Proceedings of the BioNLP Workshop on Linking Natural Language Processing and Biology at HLT-NAACL 06, pages 126–133,
New York City, June 2006. c©2006 Association for Computational Linguistics
The Difficulties of Taxonomic Name Extraction and a Solution 
Guido Sautter Klemens Böhm 
Dept. of Computer Science 
Universität Karlsruhe (TH) 
Germany 
sautter@ipd.uka.de boehm@ipd.uka.de 
 
Abstract 
In modern biology, digitization of biosys-
tematics publications is an important task. 
Extraction of taxonomic names from such 
documents is one of its major issues. This 
is because these names identify the various 
genera and species. This article reports on 
our experiences with learning techniques 
for this particular task. We say why estab-
lished Named-Entity Recognition tech-
niques are somewhat difficult to use in our 
context. One reason is that we have only 
very little training data available. Our ex-
periments show that a combining approach 
that relies on regular expressions, heuris-
tics, and word-level language recognition 
achieves very high precision and recall and 
allows to cope with those difficulties. 
1 Introduction 
Digitization of biosystematics publications cur-
rently is a major issue. They contain the names 
and descriptions of taxonomic genera and species. 
The names are important because they identify the 
various genera and species. They also position the 
species in the tree of life, which in turn is useful 
for a broad variety of biology tasks. Hence, rec-
ognition of taxonomic names is relevant. How-
ever, manual extraction of these names is time-
consuming and expensive. 
The main problem for the automated recognition 
of these names is to distinguish them from the 
surrounding text, including other Named Entities 
(NE). Named Entity Recognition (NER) currently 
is a big research issue. However, conventional 
NER techniques are not readily applicable here 
for two reasons: First, the NE categories are rather 
high-level, e.g., names of organizations or persons 
(cf. common NER benchmarks such as (Carreras 
2005)). Such a classification is too coarse for our 
context. The structure of taxonomic names varies 
widely and can be complex. Second, those recog-
nizers require large bodies of training data. Since 
digitization of biosystematics documents has 
started only recently, such data is not yet available 
in biosystematics. On the other hand, it is impor-
tant to demonstrate right away that text-learning 
technology is of help to biosystematics as well. 
This paper reports on our experiences with learn-
ing techniques for the automated extraction of 
taxonomic names from documents. The various 
techniques are obviously useful in this context: 
• Language recognition – taxonomic names are 
a combination of Latin or Latinized words, 
with surrounding text written in English, 
• structure recognition – taxonomic names fol-
low a certain structure, 
• lexica support – certain words never are/may 
well be part of taxonomic names. 
On the other hand, an individual technique in iso-
lation is not sufficient for taxonomic name extrac-
tion. Mikheev (1999) has shown that a combining 
approach, i.e., one that integrates the results of 
several different techniques, is superior to the in-
dividual techniques for common NER. Combin-
ing approaches are also promising for taxonomic 
name extraction. Having said this, the article will 
now proceed as follows: 
First, we have conducted a thorough inspection of 
taxonomic names. An important observation is 
that one cannot model taxonomic names both 
concisely and precisely using regular expressions. 
As is done in bootstrapping, we use two kinds of 
regular expressions: precision rules, whose in-
stances are taxonomic names with very high 
probability, and recall rules, whose instances are 
a superset of all taxonomic names. We propose a 
meaningful definition of precision rules and recall 
rules for taxonomic names. 
126
Second, the essence of a combining approach is to 
arrange the individual specific approaches in the 
right order. We propose such a composition for 
taxonomic name extraction, and we say why it is 
superior to other compositions that may appear 
feasible as well at first sight. 
Finally, to quantify the impact of the various al-
ternatives described so far, we report on experi-
mental results. The evaluation is based on a cor-
pus of biosystematics documents marked up by 
hand. The best solution achieves about 99.2% in 
precision and recall. It prompts the user for only 
0.2% of the words.  
The remainder of the paper is as follows: Sec-
tion 2 discusses related approaches. Section 3 in-
troduces some preliminaries. Section 4 describes 
one specific combining approach in some detail. 
Section 5 features an evaluation. Section 6 con-
cludes. 
2 Related Work 
This section reviews solutions to problems related 
to the extraction of taxonomic names. 
2.1 Named Entity Recognition 
Taxonomic names are a special case of named 
entity. In the recent past, NER has received much 
attention, which yielded a variety of methods. The 
most common ones are list lookups, grammars, 
rules, and statistical methods like SVMs (Bikel 
1997). All these techniques have been developed 
for tasks like the one presented by Carreras 
(2005). Thus, their focus is the recognition of 
somewhat common NE like locations and per-
sons. Consequently, they are not feasible for the 
complex and variable structure of taxonomic 
names (see Section 3.3). Another problem of 
common NER techniques is that they usually re-
quire several hundred thousand words of pre-
annotated training data. 
2.2 List-based Techniques 
List-based NER techniques (Palmer 1997) make 
use of lists to determine whether a word is a NE 
of the category sought. The sole use of a thesaurus 
as a positive list is not an option for taxonomic 
names. All existing thesauri are incomplete. Nev-
ertheless, such a list allows recognizing known 
parts of taxonomic names. 
The inverse approach would be list-based exclu-
sion, using a common English dictionary. Koning 
(2005) combines such an approach with structural 
rules. In isolation, however, it is not an option 
either. First, it would not exclude proper names 
reliably. Second, it excludes parts of taxonomic 
names that are also used in common English. 
However, exclusion of sure negatives, i.e., words 
that are never part of taxonomic names, simplifies 
the classification. 
2.3 Rule Based Techniques 
Rule based techniques do not require pre-
annotated training data. They extract words or 
word sequences based on their structure. Yoshida 
(1999) applies regular expressions to extract the 
names of proteins. He makes use of the syntax of 
protein names like NG-monomethyl-L-arginine, 
which is very distinctive. 
There are also rules for the syntax of taxonomic 
names, but they are less restrictive. For instance, 
Prenolepis (Nylanderia) vividula Erin subsp. gua-
temalensis Forel var. itinerans Forel is a taxo-
nomic name as well as Dolichoderus decollatus. 
Because of the wide range of optional parts, it is 
impossible to find a regular expression that 
matches all taxonomic names and at the same 
time provides satisfactory precision. Koning 
(2005) presents an approach based on regular ex-
pressions and static dictionaries. This technique 
performs satisfactorily compared to common 
NER approaches, but their conception of what is a 
positive is restricted. For instance, they leave 
aside taxonomic names that do not specify a ge-
nus. However, the idea of rule-based filters for the 
phrases of documents is helpful. 
2.4 Bootstrapping 
Instead of a large amount of labeled training data, 
Bootstrapping uses some labeled examples 
(“seeds”) and an even larger amount of unlabeled 
data for the training. Jones (1999) has shown that 
this approach performs equal to techniques requir-
ing labeled training data. However, Bootstrapping 
is not readily applicable to our particular problem. 
Niu (2003) used an unlabeled corpus of 
88.000.000 words for training a named entity rec-
ognizer. For our purpose, even unlabeled training 
data is not available in this order of magnitude, at 
least right now. 
127
2.5 Active Learning 
According to Day (1997), the original idea of Ac-
tive Learning was to speed up the creation of 
large labeled training corpora from unlabeled 
documents. The system uses all of its knowledge 
during all phases of the learning. Thus, it labels 
most of the data items automatically and requires 
user interaction only in rare cases. In order to in-
crease data quality, we include user-interaction in 
our taxonomic name extractor as well. 
2.6 Gene and Protein Name Extraction 
In the recent past, the major focus of biomedical 
NER has been the recognition of gene and protein 
names. Tanabe (2002) gives a good overview of 
various approaches to this task. Frequently used 
techniques are structural rules, dictionary lookups 
and Hidden Markov Models. Most of the ap-
proaches use the output of a part-of-speech tagger 
as additional evidence. Both gene and protein 
names differ from taxonomic names in that the 
nomenclature rules for them are by far stricter. 
For instance, they never include the names of the 
discoverer / author of a given part. In addition, 
there are parts which are easily distinguished from 
the surrounding text based on their structure, 
which is not true for taxonomic names. Conse-
quently, the techniques for gene or protein name 
recognition are not feasible for the extraction of 
taxonomic names. 
3 Preliminaries 
This section introduces some preliminaries re-
garding word-level language recognition. We also 
describe a measure to quantify the user effort in-
duced by interactions. 
3.1 Measure for User Effort 
In NLP, the f-Measure is popular to quantify the 
performance of a word classifier: 
P(P) := positives classified as positive 
N(P) := positives classified as negative 
P(N) := negatives classified as positive 
N(N) := negatives classified as negative 
P(N)P(P)
P(P)
:p ecisionPr
+
=
   
N(P)  P(P)
P(P)
:r callRe
+
=
 
rp
rp2
:fMeasure
+
××
=
 
But components that use active learning have 
three possible outputs. If the decision between 
positive or negative is narrow, they may classify a 
word as uncertain and prompt the user. This pre-
vents misclassifications, but induces intellectual 
effort. To quantify this effort as well, there are 
two further measures: 
U(P) := positives not classified (uncertain) 
U(N) := negatives not classified (uncertain) 
Given this, Coverage C is defined as the fraction 
of all classifications that are not uncertain: 
)N(U)N(N)N(P)P(U)P(N)P(P
)N(N)N(P)P(N)P(P
:C
+++++
+++
=
 
To obtain a single measure for overall classifica-
tion quality, we multiply f-Measure and coverage 
and define Quality Q as 
CfMeasure:Q ×=
 
3.2 Word-Level Language Recognition 
for Taxonomic Name Extraction 
In earlier work (Sautter 2006), we have presented 
a technique to classify words as parts of taxo-
nomic names or as common English, respectively. 
It is based on two statistics containing the N-
Gram distribution of taxonomic names and of 
common English. Both statistics are built from 
examples from the respective languages. It uses 
active learning to deal with the lack of training 
data. Precision and recall reach a level of 98%. 
This is satisfactory, compared to common NER 
components. At the same time, the user has to 
classify about 3% of the words manually. In a text 
of 10.000 words, this would be 300 manual classi-
fications. We deem this relatively high. 
3.3 Formal Structure of Taxonomic Names 
The structure of taxonomic names is defined by 
the rules of Linnaean nomenclature (Ereshefsky 
1997). They are not very restrictive and include 
many optional parts. For instance, both Prenole-
pis (Nylanderia) vividula Erin subsp. guatemalen-
sis Forel var. itinerans Forel and Dolichoderus 
decollatus are taxonomic names. There are only 
two mandatory parts in such a name: the genus 
and the species. Table 1 shows the decomposition 
of the two examples. The parts with their names 
in brackets are optional. More formally, the rules 
of Linnaean nomenclature define the structure of 
taxonomic names as follows: 
• The genus is mandatory. It is a capitalized 
word, often abbreviated by its first one or two 
letters, followed by a dot. 
128
• The subgenus is optional. It is a capitalized 
word, often enclosed in brackets. 
• The species is mandatory. It is a lower case 
word. It is often followed by the name of the 
scientist who first described the species. 
• The subspecies is optional. It is a lower case 
word, often preceded by subsp. or subspecies 
as an indicator. It is often followed by the 
name of the scientist who first described it. 
• The variety is optional. It is a lower case 
word, preceded by var. or variety as an indi-
cator. It is often followed by the name of the 
scientist who first described it. 
Part   
Genus Prenolepis Dolichoderus 
(Subgenus) (Nylanderia)  
Species vividula decollatus 
(Discoverer) Erin  
(Subspecies) subsp. guatemalensis  
(Discoverer) Forel  
(Variety) var. itinerans  
(Discoverer) Forel  
Table 1: The parts of taxonomic names 
4 Combining Techniques  
for Taxonomic Name Extraction 
Due to its capability of learning at runtime, the 
word-level language recognizer needs little train-
ing data, but it still does. In addition, the manual 
effort induced by uncertain classifications is high. 
Making use of the typical structure of taxonomic 
names, we can improve both aspects. First, we 
can use syntax-based rules to harvest training data 
directly from the documents. Second, we can use 
these rules to reduce the number of words the 
classifier has to deal with. However, it is not pos-
sible to find rules that extract taxonomic names 
with both high precision and recall, as we will 
show later. But we have found rules that fulfill 
one of these requirements very well. In what fol-
lows, we refer to these as precision rules and re-
call rules, respectively. 
4.1 The Classification Process 
1. We apply the precision rules. Every word 
sequence from the document that matches 
such a rule is a sure positive. 
2. We apply the recall rules to the phrases that 
are not sure positives. A phrase not matching 
one of these rules is a sure negative. 
3. We make use of domain-specific vocabulary 
and filter out word sequences containing at 
least one known negative word. 
4. We collect a set of names from the set of sure 
positives (see Subsection 4.5). We then use 
these names to both include and exclude fur-
ther word sequences. 
5. We train the word-level language recognizer 
with the surely positive and surely negative 
words. We then apply it to the remaining un-
certain word sequences. 
Figure 1 visualizes the classification process. At 
first sight, other orders seem to be possible as 
well, e.g., the language recognizer classifies each 
word first, and then we apply the rules. But this is 
not feasible: It would require external training 
data. In addition, the language recognizer would 
have to classify all the words of the document. 
This would incur more manual classifications. 
 
Figure 1: The Classification Process 
This approach is similar to the bootstrapping algo-
rithm proposed by Jones (1999). The difference is 
that this process works solely with the document 
it actually processes. In particular, it does not 
need any external data or a training phase. Aver-
age biosystematics documents contain about 
15.000 words, which is less than 0.02% of the 
data used by Niu (2003). On the other hand, with 
the classification process proposed here, the accu-
racy of the underlying classifier has to be very 
high from the start. 
129
4.2 Structural Rules 
In order to make use of the structure of taxonomic 
names, we use rules that refer to this structure. 
We use regular expressions for the formal repre-
sentation of the rules. In this section, we develop 
a regular expression matching any word sequence 
that conforms to the Linnaean rules of nomencla-
ture (see 3.3). Table 2 provides some abbrevia-
tions, to increase readability. We model taxo-
nomic names as follows: 
_ one white space character 
<LcW> [a-z]
(3,)
 
<CapW> [A-Z][a-z]
(2,)
 
<CapA> [A-Z]{[a-z]}?. 
<Name> {<CapA>_}
(0,2)
<CapW> 
Table 2: Abbreviations 
• The genus is a capitalized word, often abbre-
viated. We denote it as <genus>, which 
stands for {<CapW>|<CapA>}. 
• The subgenus is a capitalized word, option-
ally surrounded by brackets. We denote it as 
<subGenus>, which stands for 
<CapW>|(<CapW>). 
• The species is a lower case word, optionally 
followed by a name. We denote it as 
<species>, which stands for 
<LcW>{_<Name>}?. 
• The subspecies is a lower case word, pre-
ceded by the indicator subsp. or subspecies, 
and optionally followed by a name. We de-
note it as <subSpecies>, standing for 
{subsp.|subspecies}_<LcW>{_<Name>}?. 
• The variety is a lower case word, preceded by 
the indicator var. or variety, and optionally 
followed by a name. We denote it as 
<variety>, which stands for {var.| 
variety}_<LcW>{_<Name>}?. 
A taxonomic name is now modeled as follows. 
We refer to the pattern as <taxName>: 
 <genus>{_<subGenus>}? 
 _<species>{_<subSpecies>}? 
 {_<variety>}? 
4.3 Precision Rules 
Because <taxName> matches any sequence of 
words that conforms to the Linnaean rules, it is 
not very precise. The simplest match is a capital-
ized word followed by one in lower case. Any two 
words at the beginning of a sentence are a match! 
To obtain more precise regular expressions, we 
rely on the optional parts of taxonomic names. In 
particular, we classify a sequence of words as a 
sure positive if it contains at least one of the op-
tional parts <subGenus>, <subSpecies> and 
<variety>. Even though these regular expres-
sions may produce false negatives, our evaluation 
will show that this happens very rarely. Our set of 
precise regular expressions has three elements: 
• <taxName> with subgenus in brackets, 
<subspecies> and <variety> optional:  
 <genus>_(<CapW>) 
 _<species>{_<subSpecies>}? 
 {_<variety>}? 
• <taxName> with <subspecies> given, 
<subGenus> and <variety> optional:  
 <genus>{_<subGenus>}? 
 _<species>_<subSpecies> 
 {_<variety>}? 
• <taxName> with <variety> mandatory, 
<subGenus> and <subSpecies> optional:  
 <genus>{_<subGenus>}? 
 _<species>{_<subSpecies>}? 
 {_<variety>} 
To classify a word sequence as a sure positive if it 
matches at least one of these regular expressions, 
we combine them disjunctively and call the result 
<preciseTaxName>. 
A notion related to that of a sure positive is the 
one of a surely positive word. A surely positive 
word is a part of a taxonomic name that is not part 
of a scientist’s name. For instance, the taxonomic 
name Prenolepis (Nylanderia) vividula Erin 
subsp. guatemalensis Forel var. itinerans Forel 
contains the surely positive words Prenolepis, 
Nylanderia, vividula, guatemalensis, and itiner-
ans. We assume that surely positive words exclu-
sively appear as parts of taxonomic names. 
4.4 Recall Rules 
<taxName> matches any sequence of words that 
conforms to the Linnaean rules, but there is a fur-
ther issue: Enumerations of several species of the 
same genus tend to contain the genus only once. 
For instance, in Pseudomyrma arboris-sanctae 
Emery, latinoda Mayr and tachigalide Forel”we 
want to extract latinoda Mayr and tachigalide 
Forel as well. To address this, we make use of the 
surely positive words: We use them to extract 
parts of taxonomic names that lack the genus. 
130
Our technique also extracts the names of the sci-
entists from the sure positives and collects them 
in a name lexicon. Based on the structure de-
scribed in Section 3.3, a capitalized word in a sure 
positive is a name if it comes after the second po-
sition. From the sure positive Pseudomyrma 
(Minimyrma) arboris-sanctae Emery, the tech-
nique extracts Pseudomyrma, Minimyrma and 
arboris-sanctae. In addition, it would add Emery 
to the name lexicon. 
We cannot be sure that the list of sure positive 
words suffices to find all species names in an 
enumeration. Hence, our technique additionally 
collects all lower-case words followed by a word 
contained in the name lexicon. In the example, we 
extract latinoda Mayr and tachigalide Forel if 
Mayr and Forel are in the name lexicon. 
4.5 Data Rules 
Because we want to achieve close to 100% in re-
call, the recall rules are very weak. In conse-
quence, many word sequences that are not taxo-
nomic names are considered uncertain. Before the 
word-level language recognizer deals with them, 
we see some more ways to exclude negatives. 
Sure Negatives . As mentioned in Subsection 4.3, 
<taxName> matches any capitalized word fol-
lowed by a word in lower case. This includes the 
start of any sentence. Making use of the sure 
negatives, we can recognize these phrases. In par-
ticular, out technique classifies any word se-
quence as negative that contains a word which is 
also in the set of sure negatives. For instance, in 
sentence “Additional evidence results from …”, 
Additional evidence matches <taxName>. An-
other sentence contains an additional advantage, 
which does not match <taxName>. Thus, the set of 
sure negatives contains an, additional, and advan-
tage. Knowing that additional is a sure negative, 
we exclude the phrase Additional evidence. 
Names of Scientists. Though the names of sci-
entists are valid parts of taxonomic names, they 
also cause false matches. The reason is that they 
are capitalized. A misclassification occurs if they 
are matched with the genus or subgenus part – 
<taxName> cannot exclude this. In addition, they 
might appear elsewhere in the text without be-
longing to a taxonomic name. Similarly to sure 
negatives, we exclude a match of <taxName> if 
the first or second word is contained in the name 
lexicon. For instance, in “…, and Forel further 
concludes”, Forel further matches <taxName>. If 
the name lexicon contains Forel, we know that it 
is not a genus, and thus exclude Forel further. 
4.6 Classification of Remaining Words 
After applying the rules, some word sequences 
still remain uncertain. To deal with them, we use 
word-level language recognition. We train the 
classifier with the sure positive and sure negative 
words. We do not classify every word separately, 
but compute the classification score of all words 
of a sequence and then classify the sequence as a 
whole. This has several advantages: First, if one 
word of a sequence is uncertain, this does not 
automatically incur a feedback request. Second, if 
a word sequence is uncertain as a whole, the user 
gives feedback for the entire sequence. This re-
sults in several surely classified uncertain words 
at the cost of only one feedback request. In addi-
tion, it is easier to determine the meaning of a 
word sequence than the one of a single word. 
5 Evaluation 
A combining approach gives rise to many ques-
tions, e.g.: How does a word-level classifier per-
form with training data automatically generated? 
How does rule-based filtering affect precision, 
recall, and coverage? What is the effect to dy-
namic lexicons? Which kinds of errors remain? 
We run two series of experiments: We first proc-
ess individual documents. We then process the 
documents incrementally, i.e., we do neither clear 
the sets of known positives and negatives after 
each document, nor the statistics of the word-level 
language recognizer. This is to measure the bene-
fit of reusing data obtained from one document in 
the processing of subsequent ones. Finally, we 
take a closer look at the effects of the individual 
steps and heuristics from Section 4. 
The platform is implemented in JAVA 1.4.2. 
We use the java.util.regex package to repre-
sent the rules. All tests are based on 20 issues of 
the American Museum Novitates, a natural science 
periodical published by the American Museum of 
Natural History. The documents contain about 
260.000 words, including about 2.500 taxonomic 
names. The latter consist of about 8.400 words. 
131
5.1 Tests with Individual Documents 
First, we test the combined classifier with indi-
vidual documents. The Docs column in Table 3 
contains the results. The combination of rules and 
word-level classification provides very high pre-
cision and recall. The former is 99.7% on average, 
the latter 98.2%. The manual effort is very low: 
The average coverage is 99.7%. 
5.2 Tests with Entire Corpus 
In the first test the classifier did not transfer any 
experience from one document to later ones. We 
now process the documents one after another. The 
Corp column of Table 3 shows the results. As 
expected, the classifier performs better than with 
individual documents. The average recall is 
99.2%, coverage is 99.8% on average. Only preci-
sion is a little less, 99.1% on average. 
  Docs Corp 
<preciseTaxName> 22,6 
<taxName> 414,1 
SN excluded 78,5 
Names excluded 176,15 
Scorings 139,9 
User Feedbacks 19,6 10,35 
False positives 4,25 1,5 
False negatives 0,55 1,5 
Precision 0,997 0,991 
Recall 0,982 0,992 
f-Measure 0,990 0,992 
Coverage 0,997 0,998 
Quality 0,987 0,990 
Table 3: Test results 
The effect of the incremental learning is obvious. 
The false positives are less than half of those in 
the first test. A comparison of Line False 
Positives in Table 3 shows this. The same is 
true for the number feedback requests (Line User 
Feedbacks). The slight decrease in precision 
(Line False Negatives) results from the propa-
gation of misclassifications between documents. 
The reason for the improvement becomes clear 
for documents where the number of word se-
quences in <preciseTaxName> is low: experience 
from previous documents compensates the lack of 
positive examples. This reduces both false posi-
tives and manual classifications. 
5.3 The Data Rules 
The exclusion of word sequences containing a 
sure negative turns out to be effective to filter the 
matches of <taxName>. Lines <taxName> and SN 
excluded of Tables 3 show this. On average, this 
step excludes about 20% of the word sequences 
matching <taxName>. Lines <taxName> and Names 
excluded tell us that the rule based on the names 
of scientists is even more effective. On average, it 
excludes about 40% of the matches of <taxName>. 
Both data rules decrease the number of words the 
language recognizer has to deal with and eventu-
ally the manual effort. This is because they reduce 
the number of words classified uncertain. 
5.4 Comparison to Word-Level Classifier 
and TaxonGrab 
A word-level classifier (WLC) is the core compo-
nent of the combining technique. We compare it 
in standalone use to the combining technique 
(Comb) and to the TaxonGrab (T-Grab) approach 
(Koning 2005). See Table 4. The combining tech-
nique is superior to both TaxonGrab and stand-
alone word-level classification. The reason for 
better precision and recall is that it uses more dif-
ferent evidence. The better coverage results from 
the lower number of words that the word-level 
classifier has to deal with. On average, it has to 
classify only 2.5% of the words in a document. 
This reduces the classification effort, leading to 
less manual feedback. It also decreases the num-
ber of potential errors of the word-level classifier. 
All these positive effects result in about 99% f-
Measure and 99.7% coverage. This means the 
error is reduced by 75% compared to word-level 
classification, and by 80% compared to Taxon-
Grab. The manual effort decreases by 94% com-
pared to the standalone word-level classifier. 
 Precision Recall f-Measure Coverage 
T-Grab 96% 94% 95% - 
WLC 97% 95% 96% 95% 
Comb 99.1% 99.2% 99% 99.7% 
Table 4: Comparison to Related Approaches 
5.5 Misclassified Words 
Despite all improvements, there still are word se-
quences that are misclassified. 
False Negatives. The regular expressions in 
<preciseTaxName> are intended to be 100% pre-
cise. There are, however, some (rare) exceptions. 
Consider the following phrase: “… In Guadeloup 
(Mexico) another subspecies killed F. Smith.” 
Except for the word In, this sentence matches the 
132
regular expression from <preciseTaxName> 
where <subSpecies> is mandatory. Similar 
pathologic cases could occur for the variety part. 
Another class of false negatives contains two 
word sequences, and the first one is the name of a 
genus. For instance, “Xenomyrmex varies …” falls 
into this category. The classifier (correctly) rec-
ognizes the first word as a part of a taxonomic 
name. The second one is not typical enough to 
change the overall classification of the sequence. 
To recognize these false negatives, one might use 
POS-tagging. We could exclude word sequences 
containing words whose meaning does not fit into 
a taxonomic name. 
False Positives. Though <taxName> matches any 
taxonomic name, the subsequent exclusion 
mechanisms may misclassify a sequence of 
words. In particular, the word-level classifier has 
problems recognizing taxonomic names contain-
ing proper names of persons. The problem is that 
these words consist of N-Grams that are typical 
for common English. “Wheeleria rogersi Smith”, 
for instance, is a fictitious but valid taxonomic 
name. A solution to this problem might be to use 
the scientist names for constructing and recogniz-
ing the genus and species names derived from 
them. 
6 Conclusions  
This paper has reported on our experiences with 
the automatic extraction of taxonomic names from 
English text documents. This task is essential for 
modern biology. A peculiarity of taxonomic name 
extraction is a shortage of training data. This is 
one reason why deployment of established NER 
techniques has turned out to be infeasible, at least 
without adaptations. A taxonomic-name extractor 
must circumvent that shortage. Our experience 
has been that designing regular expressions that 
generate training data directly from the documents 
is feasible in the context of taxonomic name ex-
traction. A combining approach where individual 
techniques are carefully tuned and assigned in the 
right order has turned out to be superior to other 
potential solutions with regard to precision, recall, 
and number of user interactions. – Finally, is 
seems promising to use document and term fre-
quencies as additional evidence. The ides is that 
both are low for taxonomic names. 

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