numerose analisi e ricerche. Il Contesto
Citazionale e` stato definito in diversi modi
in letteratura, in relazione a differenti
scopi, domini e applicazioni. In questo
paper presentiamo le principali
dimensioni testuali di Contesto Citazionale
investigate dai ricercatori nel corso degli
anni.
1 Introduction and Background
Researchers consider as Citation Context (CC)
different snippets of text around a citation marker.
These differences of width influence the
applications that exploit CC as source of
information. For example,
Qazvinian and Radev (2010)
showed that using also implicit citations (i.e.
sentences that contain information about a specific
secondary source but do not explicitly cite it) for
generating surveys, rather than citing sentences
alone, improve the results.
Ritchie et al. (2008)
compared different widths of CC in order to find
the most appropriate window for identifying
Index Terms. They proved that varying the context
from which the Index Terms are gathered has a
significant effect on retrieval effectiveness.
Aljaber et al. (2010)
tested different sizes of CC for
a document clustering experiment. They claimed
that a window size of 50 words from either side
of the citation marker works better than taking 10
or 30 terms or the citing sentence alone, whatever
its size is. From their analysis, relevant
synonymous and related vocabulary extracted from this
window of text, in combination with an original
full-text representation of the cited document, are
effective for document clustering. We can claim
that the issue of finding the optimal CC for a
specific application is a challenging task that interests
researchers and which is at the base of every study
that exploits the CC as a source of information.
1 With the purpose of providing a useful
background to anyone approaching this question, in the
following sections we give an overview of
different dimensions of textual CC investigated in
literature. We classified them in 3 main categories:
a) fixed number of characters b) citing sentence
c) extended context (fixed and adaptive), and we
summarized our analysis in Figure1. We focus
on the strategies to identify the correct textual CC
of a citation, nevertheless other CC related topics
have been investigated in literature as for example
citation recommendations (see
Farber (2018)
and
Ebesu (2017)
)
The belief of the need of a clear introductory
survey about how CC has been differently shaped in
literature came to our mind when we faced the
problem of defining the optimal CC for the
Semantic Coloring of Academic References (SCAR)
project1
(Di Iorio et al., 2018)
. The goal of the
SCAR project is to enrich bibliographies of
scientific articles by adding explicit meta data about
individual bibliographic entries and to characterize
these entries according to multiple criteria. With
this purpose, we are studying a set of properties
to support the automatic characterization of
bibliographic entries and one of our primary source of
information is the textual content around citation
markers, i.e. the CC. We are currently
investigating on finding the best span of text for our needs.
By reviewing the literature, we realized that
different approaches correspond to different tasks and
are also related to the linguistic domain of
application. The SCAR project as well as this review are
focused on the English language but it would be
interesting to extend this study to other languages.
1http://dasplab.cs.unibo.it/index.php/scar/
2
Fixed Number of Characters
A good way to start exploring how the CC can be
diversely defined is to look for well known
examples. One of these is the public search engine and
digital library for scientific and academic papers
CiteSeerX2. This web platform allows users to
browse papers’ references and to read the context
in which a reference is cited. The function enables
the reading of 200 characters before and after the
citation marker. Here the choice of the CC width
is not directly related to further analysis and
applications as the purpose is the mere reading of text
by users. As
Ii et al. (2014)
describe, CiteSeerX
uses ParsCit
(Councill et al., 2008)
for citation
extraction. ParsCit is a freely available, open-source
implementation of a reference string parsing
package which performs reference string segmentation
and CC extraction. The size of the context is
configurable, but by default extends to 200 characters
on either side of the match. ParsCit is a well know
software and is used in different projects. For
example, the Association Of Computational
Linguistics (ACL) Anthology Network3 uses ParsCit
for curation.
Doslu and Bingol (2016)
also used
ParsCit in their work regarding how to rank
articles for a given topic. The authors exploited the
information contained in the CC of a certain
paper for detecting important articles and providing
focused directions to access the literature about a
topic. They stated that the words that are used to
describe a cited paper stand close to the citation
marker, and this is their motivation for choosing a
fixed window size context. Before Doslu and
Bingol, also
Bradshaw (2003)
used CC to index cited
2http://citeseerx.ist.psu.edu/index
3http://aan.how/index.php/home/about
paper for specific topics. He designed the
Reference Direct Indexing in which measures of
relevance and impact are joined in a single retrieval
metric based on the comparison of the terms
authors use in multiple CC of a document. The CC
Bradshaw used to index the documents are directly
gathered from CiteSeerX. Also the tool presented
by
Knoth et al. (2017)
, who address the problem
of automatically retrieving and collecting CC for
a given unstructured research paper, extract a CC
window of fixed length corresponding to 300
characters before and after a citation marker. The
approach of considering as CC a fixed length
snippet around the citation marker is a naive baseline
method. It can be used to retrieve terms related to
a cited entity and the accuracy of applications that
employ it might be improved for example by
considering sentence or paragraph boundaries
(Aljaber
et al., 2010)
. This kind of context is unsuitable if
the CC needs to be further analyzed, for example
by using syntactic parsers, or if its content have
to be represented in a coherent formal way where
the meaning and structure of sentences have to be
preserved.
3
Citing Sentence
Another famous platform among scholars is
Semantic Scholar4. This subjective search service
for journal articles provides several functions for
browsing papers among which the possibility of
quickly read the CC of each citation. This service
allows reading more than one excerpt of text for
each entity (when available). Each CC shown
corresponds exactly to a citing sentence, i.e. the
sentence that contains the targeted reference marker.
Implicit citations5 are also investigated by
exploiting lexical hooks and also in these cases the CC
excerpts shown are in the form of a full sentence.
The same CC window has been adopted in
several projects.
Nakov et al. (2004)
investigated
the use of CC for semantic interpretation of
bioscience articles. Starting from the collection of the
citing sentences related to a specific cited entity
(that they call citances), they used the output of a
4https://www.semanticscholar.org
5More in details, with implicit citations we refer to those
mentions of a work where the relation cited entity-citing
entity is not provided by a citation marker but rather by a lexical
object related to the cited entity. E.g.: The heuristics based on
WordNet and Wikipedia ontologies are very sensitive to
preprocessing is an implicit citation of George A. Miller (1995).
WordNet: A Lexical Database for English. Communications
of the ACM Vol. 38, No. 11: 39-41.
dependency parser to build paraphrase expressing
relations between two named entities. As
commented before, parsers need to be fed with full
sentences in order to provide proper
representations and this work is a clear example where a
fixed length CC would not have been an
appropriate input. Also
Elkiss et al. (2008)
focused
their research on the set of citing sentences of a
given article (named by the authors citation
summaries) testing the biomedical domain. Despite
Elkiss study did not rely on any strictly sentence
based technique (they employed cosine
similarity and tf-idf), both their hypothesis are grounded
on the importance of citing sentences boundaries.
Sula and Miller (2014)
presented an experimental
tool for extracting and classifying citation contexts
in humanities. Their approach is based on
citing sentences from which they extracted features
(e.g. location in document) and polarity
(evaluating n-grams with a naive Bayes classifier).
Bertin
et al. (2016)
followed a similar approach to
identify n-grams and sentiment in CC. They chose to
work on a sentence basis stating that sentences are
the natural building blocks of text and likely to
include the context of a specific reference. Starting
from citing sentences they extracted 3-grams
containing verbs, together with position in the paper
and type of section according to the IMRaD
structure in order to analyze the combination and
distribution of these features in the biomedical domain.
Citing sentence as a base unit for CC is mostly
chosen in hard sciences domains. In fact,
scientific communities have particular ways of
using language and specific conventions that reveal
clear disciplinary differences.
Hyland (2009)
describes some of these language variations that go
from terminology differences to different citations
practices and rhetorical preferences. Writers use
different sets of reporting verbs to refer to others
work (engineers show, philosophers argue,
biologists find and linguists suggest); frequencies of
hedges and self citations, directives and n-grams
also diverge across fields. In the humanities
writers tend to include extensive referencing and build
a background for the heterogeneous readership
while in hard sciences most of the readers share a
common context with writers. This attitude
clarifies citers’ behaviors in different domains and
makes us presume that CC in humanities might
be more complex than in hard sciences.
Following these considerations, it is reasonable to
conclude that for choosing the appropriate CC width
one needs to take into account not only the task
he is going to face but also the domain of
applications and the specificity of the language. In this
sense, CC as citing sentence might not always
correspond to the entire fragment of text referring to
a targeted citation marker.
4
Extended Context
Extending CC beyond the citing sentence can
prove useful in many cases as illustrated by
the social networking site for researchers
ResearchGate6. Every document in this platform’s
database can be inspected according to different
prospectives. Among them, readers can browse
documents citations lists and access CC (when
available) displayed in the form of: 1 sentence
before the citing sentence + citing sentence + 1
sentence after the citing sentence. This window
size allows users to better understand the full
context of a citation without loosing any possible
informations contained in the nearby sentences.
This is particularly relevant for the task of polarity
identification of citations.
Athar and Teufel (2012)
have shown that authors’ sentiments are most
likely expressed outside the citing sentences.
Sentiment in citations is often hidden and especially
criticism might be hedged both for politeness
and for political reasons
(MacRoberts and
MacRoberts, 1984)
. Citing sentences are typically
neutral and in particular negative polarity occurs
in the following sentences
(Teufel et al., 2006)
,
see for example (from
(Platt, 1990)
):
In [19, sec. 11.11], Vapnik suggests a method
for mapping the output of SVM to probabilities by
decomposing the feature space []. Preliminary
results for this method, are promising.However,
there are some limitations that are overcome by
the method of this chapter.
Particularly for, but not limited to, polarity
identification tasks, a context extended to the nearby
sentences can supply the complete set of
information about a citation to applications and readers.
Sentences nearby a citing sentence can be add as
part of the CC according to a fixed schema or by
following an adaptive approach.
6https://www.researchgate.net
4.1
Besides ResearchGate and the aforementioned
Ritchie’s work, who studied different window
sizes of CC for identifying Index Terms, also
Mei
and Zhai (2008)
implemented a fixed extended
context for their study of summarizing articles
influence. For their impact-based summarization
task they used a 5 sentences window size, with
2 sentences before and after the citing sentence.
This technique allows to include more info in the
CC but at the same time the risk of adding noise is
high. This is why most of the literature concerning
extended CC rather provides adaptive methods.
A mention is needed to the work of
Fujiwara and
Yamamoto (2015)
, mostly for their overall project
than for the CC retrieval approach which relies on
a very basic technique (they include the sentence
after the citing one if the reference marker is at
the end of the citing sentence and limit long citing
sentences to 240 characters before and after
citation markers). The authors built the Colil database
where CC of the life sciences domain are stored,
and made it available to users through a web-based
search service. For each resource stored in the
database, a list of CC in which the resource has
been cited is returned to the user who can easily
read how a work is perceived and used by
different authors.
4.2
Adaptive Extended Context
O’Connor (1982
) was the first who investigated
the CC as a sequence of sentences - a
multisentence citing statement. His purpose was to
study the words of CC as possible improvement
for the retrieval of the related cited entities. He
wrote 16 complex and detailed computer rules (not
completely computer procedures at that time) with
linguistic, structural and more general features for
the selection of citing statements.
Nanba and
Okumura (1999)
presented a system to support
writing surveys of a specific domain. They see the
CC as a succession of sentences where the
possible connections are indicated by 6 kinds of cue
words (anaphora, negative expression, 1st and 3rd
person pronoun, adverb, other) that they use for
retrieving the suitable CC for their system. To
identify the full span of CC,
Kaplan et al. (2009)
presented a different method based on co-reference
chains. They built a SVM
(Cortes and Vapnik,
1995)
classifier with 13 features (among which:
cosine similarity, gender and number agreement,
semantic class agreement etc.) that are tested in
order to find the best configuration. Results of the
classifier alone and in combination with cue-based
techniques are promising. Despite the little data
analyzed for the project, Kaplan raised some
interesting remarks about CC. Particularly, they stated
that sentences of CC are not necessarily
contiguous.
Qazvinian and Radev (2010)
explored the
task of retrieving background information close to
explicit citations by implementing a probabilistic
inference model (Markov Random Field). Like
previous authors, they observed that the majority
of sentences related to a citation directly occur
after or before the citation or another context
sentence; however they also confirmed Kaplan’s
intuition about possible gaps between sentences
describing a cited paper.
Athar and Teufel (2012)
tried to go further by attempting to retrieve all the
mentions of a cited entity within the full text of the
citing paper. As claimed by the authors, mentions
to a cited entity can occur in the full article and are
necessary to identify the real sentiment toward the
cited work. Their first experiment of manual
annotation proved the insight that retrieving all the
mentions of a cited entity increases citation
sentiment coverage. Also the SVM framework
implemented by the authors, despite limited to a 4
sentence window, outperformed a single sentence
baseline system.
Abu-Jbara et al. (2013)
, with
the purpose of adding qualitative aspects to
standard quantitative bibliometrics (H-Index, G-Index,
etc.), analyzed the text surrounding a citation in
order to define the citer’s purposes and polarity. This
piece of text (CC), is retrieved with a sequence
labeling method. Starting from the citing sentence,
Abu-Jbara’s team used CRF
(Lafferty et al., 2001)
to determine if the sentence before and the two
sentences after the citing sentences have to be
included in the CC. The features for the CRF model
are both structural (e.g. position of the current
sentence with respect to the citing sentence) and
lexical (e.g. presence of demonstrative determiners).
Kaplan et al. (2016)
named Citation Block
Determination(CBD) the task of detecting non-explicit
citing sentences and faced it by testing various
features representing different aspects of textual
coherence. Non local mentions are excluded from
what they formalized as a binary classification task
of sentences from the citing one. They tested
different relational and entity coherence features and
their combinations. Experiments showed that the
CRF method fits better the task than the SVM
approach.
The different works briefly described so far give
an overview of the most interesting techniques
explored by researchers. From rule-based
approaches to probability methods, the implemented
features are most of the time domain-specific
relying on particular vocabulary and on stylistic and
rhetorical habits.
4.2.1
Citation Scope
Related to the Adaptive Extended Context topic is
the identification of the Scope of a citation. So far
we have discussed different ways of including in
the CC what is outside the citing sentence but at
the same time related to it. The idea is to extend
the context. However, there are cases in which the
citing sentence does not completely refer to the
targeted citation or where the context of multiple
citations overlap. In these cases the
aforementioned approaches of CC extraction would include
noise and affect applications results. See for
instance the following example where the whole
citing sentence might produce a negative polarity
despite the neutral value of the citation:
The negative results produced by the BoW
approach led our team to change direction and
we tested a SVM
(CORTES, 1995)
classifier.
Finding a procedure to cut out the precise scope
of a citation is a tricky and challenging task for
which little experiments have been done.
Athar (2011)
suggested to trim the parse tree of
each citing sentence and to keep only the deepest
clause in the subtree of which the citation is a part.
Abu-Jbara and Radev (2012)
explored 3 different
methods for identifying the scope: word
classification, sequence labeling and segment
classification. Results showed that the scope of a given
reference consists of units of higher granularity than
words. In fact, the segment classification
technique achieved the best performance. Despite the
interesting results, we agree with
Hernandez and
Gomez (2016)
who stated that additional work is
required to improve the citation scope
identification task. The need of further research in this
field is also encouraged by the analysis of
Jha et
al. (2017)
who performed an annotation
experiment on a sample of the ACL Anthology Network
revealing that, on average, the reference scope for
a given target reference contains only 57.63 per
cent of the original citing sentence.
5