Identifying problems and solutions in scientific text
Identifying problems and solutions in scientific text
Kevin Heffernan 0 1
Simone Teufel 0 1
Simone Teufel 0 1
0 Department of Computer Science and Technology, University of Cambridge , 15 JJ Thomson Avenue, Cambridge CB3 0FD , UK
1 & Kevin Heffernan
Research is often described as a problem-solving activity, and as a result, descriptions of problems and solutions are an essential part of the scientific discourse used to describe research activity. We present an automatic classifier that, given a phrase that may or may not be a description of a scientific problem or a solution, makes a binary decision about problemhood and solutionhood of that phrase. We recast the problem as a supervised machine learning problem, define a set of 15 features correlated with the target categories and use several machine learning algorithms on this task. We also create our own corpus of 2000 positive and negative examples of problems and solutions. We find that we can distinguish problems from non-problems with an accuracy of 82.3%, and solutions from non-solutions with an accuracy of 79.7%. Our three most helpful features for the task are syntactic information (POS tags), document and word embeddings.
Problem-solving patterns; Machine learning
Problem solving is generally regarded as the most important cognitive activity in everyday
and professional contexts
. Many studies on formalising the cognitive
process behind problem-solving exist, for instance
argues that we all share knowledge of the thought/action problem-solution process
involved in real life, and so our writings will often reflect this order. There is general
agreement amongst theorists that state that the nature of the research process can be viewed
as a problem-solving activity
(Str u¨bing 2007; Van Dijk 1980; Hutchins 1977; Grimes
One of the best-documented problem-solving patterns was established by
. Winter analysed thousands of examples of technical texts, and noted that these
texts can largely be described in terms of a four-part pattern consisting of Situation,
Problem, Solution and Evaluation. This is very similar to the pattern described by Van
Dijk (1980), which consists of Introduction-Theory, Problem-Experiment-Comment and
Conclusion. The difference is that in Winter’s view, a solution only becomes a solution
after it has been evaluated positively. Hoey changes Winter’s pattern by introducing the
concept of Response in place of Solution
. This seems to describe the situation
in science better, where evaluation is mandatory for research solutions to be accepted by
the community. In Hoey’s pattern, the Situation (which is generally treated as optional)
provides background information; the Problem describes an issue which requires attention;
the Response provides a way to deal with the issue, and the Evaluation assesses how
effective the response is.
An example of this pattern in the context of the Goldilocks story can be seen in Fig. 1.
In this text, there is a preamble providing the setting of the story (i.e. Goldilocks is lost in
the woods), which is called the Situation in Hoey’s system. A Problem in encountered
when Goldilocks becomes hungry. Her first Response is to try the porridge in big bear’s
bowl, but she gives this a negative Evaluation (‘‘too hot!’’) and so the pattern returns to the
Problem. This continues in a cyclic fashion until the Problem is finally resolved by
Goldilocks giving a particular Response a positive Evaluation of baby bear’s porridge
(‘‘it’s just right’’).
It would be attractive to detect problem and solution statements automatically in text.
This holds true both from a theoretical and a practical viewpoint. Theoretically, we know
that sentiment detection is related to problem-solving activity, because of the perception
that ‘‘bad’’ situations are transformed into ‘‘better’’ ones via problem-solving. The exact
mechanism of how this can be detected would advance the state of the art in text
understanding. In terms of linguistic realisation, problem and solution statements come in many
variants and reformulations, often in the form of positive or negated statements about the
conditions, results and causes of problem–solution pairs. Detecting and interpreting those
would give us a reasonably objective manner to test a system’s understanding capacity.
Fig. 1 Example of
problemsolving pattern when applied to
the Goldilocks story. Reproduced
with permission from
Practically, being able to detect any mention of a problem is a first step towards detecting a
paper’s specific research goal. Being able to do this has been a goal for scientific
information retrieval for some time, and if successful, it would improve the effectiveness of
scientific search immensely. Detecting problem and solution statements of papers would
also enable us to compare similar papers and eventually even lead to automatic generation
of review articles in a field.
There has been some computational effort on the task of identifying problem-solving
patterns in text. However, most of the prior work has not gone beyond the usage of
keyword analysis and some simple contextual examination of the pattern.
presents a corpus-based analysis of lexio-grammatical patterns for problem and
solution clauses using articles from professional and student reports. Problem and solution
keywords were used to search their corpora, and each occurrence was analysed to
determine grammatical usage of the keyword. More interestingly, the causal category associated
with each keyword in their context was also analysed. For example, Reason–Result or
Means-Purpose were common causal categories found to be associated with problem
The goal of the work by
was to determine words which are semantically
similar to problem and solution, and to determine how these words are used to signal
problem-solution patterns. However, their corpus-based analysis used articles from the
Guardian newspaper. Since the domain of newspaper text is very different from that of
scientific text, we decided not to consider those keywords associated with problem-solving
patterns for use in our work.
Instead of a keyword-based approach,
used discourse markers to
examine how the problem-solution pattern was signalled in text. In particular, they
examined how adverbials associated with a result such as ‘‘thus, therefore, then, hence’’ are
used to signal a problem-solving pattern.
Problem solving also has been studied in the framework of discourse theories such as
Rhetorical Structure Theory
(Mann and Thompson 1988)
and Argumentative Zoning
(Teufel et al. 2000)
. Problem- and solutionhood constitute two of the original 23 relations
(Mann and Thompson 1988)
. While we concentrate solely on this aspect, RST is a
general theory of discourse structure which covers many intentional and informational
relations. The relationship to Argumentative Zoning is more complicated. The status of
certain statements as problem or solutions is one important dimension in the definitions of
AZ categories. AZ additionally models dimensions other than problem-solution hood (such
as who a scientific idea belongs to, or which intention the authors might have had in stating
a particular negative or positive statement). When forming categories, AZ combines
aspects of these dimensions, and ‘‘flattens’’ them out into only 7 categories. In AZ it is
crucial who it is that experiences the problems or contributes a solution. For instance, the
definition of category ‘‘CONTRAST’’ includes statements that some research runs into
problems, but only if that research is previous work (i.e., not if it is the work contributed in
the paper itself). Similarly, ‘‘BASIS’’ includes statements of successful problem-solving
activities, but only if they are achieved by previous work that the current paper bases itself
on. Our definition is simpler in that we are interested only in problem solution structure,
not in the other dimensions covered in AZ. Our definition is also more far-reaching than
AZ, in that we are interested in all problems mentioned in the text, no matter whose
problems they are. Problem-solution recognition can therefore be seen as one aspect of AZ
which can be independently modelled as a ‘‘service task’’. This means that good problem
solution structure recognition should theoretically improve AZ recognition.
In this work, we approach the task of identifying problem-solving patterns in scientific
text. We choose to use the model of problem-solving described by
pattern comprises four parts: Situation, Problem, Response and Evaluation. The Situation
element is considered optional to the pattern, and so our focus centres on the core pattern
Goal statement and task
Many surface features in the text offer themselves up as potential signals for detecting
problem-solving patterns in text. However, since Situation is an optional element, we
decided to focus on either Problem or Response and Evaluation as signals of the pattern.
Moreover, we decide to look for each type in isolation. Our reasons for this are as follows:
It is quite rare for an author to introduce a problem without resolving it using some sort of
response, and so this is a good starting point in identifying the pattern. There are exceptions
to this, as authors will sometimes introduce a problem and then leave it to future work, but
overall there should be enough signal in the Problem element to make our method of
looking for it in isolation worthwhile. The second signal we look for is the use of Response
and Evaluation within the same sentence. Similar to Problem elements, we hypothesise that
this formulation is well enough signalled externally to help us in detecting the pattern. For
example, consider the following Response and Evaluation: ‘‘One solution is to use
smoothing’’. In this statement, the author is explicitly stating that smoothing is a solution to
a problem which must have been mentioned in a prior statement. In scientific text, we often
observe that solutions implicitly contain both Response and Evaluation (positive) elements.
Therefore, due to these reasons there should be sufficient external signals for the two
pattern elements we concentrate on here.
When attempting to find Problem elements in text, we run into the issue that the word
‘‘problem’’ actually has at least two word senses that need to be distinguished. There is a
word sense of ‘‘problem’’ that means something which must be undertaken (i.e. task),
while another sense is the core sense of the word, something that is problematic and
negative. Only the latter sense is aligned with our sense of problemhood. This is because
the simple description of a task does not predispose problemhood, just a wish to perform
some act. Consider the following examples, where the non-desired word sense is being
‘‘Das and Petrov (2011) also consider the problem of unsupervised bilingual POS
(Chen et al. 2011)
‘‘In this paper, we describe advances on the problem of NER in Arabic Wikipedia’’.
(Mohit et al. 2012)
Here, the author explicitly states that the phrases in orange are problems, they align with
our definition of research tasks and not with what we call here ‘problematic problems’. We
will now give some examples from our corpus for the desired, core word sense:
‘‘The major limitation of supervised approaches is that they require annotations for
(Poon and Domingos 2009)
‘‘To solve the problem of high dimensionality we use clustering to group the words
present in the corpus into much smaller number of clusters’’.
(Saha et al. 2008)
When creating our corpus of positive and negative examples, we took care to select only
problem strings that satisfy our definition of problemhood; ‘‘Corpus creation’’ section will
explain how we did that.
Our new corpus is a subset of the latest version of the ACL anthology released in March,
20161 which contains 22,878 articles in the form of PDFs and OCRed text.2
The 2016 version was also parsed using ParsCit
(Councill et al. 2008)
recognises not only document structure, but also bibliography lists as well as references within
running text. A random subset of 2500 papers was collected covering the entire ACL
timeline. In order to disregard non-article publications such as introductions to conference
proceedings or letters to the editor, only documents containing abstracts were considered.
The corpus was preprocessed using tokenisation, lemmatisation and dependency parsing
with the Rasp Parser
(Briscoe et al. 2006)
Definition of ground truth
Our goal was to define a ground truth for problem and solution strings, while covering as
wide a range as possible of syntactic variations in which such strings naturally occur. We
also want this ground truth to cover phenomena of problem and solution status which are
applicable whether or not the problem or solution status is explicitly mentioned in the text.
To simplify the task, we only consider here problem and solution descriptions that are at
most one sentence long. In reality, of course, many problem descriptions and solution
descriptions go beyond single sentence, and require for instance an entire paragraph.
However, we also know that short summaries of problems and solutions are very prevalent
in science, and also that these tend to occur in the most prominent places in a paper. This is
because scientists are trained to express their contribution and the obstacles possibly
hindering their success, in an informative, succinct manner. That is the reason why we can
afford to only look for shorter problem and solution descriptions, ignoring those that cross
To define our ground truth, we examined the parsed dependencies and looked for a
target word (‘‘problem/solution’’) in subject position, and then chose its syntactic argument
as our candidate problem or solution phrase. To increase the variation, i.e., to find as many
different-worded problem and solution descriptions as possible, we additionally used
semantically similar words (near-synonyms) of the target words ‘‘problem’’ or ‘‘solution’’
for the search. Semantic similarity was defined as cosine in a deep learning distributional
vector space, trained using Word2Vec
(Mikolov et al. 2013)
on 18,753,472 sentences from
a biomedical corpus based on all full-text Pubmed articles
(McKeown et al. 2016)
the 200 words which were semantically closest to ‘‘problem’’, we manually selected 28
clear synonyms. These are listed in Table 1. From the 200 semantically closest words to
‘‘solution’’ we similarly chose 19 (Table 2). Of the sentences matching our dependency
search, a subset of problem and solution candidate sentences were randomly selected.
An example of this is shown in Fig. 2. Here, the target word ‘‘drawback’’ is in subject
position (highlighted in red), and its clausal argument (ccomp) is ‘‘(that) it achieves low
1 http://acl-arc.comp.nus.edu.sg/. 2 The corpus comprises 3,391,198 sentences, 71,149,169 words and 451,996,332 characters.
performance’’ (highlighted in purple). Examples of other arguments we searched for
included copula constructions and direct/indirect objects.
If more than one candidate was found in a sentence, one was chosen at random.
Nongrammatical sentences were excluded; these might appear in the corpus as a result of its
source being OCRed text.
800 candidates phrases expressing problems and solutions were automatically extracted
(1600 total) and then independently checked for correctness by two annotators (the two
authors of this paper). Both authors found the task simple and straightforward. Correctness
was defined by two criteria:
The sentence must unambiguously and clearly state the phrase’s status as either a
problem or a solution. For problems, the guidelines state that the phrase has to
represent one of the following:
An unexplained phenomenon or a problematic state in science; or
A research question; or
An artifact that does not fulfil its stated specification.
For solutions, the phrase had to represent a response to a problem with a positive
evaluation. Implicit solutions were also allowed.
The phrase must not lexically give away its status as problem or solution phrase.
The second criterion saves us from machine learning cues that are too obvious. If for
instance, the phrase itself contained the words ‘‘lack of’’ or ‘‘problematic’’ or ‘‘drawback’’,
our manual check rejected it, because it would be too easy for the machine learner to learn
such cues, at the expense of many other, more generally occurring cues.
Sampling of negative examples
We next needed to find negative examples for both cases. We wanted them not to stand out
on the surface as negative examples, so we chose them so as to mimic the obvious
characteristics of the positive examples as closely as possible. We call the negative
examples ‘non-problems’ and ‘non-solutions’ respectively. We wanted the only differences
between problems and non-problems to be of a semantic nature, nothing that could be read
off on the surface. We therefore sampled a population of phrases that obey the same
statistical distribution as our problem and solution strings while making sure they really are
negative examples. We started from sentences not containing any problem/solution words
(i.e. those used as target words). From each such sentence, we at random selected one
syntactic subtree contained in it. From these, we randomly selected a subset of negative
examples of problems and solutions that satisfy the following conditions:
The distribution of the head POS tags of the negative strings should perfectly match the
head POS tags3 of the positive strings. This has the purpose of achieving the same
proportion of surface syntactic constructions as observed in the positive cases.
The average lengths of the negative strings must be within a tolerance of the average
length of their respective positive candidates e.g., non-solutions must have an average
length very similar (i.e. þ= small tolerance) to solutions. We chose a tolerance value
of 3 characters.
Again, a human quality check was performed on non-problems and non-solutions. For each
candidate non-problem statement, the candidate was accepted if it did not contain a
phenomenon, a problematic state, a research question or a non-functioning artefact. If the
string expressed a research task, without explicit statement that there was anything
problematic about it (i.e., the ‘wrong’ sense of ‘‘problem’’, as described above), it was
allowed as a non-problem. A clause was confirmed as a non-solution if the string did not
represent both a response and positive evaluation.
If the annotator found that the sentence had been slightly mis-parsed, but did contain a
candidate, they were allowed to move the boundaries for the candidate clause. This
resulted in cleaner text, e.g., in the frequent case of coordination, when non-relevant
constituents could be removed.
From the set of sentences which passed the quality-test for both independent assessors,
500 instances of positive and negative problems/solutions were randomly chosen (i.e. 2000
instances in total). When checking for correctness we found that most of the automatically
extracted phrases which did not pass the quality test for problem-/solution-hood were
either due to obvious learning cues or instances where the sense of problem-hood used is
relating to tasks (cf. ‘‘Goal statement and task’’ section).
3 The head POS tags were found using a modification of the Collins’ Head Finder. This modified algorithm
addresses some of the limitations of the head finding heuristics described by
and can be found
In our experiments, we used three classifiers, namely Na¨ıve Bayes, Logistic Regression
and a Support Vector Machine. For all classifiers an implementation from the WEKA
machine learning library
(Hall et al. 2009)
was chosen. Given that our dataset is small,
tenfold cross-validation was used instead of a held out test set. All significance tests were
conducted using the (two-tailed) Sign Test
Linguistic correlates of problem- and solution-hood
We first define a set of features without taking the phrase’s context into account. This will
tell us about the disambiguation ability of the problem/solution description’s semantics
alone. In particular, we cut out the rest of the sentence other than the phrase and never use
it for classification. This is done for similar reasons to excluding certain ‘give-away’
phrases inside the phrases themselves (as explained above). As the phrases were found
using templates, we know that the machine learner would simply pick up on the semantics
of the template, which always contains a synonym of ‘‘problem’’ or ‘‘solution’’, thus
drowning out the more hidden features hopefully inherent in the semantics of the phrases
themselves. If we allowed the machine learner to use these stronger features, it would
suffer in its ability to generalise to the real task.
ngrams Bags of words are traditionally successfully used for classification tasks in NLP,
so we included bags of words (lemmas) within the candidate phrases as one of our features
(and treat it as a baseline later on). We also include bigrams and trigrams as multi-word
combinations can be indicative of problems and solutions e.g., ‘‘combinatorial explosion’’.
Polarity Our second feature concerns the polarity of each word in the candidate strings.
Consider the following example of a problem taken from our dataset: ‘‘very conservative
approaches to exact and partial string matches overgenerate badly’’. In this sentence,
words such as ‘‘badly’’ will be associated with negative polarity, therefore being useful in
determining problem-hood. Similarly, solutions will often be associated with a positive
sentiment e.g. ‘‘smoothing is a good way to overcome data sparsity’’. To do this, we
perform word sense disambiguation of each word using the Lesk algorithm
The polarity of the resulting synset in SentiWordNet
(Baccianella et al. 2010)
looked up and used as a feature.
Syntax Next, a set of syntactic features were defined by using the presence of POS tags
in each candidate. This feature could be helpful in finding syntactic patterns in problems
and solutions. We were careful not to base the model directly on the head POS tag and the
length of each candidate phrase, as these are defining characteristics used for determining
the non-problem and non-solution candidate set.
Negation Negation is an important property that can often greatly affect the polarity of a
phrase. For example, a phrase containing a keyword pertinent to solution-hood may be a
good indicator but with the presence of negation may flip the polarity to problem-hood e.g.,
‘‘this can’t work as a solution’’. Therefore, presence of negation is determined.
Exemplification and contrast Problems and solutions are often found to be coupled with
examples as they allow the author to elucidate their point. For instance, consider the
following solution: ‘‘Once the translations are generated, an obvious solution is to pick the
most fluent alternative, e.g., using an n-gram language model’’.
(Madnani et al. 2012)
acknowledge this, we check for presence of exemplification. In addition to examples,
problems in particular are often found when contrast is signalled by the author (e.g.
‘‘however, ‘‘but’’), therefore we also check for presence of contrast in the problem and
non-problem candidates only.
Discourse Problems and solutions have also been found to have a correlation with
discourse properties. For example, problem-solving patterns often occur in the background
sections of a paper. The rationale behind this is that the author is conventionally asked to
objectively criticise other work in the background (e.g. describing research gaps which
motivate the current paper). To take this in account, we examine the context of each string
and capture the section header under which it is contained (e.g. Introduction, Future work).
In addition, problems and solutions are often found following the Situation element in the
problem-solving pattern (cf. ‘‘Introduction’’ section). This preamble setting up the problem
or solution means that these elements are likely not to be found occurring at the beginning
of a section (i.e. it will usually take some sort of introduction to detail how something is
problematic and why a solution is needed). Therefore we record the distance from the
candidate string to the nearest section header.
Subcategorisation and adverbials Solutions often involve an activity (e.g. a task). We
also model the subcategorisation properties of the verbs involved. Our intuition was that
since problematic situations are often described as non-actions, then these are more likely
to be intransitive. Conversely solutions are often actions and are likely to have at least one
argument. This feature was calculated by running the C&C parser
(Curran et al. 2007)
each sentence. C&C is a supertagger and parser that has access to subcategorisation
information. Solutions are also associated with resultative adverbial modification (e.g.
‘‘thus, therefore, consequently’’) as it expresses the solutionhood relation between the
problem and the solution. It has been seen to occur frequently in problem-solving patterns,
as studied by
. Therefore, we check for presence of resultative adverbial
modification in the solution and non-solution candidate only.
Embeddings We also wanted to add more information using word embeddings. This was
done in two different ways. Firstly, we created a Doc2Vec model
(Le and Mikolov 2014)
which was trained on 19 million sentences from scientific text (no overlap with our data
set). An embedding was created for each candidate sentence. Secondly, word embeddings
were calculated using the Word2Vec model (cf. ‘‘Corpus creation’’ section). For each
candidate head, the full word embedding was included as a feature. Lastly, when creating
our polarity feature we query SentiWordNet using synsets assigned by the Lesk algorithm.
However, not all words are assigned a sense by Lesk, so we need to take care when that
happens. In those cases, the distributional semantic similarity of the word is compared to
two words with a known polarity, namely ‘‘poor’’ and ‘‘excellent’’. These particular words
have traditionally been consistently good indicators of polarity status in many studies
(Turney 2002; Mullen and Collier 2004)
. Semantic similarity was defined as cosine
similarity on the embeddings of the Word2Vec model (cf. ‘‘Corpus creation’’ section).
Modality Responses to problems in scientific writing often express possibility and
necessity, and so have a close connection with modality. Modality can be broken into three
main categories, as described by
, namely epistemic (possibility), deontic
(permission / request / wish) and dynamic (expressing ability).
Problems have a strong relationship to modality within scientific writing. Often, this is
due to a tactic called ‘‘hedging’’
(Medlock and Briscoe 2007)
where the author uses
speculative language, often using Epistemic modality, in an attempt to make either
noncommital or vague statements. This has the effect of allowing the author to distance
themselves from the statement, and is often employed when discussing negative or
Italicized results reflect highest f-measure reported per modal category
problematic topics. Consider the following example of Epistemic modality from
and Hearst (2008)
: ‘‘A potential drawback is that it might not work well for low-frequency
To take this linguistic correlate into account as a feature, we replicated a modality
classifier as described by
(Ruppenhofer and Rehbein 2012)
. More sophisticated modality
classifiers have been recently introduced, for instance using a wide range of features and
convolutional neural networks, e.g,
(Zhou et al. 2015; Marasovic´ and Frank 2016)
However, we wanted to check the effect of a simpler method of modality classification on
the final outcome first before investing heavily into their implementation. We trained three
classifiers using the subset of features which Ruppenhofer et al. reported as performing
best, and evaluated them on the gold standard dataset provided by the authors4. The results
of the are shown in Table 3. The dataset contains annotations of English modal verbs on
the 535 documents of the first MPQA corpus release
(Wiebe et al. 2005)
Logistic Regression performed best overall and so this model was chosen for our
upcoming experiments. With regards to the optative and concessive modal categories, they
can be seen to perform extremely poorly, with the optative category receiving a null score
across all three classifiers. This is due to a limitation in the dataset, which is unbalanced
and contains very few instances of these two categories. This unbalanced data also is the
reason behind our decision of reporting results in terms of recall, precision and f-measure
in Table 3.
The modality classifier was then retrained on the entirety of the dataset used by
Ruppenhofer and Rehbein (2012)
using the best performing model from training (Logistic
Regression). This new model was then used in the upcoming experiment to predict
modality labels for each instance in our dataset.
As can be seen from Table 4, we are able to achieve good results for distinguishing a
problematic statement from non-problematic one. The bag-of-words baseline achieves a
very good performance of 71.0% for the Logistic Regression classifier, showing that there
is enough signal in the candidate phrases alone to distinguish them much better than
Table 4 Results distinguishing
problems from non-problems
using Na¨ıve Bayes (NB), logistic
regression (LR) and a support
vector machine (SVM)
Each feature set’s performance is
shown in isolation followed by
combinations with other features.
crossvalidation was used across all
significance with respect to the
baseline at the p\0:05, 0.01,
0.001 levels is denoted by *, **
and *** respectively
Taking a look at Table 5, which shows the information gain for the top lemmas,
we can see that the top lemmas are indeed indicative of problemhood (e.g.
‘‘limit’’,‘‘explosion’’). Bigrams achieved good performance on their own (as did negation
and discourse) but unfortunately performance deteriorated when using trigrams,
particularly with the SVM and LR. The subcategorisation feature was the worst performing
feature in isolation. Upon taking a closer look at our data, we saw that our hypothesis that
intransitive verbs are commonly used in problematic statements was true, with over 30% of
our problems (153) using them. However, due to our sampling method for the negative
cases we also picked up many intransitive verbs (163). This explains the almost random
chance performance (i.e. 50%) given that the distribution of intransitive verbs amongst the
positive and negative candidates was almost even.
The modality feature was the most expensive to produce, but also didn’t perform very
well is isolation. This surprising result may be partly due to a data sparsity issue
where only a small portion (169) of our instances contained modal verbs. The
breakdown of how many types of modal senses which occurred is displayed in Table 6. The
Table 5 Information gain (IG)
in bits of top lemmas from the
bag-of-words baseline in Table 4
No. of instances
most dominant modal sense was epistemic. This is a good indicator of problemhood (e.g.
hedging, cf. ‘‘Linguistic correlates of problem- and solution-hood’’ section) but if the
accumulation of additional data was possible, we think that this feature may have the
potential to be much more valuable in determining problemhood. Another reason for the
performance may be domain dependence of the classifier since it was trained on text from
different domains (e.g. news). Additionally, modality has also shown to be helpful in
determining contextual polarity
(Wilson et al. 2005)
and argumentation (Becker et al.
2016), so using the output from this modality classifier may also prove useful for further
feature engineering taking this into account in future work.
Polarity managed to perform well but not as good as we hoped. However, this feature
also suffers from a sparsity issue resulting from cases where the Lesk algorithm
is not able to resolve the synset of the syntactic head.
Knowledge of syntax provides a big improvement with a significant increase over the
baseline results from two of the classifiers.
Examining this in greater detail, POS tags with high information gain mostly included
tags from open classes (i.e. VB-, JJ-, NN- and RB-). These tags are often more associated
with determining polarity status than tags such as prepositions and conjunctions (i.e.
adverbs and adjectives are more likely to be describing something with a non-neutral
The embeddings from Doc2Vec allowed us to obtain another significant increase in
performance (72.9% with Na¨ıve Bayes) over the baseline and polarity using Word2Vec
provided the best individual feature result (77.2% with SVM).
Combining all features together, each classifier managed to achieve a significant result
over the baseline with the best result coming from the SVM (81.8%). Problems were also
better classified than non-problems as shown in the confusion matrix in Table 7. The
addition of the Word2Vec vectors may be seen as a form of smoothing in cases where
previous linguistic features had a sparsity issue i.e., instead of a NULL entry, the
embeddings provide some sort of value for each candidate. Particularly wrt. the polarity
feature, cases where Lesk was unable to resolve a synset meant that a ZERO entry was
added to the vector supplied to the machine learner. Amongst the possible combinations,
the best subset of features was found by combining all features with the exception of
bigrams, trigrams, subcategorisation and modality. This subset of features managed to
Table 8 Results distinguishing
solutions from non-solutions
using Na¨ıve Bayes (NB), logistic
regression (LR) and a support
vector machine (SVM)
Each feature set’s performance is
shown in isolation followed by
combinations with other features.
crossvalidation was used across all
improve results in both the Na¨ıve Bayes and SVM classifiers with the highest overall result
coming from the SVM (82.3%).
The results for disambiguation of solutions from non-solutions can be seen in Table 8.
The bag-of-words baseline performs much better than random, with the performance being
quite high with regard to the SVM (this result was also higher than any of the baseline
performances from the problem classifiers). As shown in Table 9, the top ranked lemmas
from the best performing model (using information gain) included ‘‘use’’ and ‘‘method’’.
These lemmas are very indicative of solutionhood and so give some insight into the high
baseline returned from the machine learners. Subcategorisation and the result adverbials
were the two worst performing features. However, the low performance for
subcategorisation is due to the sampling of the non-solutions (the same reason for the low performance
Table 9 Information gain (IG)
in bits of top lemmas from the
bag-of-words baseline in Table 8
of the problem transitivity feature). When fitting the POS-tag distribution for the negative
samples, we noticed that over 80% of the head POS-tags were verbs (much higher than the
problem heads). The most frequent verb type being the infinite form.
This is not surprising given that a very common formulation to describe a solution is to
use the infinitive ‘‘TO’’ since it often describes a task e.g., ‘‘One solution is to find the
singletons and remove them’’. Therefore, since the head POS tags of the non-solutions had
to match this high distribution of infinitive verbs present in the solution, the
subcategorisation feature is not particularly discriminatory. Polarity, negation, exemplification and
syntactic features were slightly more discriminate and provided comparable results.
However, similar to the problem experiment, the embeddings from Word2Vec and
Doc2Vec proved to be the best features, with polarity using Word2Vec providing the best
individual result (73.4% with SVM).
Combining all features together managed to improve over each feature in isolation and
beat the baseline using all three classifiers. Furthermore, when looking at the confusion
matrix in Table 10 the solutions were classified more accurately than the non-solutions.
The best subset of features was found by combining all features without adverbial of result,
bigrams, exemplification, negation, polarity and subcategorisation. The best result using
this subset of features was achieved by the SVM with 79.7%. It managed to greatly
improve upon the baseline but was just shy of achieving statistical significance
(p ¼ 0:057).
In this work, we have presented new supervised classifiers for the task of identifying
problem and solution statements in scientific text. We have also introduced a new corpus
for this task and used it for evaluating our classifiers. Great care was taken in constructing
the corpus by ensuring that the negative and positive samples were closely matched in
terms of syntactic shape. If we had simply selected random subtrees for negative samples
without regard for any syntactic similarity with our positive samples, the machine learner
may have found easy signals such as sentence length. Additionally, since we did not allow
the machine learner to see the surroundings of the candidate string within the sentence, this
made our task even harder. Our performance on the corpus shows promise for this task, and
proves that there are strong signals for determining both the problem and solution parts of
the problem-solving pattern independently.
With regard to classifying problems from non-problems, features such as the POS tag,
document and word embeddings provide the best features, with polarity using the
Word2Vec embeddings achieving the highest feature performance. The best overall result
was achieved using an SVM with a subset of features (82.3%). Classifying solutions from
non-solutions also performs well using the embedding features, with the best feature also
being polarity using the Word2Vec embeddings, and the highest result also coming from
the SVM with a feature subset (79.7%).
In future work, we plan to link problem and solution statements which were found
independently during our corpus creation. Given that our classifiers were trained on data
solely from the ACL anthology, we also hope to investigate the domain specificity of our
classifiers and see how well they can generalise to domains other than ACL (e.g.
bioinformatics). Since we took great care at removing the knowledge our classifiers have of the
explicit statements of problem and solution (i.e. the classifiers were trained only on the
syntactic argument of the explicit statement of problem-/solution-hood), our classifiers
should in principle be in a good position to generalise, i.e., find implicit statements too. In
future work, we will measure to which degree this is the case.
To facilitate further research on this topic, all code and data used in our experiments can
be found here: www.cl.cam.ac.uk/*kh562/identifying-problems-and-solutions.html
Acknowledgements The first author has been supported by an EPSRC studentship (Award Ref: 1641528).
We thank the reviewers for their helpful comments.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if changes were made.
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