, 244
self-loops in, 246
social, 244
sparse, 246
two-mode, 246
undirected, 262
weakness of, 262
neural networks (NN), 75
Ng, Vincent, 118
ngrams, 156
construction of, 157
featureset declaration, 163
parent, 161
restriction clause in, 163
shrinkage, 163
statistics for, 159
token generation by, 159, 161
NIH. See National Institute of Health
9/11 hijacker example, 246
NLM. See National Library of Medicine
NLP. See natural language processing
NN. See neural networks
nodes
circle graph, 294
concept hierarchy, 20
extracted literature as, 190
internal, 23, 180
radiating, 228
relatedness of, 228
sibling, 22
tree structure of, 182, 195
vertices as, 33
nominals, predicate, 110–111
noun groups, 107–108
noun phrases, 97, 113, 114, 115. See also generic noun phrases; phrases, coreferring; pronoun resolution engine; proper noun phrases
definite, 117
demonstrative, 117
indefinite, 117
NPs
base, 154
chunking of, 154
Nymble, 149–152
experimental evaluation of, 152
HMM topology of, 150
tokenization and, 150
object tree. See O-Tree
objects
hierarchical structure among, 123
structured, 95
OLDMEDLINE, 12
one-anaphora, 111
online categorization, 67
ontologies, 8, 42, 43
commercial, 45
creation, 246–250, 277
DAG, 197
domain, 42–43, 44, 51
external, 100
Gene Ontology Consortium, 277
hierarchical forms generated by, 46
open-ended architecture, 116
operations
browsing-support, 204
link, 204–205
preprocessing, 204–205
presentation, 205
search, 204
optimization, 6
algorithm, 248
clustering, 85
problems, 84, 139
orderings, partial, 203
ordinal anaphora, 111
orthographic features, 100
O-Tree(s), 123
algorithm, 124–125
documents structured as, 125
output concepts, 156
overabundance
pattern, 9, 189
problem of, 9, 179
overlapped matches, 101
OWL, 277
pairs
tag–tag, 153
tag–word, 153
pajek
block modeling of, 270
scope of, 273
shrinking option of, 260
Web site, 273
Palka system, 166
paradigm, input–output, 13
parameter
loss ratio, 74
maximum likelihood, 91
search, 180
parameterization, 11, 178
parse tree, 137
parsing
problem, 138
shallow, 61, 107–108, 154–155, 167
syntactic, 59, 60–61
XML, 116
partial orderings, 203
partitioning, 259–272
part-of-speech (POS) tagging, 59, 60, 113, 114, 156, 285. See also Brown Corpus tag set; lemmas
categories of, 60
conditional random fields with, 153–154
external, 163
HMM-based, 156
part–whole coreference, 112
patent(s)
analysis, 297, 300
documents, 306
managers, 309
search, 276, 297
strategy, 297
trends in issued, 305–309
Patent Researcher, 297
application usage scenarios, 302
architecture/functionality of, 298–302
bundled terms, 306
constraints supported by, 300–301
core text mining operations and, 300–301
data for, 299
DIAL language and, 299
GeneWays comparison with, 309
GUI of, 301–302
implementation of, 298, 299
knowledge discovery support by, 301
preprocessing operations, 299–300
presentation layer, 301–302
queries, 301
refinement constraints, 300–301
Taxonomy Chooser interface, 299
trend analysis capabilities of, 305
visualization tools, 301–302, 303
path(s), 245, 250–251
pattern(s)
browsing, 14
collocation, 115
concept, 10
concept occurrence, 19
DIAL language text, 317–318
discovery, 5
distribution-based, 29, 32, 303
Document Explorer search, 237
elements, 323–327
interesting, 29–30
knowledge discovery, 14
matching, 272, 322–323
network, 244
operators, 323
overabundance, 9, 189
RlogF, 173
search for, 8–10
sequential, 41
text, 317–318, 323
text mining, 1, 19
textual data, 40
unsuspected, 191
user’s knowledge of, 36
PCFGs, disambiguation ability of, 156
PDF files, 3
percentage thresholds, 38, 39
perceptual grouping, 59, 123–124
performance measures, 79
Perl script, 164
PersonAffiliation relation, 162–163
Phillips, W., 171
phrases, coreferring, 109. See also generic noun phrases; noun phrases; proper noun phrases
PieSky software, 232
plan, hyperbolic non-Euclidean, 218
pleonastic, 110
PNP. See proper noun phrases
polysemy, 45, 69
POS tags. See part-of-speech tagging
power centrality, 256–257
predicate(s)
nominals, 110–111
unary/binary, 16
preference
collocation pattern, 115
domain terminology, 116
section heading, 115
prefix
lengthening of, 149
splitting of, 149
preprocessing methodologies, xi, 2, 57
algorithms, 57
architecture, 58
categorizing, 57
GeneWays’, 310–312
Patent Researcher’s, 299–300
task oriented, 13, 57
varieties of, 57
presentation layer, 185–186
components, 14
elements of, 1, 14
importance of, 10–11
Industry Analyzer, 287–290
interface, 186
Patent Researcher, 301–302
text mining system’s, 10
utilities, 193
presentation operations, 205
prestige, types of, 251
Princeton University, 43
priors
Gaussian, 71
Laplace, 71, 72
probabilistic classifiers, 71, 78
probabilistic extraction algorithm, 121
probabilistic generative process, 131
probabilistic models, 131
probability
conditional, 71, 142, 143
context-dependent, 149, 152
emission, 132, 150
marginal, 71
transition, 132, 141, 150, 151
problem(s)
bootstrapping, 172
categorization, 82
clustering, 84–85
CRF, 143
data sparseness, 136–137, 148
definition, 122–123
document sorting, 65
HMM’s, 132
label bias, 142
optimization, 84, 139
overabundance, 9, 179
parsing, 138
sets, 194
tasks dependent on, 58, 59, 61–62
TC, 69
text categorization, 69, 79
unsolved, 58
procedure
C4.5, 73
CART, 73
forward-backward, 132–133
ID3, 73
process, probabilistic generative, 131
processing
linguistic, 285
random, 131
themes related to, 131
profiles, user, 11
pronominal anaphora, 110
pronominal resolution, 112
pronoun resolution engine, 113
proper names, 97
coreference, 110
identification, 106–107
proper noun phrases (PNP), 171, 172. See also generic noun phrases; noun phrases; phrases, coreferring; pronoun resolution engine
proportional thresholding, 67
proportions, 19
concept, 22, 29
interestingness and, 29–30
protocols
RDF, 194
XML-oriented, 194
prototyping, 208
proximity, 191
pruning, 73, 178
PubMed, 2, 11, 277
scope of, 2
Web site, 12
pull-down boxes, 278, 280
quality constraints, 186
query
association-discovery, 45, 46
canned, 280
choosing entities for, 277
clustering and, 83
constraints, 280
construction of, 280
distribution-type, 206, 294
engines, 16
expressions, 45
GUI driven, 286
Industry Analyzer event-type, 292
interpreters, 10
KDTL, 52–54, 55, 237
languages, 10, 14, 51–52, 177
lists of, 278
parameterization of, 11, 178
Patent Researcher, 301
preset, 276, 278
proportion-type, 206
result sets and, 45
support for, 179
tables, 23
templates for, 280
trend analysis, 306
user’s, 13
query languages, 10, 14
accessing, 177, 186–187
FACT’s, 46
interfaces, 10
parameterization, 178
text mining, 51–52
RDF protocols, 194
redundancy
constraints, 186
filters, 203
Reed Elsevier company, 277
reference, immediate, 115
referential distance, 116
refinement
constraints, 11, 14, 19–41, 191, 286–287, 300–301
techniques, 14–17, 186
regression methods, 74
Reingold, E., 232, 248. See also FR method
reiteration, lexical, 115
relatedness
node, 228
semantic, 69
relationship(s)
building, 108
categorization by, 45
context, 32, 33
co-occurrence, 12
data, 2
extraction, 156, 164–166
meaningful, content-bearing, 94
meronymy/holonymy, 112
PersonAffiliation, 162–163
rule attributes, 42
tagged, 164
temporal context, 35
term, 277
relativity, 191
representations
2-D, 220
bag-of-words document, 89
binary document, 73
character’s, 5
concept-level, 7
document’s, 4, 5, 6, 7, 58, 68
term-based, 6, 7
word-level, 6
research
deviation detection, 32
enhancing speed/efficiency of, 2
life sciences,
text mining and patent, 275
resolution. See also anaphora resolution; coreference, resolution
coreference, 109, 112
pronominal, 112
result sets, 45
retrieval
cluster-based, 84
document, 179
information, 1, 2, 62, 82
Reuters newswire, 4, 31, 70
RHS. See right-hand side
right brain stimulation, 191
right-hand side (RHS), 26, 45, 201
Riloff, Ellen, 166, 168, 169, 171
Ripper algorithm, 74, 300
RlogF pattern, 173
Rocchio classifier, 74–75
ROLE
relation, 165
rules, 165–166
rule(s)
associations, 24, 25, 27, 182
averaging, 267
constraints, 327–328
DNF, 73
learners, 74
maximal association, 27, 40
ROLE, 165–166
tagging, 120
TEG syntax, 156
Rzhetzky, A., 312
salience algorithm, 114
Sarkar, M., 229
scaling. See generalized iterative scaling
scanner properties, 320, 327
scatter/gather browsing method, 83
scattering, 83
scenario templates (STs), 99
SCFG. See stochastic context-free grammars
schemes
classification, 131
TF-IDF, 68
weighting, 68
search. See also Google
algorithms, 36, 178, 238
association rules, 36
brute force, 9
constraints, 178, 204
DIAL language, 321
Document Explorer patterns of, 237
engines, 82, 200
expanding parameters of, 180
fuzzy, 184
improving, 82–83
Industry Analyzer, 293
leveraging knowledge from previous, 36
operations, 204
parameters, 180
patent, 276, 297
for patterns, 8–10
precision, 83
task conditioning, 178
for trends, 8–10
selection. See feature, selection
self-organizing maps (SOMs). See also WEBSOM
algorithm generation of, 214, 217–218
multiple-lattice, 220
self-loops, 246
semantic features, 100
semantic lexicon, 169, 170
semantic relatedness, 69
sentences
concept, 321
linguistic analysis of, 109
sequential patterns, 30, 41
sets. See also concept sets
answer, 1, 23
association rules involving, 201
Brown Corpus tag, 60
frequent and near frequent, 19, 25
frequent concept, 9, 23–24
identical, 111
POS tag, 60
result, 45
σ-cover, 24
test, 67, 100
test document, 79
training, 68, 79, 100, 118
validation, 68, 75
shallow parsing, 61, 107–108, 154–155, 167
shrinkage, 136
defined, 136
hierarchies, 148
ngram, 163
technique, 148, 161
shuffling algorithms, 85
sibling node, 22
similarity
cosine, 90, 201, 202
function, 84, 201–202
measures, 85
simple concept association graphs, 201–202
simple concept graphs, 195–206, 241, 288, 296
simulated annealing, 249
single-label categorization, 67
singleton
σ-covers, 24
vertex, 199
singular value decomposition (SVD), 89–90, 91
smoothing, 136
social networks, 244
soft mark-up language, 3
software
browsing, 177
corporate intelligence, 275
Insight, 218
libraries, 208
link analysis, 273–274
NetMiner, 274
PieSky, 232
protein interaction analysis, 275
search engine, 200
StarTree Studio, 218
SOMs. See self-organizing maps
Soon’s string match feature (SOON STR), 118
sparse network, 246
sparseness, 72
data, 136–137, 148
training data, 136–137
sparsity. See feature, sparsity
spring embedding, 232.
See also networks
algorithms, 247
network graphs, 232
StarTree Studio software, 218
states
background, 149
HMM’s classes of, 147
stimulation, right brain, 191
stochastic context-free grammars (SCFG), 131, 137
defined, 138
information extraction and, 155–166
using, 137–138
stop words, 68
strategy
divide-and-conquer, 58
patent, 297
string, 138
constants, 324
strong components, 262
structural equivalence, 263
structural mapping, 125–127
structured objects, 95
STs. See scenario templates
sublanguages, 138
subtasks, 123–124
suffix
lengthening of, 149
splitting of, 149
sum-of-squares, 29
supervised learning, 70
support, 24, 25, 251
query, 179
thresholds, 181
vectors, 76
support vector machines (SVM), 76–77, 78 76–77, 78
SVD. See singular value decomposition
SVM. See support vector machines
Swiss-Prot database, 310
symbolic classifiers, 72
symbols, terminal, 156
SYNDICATE system, 18
Synonym/Homonym resolver, 311
synonymy, 45, 69
syntactic analysis, 105
syntactic heuristics, 171, 172
syntactic parsing, 59, 60–61
syntactical constraints, 186
syntax, TEG rulebook, 156
system(s). See also AutoSlog-TS system; CONSTRUE system; Explora system; GENomics Information Extraction System; hybrid system; MedLEE medical NLP system; Palka system; SYNDICATE system; text mining systems; TEXTRISE system
architects, 17
architecture, 47, 186
thresholds defined by, 230
table(s)
of contents, 83
joins, 1
query, 23
tagging. See also part-of-speech tagging
chunk, 155
documents, 94
MUC style, 100
POS, 285
rules, 120
tag–tag pairs, 153
tag–word pairs, 153
target string
lengthening of, 149
splitting of, 149
task(s). See also coreference task; subtasks; template element tasks; template relationship task; visual information extraction task
AI, 64
algorithms, 58
clustering, 82–84
documents structured by, 57
entity extraction, 150, 156
NE, 96
preprocessing by, 13, 57
problem dependent, 58, 59, 61–62
search, 178
text categorization, 66
TR, 99
taxonomies, 8, 42, 180
classic, 185
concept, 195
editors, 183–184
maintaining, 183
roles of, 182
Taxonomy Chooser interface, 299
TC. See text categorization
TEG. See trainable extraction grammar
template element (TE) tasks, 98
template relationship (TR) task, 99
templates, 123, 127–128, 280
temporal context graphs, 30, 32, 35
temporal context relationships, 32, 35
temporal selection, 35
term(s), 5–6, 8
candidate, 6
clustering, 69
extraction, 6, 12, 95, 285
hierarchy editor, 239
lemmatized, 6
Patent Researcher’s bundled, 306
relationships, 277
tokenized, 6
terminal symbols, 156
term-level representations, 7
termlists, 156
TEs. See template element tasks
test sets, 67, 100
text(s)
classifiers, 76, 79–80
clustering, xi, 89, 91–92
comprehension, 95
elements extracted from, 96
extraction, 96
filtering, 65–66
fragments, 109
natural language, 1
pattern, 317–318, 323
tokenization, 320
text analysis, 146–152
clustering tasks in, 82–84
CRF’s application to, 153–155
text categorization (TC), 58, 61–62, 64
applications, 64, 65–66
approaches to, 64
automated, 64
experiments, 79
knowledge engineering approach to, 70
machine learning, 70–78
NN and, 75
problem, 69, 79
stop words removed from, 68
task, 66
text mining. See also preprocessing methodologies
algorithms, 5, 8
analysis tools for, 1
applications, xi, 8
background knowledge and, 8
biological pathways,
corporate finance and, 275
data mining, v, 1, 11
defined, x, 1, 13
essential task of, 4
goals of, 5
GUIs for, 177
human-centric, 189
IE and, 11
input–output paradigm for, 13
inspiration/direction of, 1
introductions to, 11
KDD applications, 13
life sciences and, 275
methodologies, 9
patent research and, 275
pattern overabundance limitation, 9
pattern-discovery algorithms, 1, 5
patterns, 19
preprocessing operations, 1, 2, 4, 7, 8, 13–14, 57
presentation layer elements, 1, 14
query languages for, 51–52
techniques exploited by, 1
visualization tools, 1, 194
text mining applications, 8
corporate finance-oriented, 286
Document Explorer, 18
Explora system, 18
FACT, 18
GUIs of, 177
horizontal, 309
KDT, 18
LINDI project, 18
SYNDICATE system, 18
TEXTRISE system, 18
text mining systems. See also core text mining operations
abstract level of, 13
architecture of, 13–18
background knowledge and, 8, 16, 42, 45
baseline distribution for, 22
concept proportions and, 29
content based browsing with, 10
customized profiles with, 11
designers of, 222, 277
distributions and, 29
domain specific data sources, 16
early, 30
empowering users of, 10
front-ends of, 10, 11
graphical elements of, 11
hypothetical, 19
incremental update schemes for, 38
practical approach of, 30
presentation layer of, 10
query engines of, 16
refinement constraints, 11
refinement techniques, 14–17
state-of-the-art, 10, 194
TEXTRISE system, 18
textual data, 88–92, 189, 195
TF-IDF schemes, 68
thematic hierarchical thesaurus, 65
themes, processing, 131
thesaurus
MESH, 65
names, 325
NASA aerospace, 65
thematic hierarchical, 65
three dimensional (3-D) effects, 220–222
See also representations, 2-D
algorithms, 220
challenges of, 221
disadvantages of, 222
impact of, 222
opportunities offered by, 221
thresholding
fixed, 67
proportional, 67
thresholds
confidence, 181
data, 39
minconf, 26, 40
minsup, 26, 40
percentage, 38, 39
support, 181
system-defined, 230
user-defined, 230
time-based analysis, 30
Tipster, 96–101
Title Browser, 303
token(s)
elements, 327
features of, 150, 161
ngram generation of, 159, 161
_UNK_, 152
unknown, 152, 161
tokenization, 59, 60, 104, 106, 107
DIAL language text, 320
linguistic processing and, 285
Nymble and, 150
tokenizer, external, 161
tools
analysis, 1
browsing, 181
clustering, 11, 184–185
Document Explorer visualization, 237
editing, 184
graphical, 189
hybrid, 222–225
hyperbolic tree, 218
line graphs as prototyping, 208
prototyping, 208
visualization, 1, 10, 14, 192, 194, 227–228, 294 1, 10, 14, 192, 194, 227–228, 294
TR. See template relationship task
trainable extraction grammar (TEG), 155, 156
accuracy of, 165
experimental evaluation of, 164
extractor, 164
grammar, 157, 158
as hybrid system, 156
rulebook syntax, 156
training, 158–161
training
classifiers, 79
CRF’s, 144
examples, 117
HMM, 135–136
MEMM, 141
sets, 68, 79, 100, 118
TEG, 158–161
transmission
emission cycle, 131
matrix, 143
tree(s). See also minimal spanning tree
binary, 73
browsing, 15
hierarchical, 42, 195
hyperbolic, 218–220
node structure of, 182
parse, 137
pruning, 73, 178
trend(s)
analysis, 9, 30–31, 41, 301, 305
graphs, 30, 32, 35, 242
patent, 305–309
search for, 8–10
trigger-constraint functions, 153
trigrams, 5
tuple dimension, 5
two-mode network, 246
UCINET, 273–274
UMLS Metathaurus. See Unified Medical Language System Metathesaurus
unary predicates, 16
undirected networks, 262
Unified Medical Language System (UMLS) Metathesaurus, 284
uniformity, 152
United States Patent and Trademark Office, 299, 305
unknown tokens, 152, 161
_UNK_ tokens, 152
user(s)
browsing by, 10, 13
clustering guided by, 83
customizing profiles of, 11
empowering, 10
groups, 194
interactivity of, 179, 189
M-support and, 28
pattern knowledge of, 36
querying by, 13
thresholds defined by, 230
values identified by, 26
user-identified values
minconf, 26
minsup, 26
utilities, 193
validation sets, 68, 75
variable
backward, 133
forward, 132, 141
vector(s)
feature, 68
formats, 7
global feature, 142
original document, 90
space model, 85
support, 76
weight, 142
verbs, indicating, 115
vertices, 33, 260, 266
VIE task. See visual information extraction task
visual information extraction (VIE) task, 122, 123, 128
visual techniques, 194–226
visualization. See also circle graphs
3-D, 220
approaches, 189, 191, 281
assigning colors to, 281
capabilities, 276
circle graph, 294
DAG techniques of, 199
data, 218
Document Explorer tools for, 237, 241
hyperbolic tree, 218
interface, 191
link analysis and, 226, 237
Patent Researcher’s tools for, 301–302, 303
specialized approach to, 226
tools, 1, 10, 14, 192, 194, 227–228, 294
user interactivity and, 189
Viterbi algorithm, 133–134, 138, 141
vocabulary, controlled, 65, 277
walk, 245
Washington Post Web site, 246
weak components, 262
weak hypothesis, 77
weak learner, 77
Web pages
hierarchical categorization of, 66
HTML and, 3
hypertextual nature of, 66
Web site(s)
FBI, 246
Kevin Bacon game, 250
pajek, 273
PubMed, 12
UCINET, 273–274
U.S. Patent and Trademark Office, 299, 305
Washington Post, 246
WEBSOM, 214–216. See also self-organizing maps
advantages of, 216
zoomable interface of, 216
weight vector, 142
weighted linear combination, 77
weights
binary, 68
giving, 68
WHISK algorithm, 119
word stems, 4
wordclass names, 324–325
word-level representations, 6
WordNet, 43, 44, 50, 51, 112
word-processing files, 3
words, 5–6, 8
identifying single, 6
POS tag categorization of, 60
scanning, 106
stop, 68
syntactic role of, 58
synthetic features v. naturally occurring, 69
workbench, evaluation, 116
WYSIWYG HTML editor, 3
Xerox PARC, 218
XML
parsing, 116
protocol, 194
Yang, Y., 76
Zhou, M., 197, 199
zoning module. See tokenization
zoomability, 191
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