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<article xmlns:xlink="http://www.w3.org/1999/xlink">
  <front>
    <journal-meta />
    <article-meta>
      <title-group>
        <article-title>Cues, Scope, and Focus: Annotating Negation in Spanish Corpora</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Lucia Donatelli</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>Georgetown University Bunn Intercultural Center</institution>
          ,
          <addr-line>403A 37th and O Streets, N.W. Washington, DC 20057</addr-line>
          ,
          <country country="US">USA</country>
        </aff>
      </contrib-group>
      <pub-date>
        <year>2018</year>
      </pub-date>
      <volume>2174</volume>
      <fpage>29</fpage>
      <lpage>34</lpage>
      <abstract>
        <p>The objective of NEGES Task 1 is to establish a standard for the annotation of negation in Spanish-language corpora. Speci cally, the task analyzes guidelines used for such annotation in ve projects over three domains: news (Sandoval and Salazar, 2013), clinical reports (Oronoz et al., 2015; Cruz et al., 2017; Marimon, Vivaldi, and Bel, 2017), and product reviews (Jimenez-Zafra et al., 2018b). Here, an assessment of these various guidelines is presented, with the goals of helping establish a standard set of guidelines for annotating negation in Spanish across domains, and of contributing to the workshop's overall conversation about the treatment of negation in computational linguistics.</p>
      </abstract>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>-</title>
      <p>
        Negation is fundamental to sentence
meaning, bearing on the key questions of \what
happened (and what did not)", \who did (or
did not do) what to whom," and \what
existed (or did not)." In other words, negation
helps establish what is fact and what is not,
due to its ability to a ect the truth value of
a sentence
        <xref ref-type="bibr" rid="ref7">(Horn, 1989)</xref>
        . This is important to
tasks in Natural Language Processing (NLP)
based on the accurate identi cation and
representation of meaning encoded in
language, such as information extraction, question
answering, and sentiment analysis.
      </p>
      <p>
        The annotation of negation is not a
trivial task. Negation acts at the syntactic,
semantic, and pragmatic levels
        <xref ref-type="bibr" rid="ref15">(Sandoval and
Salazar, 2013)</xref>
        ; and it exhibits asymmetry
between a uniform semantics and a
categorial polyvalence, appearing as a pre x, verb,
determiner, adverbial, particle, idiom,
construction, and more
        <xref ref-type="bibr" rid="ref2 ref5">(Bosque Mun~oz and
Gutierrez-Rexach, 2009; Herburger, 2018)</xref>
        .
Often, the scope, or span of utterance upon
which negation acts, is variable even within
the same sentence; some argue that scope
is di erent when determined at logical form
(LF) (the syntax/semantics interface) as
opposed to pragmatically
        <xref ref-type="bibr" rid="ref11">(Moeschler, 2010)</xref>
        ,
forcing resulting annotation to distinguish
between underlying semantics and more
general speaker meaning. Additionally,
negation may carry di erent force depending on
context; this is particularly true for
Spanish, in which negative concord may o er
gradations of meaning
        <xref ref-type="bibr" rid="ref8">(Jimenez-Zafra et al.,
2018b)</xref>
        . This may be seen by the variety of
negative expressions in the following
sentences, with negative linguistic elements in bold
        <xref ref-type="bibr" rid="ref4">(Espan~ola-RAE, 2010)</xref>
        :
      </p>
      <p>(1) Ella no dijo nada. `She did not say
anything.'</p>
      <p>(2) Nadie le hac a caso. `Nobody paid
attention to him/her.'</p>
      <p>(3) Ni de una forma ni de otra
consiguieron convencerla. `They couldn't convince her
one way or another.'</p>
      <p>(4) En mi vida he visto cosa igual. `I
haven't seen anything similar in my life.'
(5) No hables tanto. `Don't talk so much.'
(6) &gt;No son ya las dos? `Isn't it already
two o'clock?'</p>
      <p>Example (1) negates the action of
speaking; no functions as a negation cue, and
nada as a negative polarity item (NPI) within
the scope of the cue in the form of a negative
inde nite. (2) a rms the matter that no one,
signaled by the inde nite pronoun nadie,
participated in an attention-giving activity. (3)
exhibits two negative conjunctions, ni, that
describe an ine ective manner of convincing.
(4) exhibits an adverbial phrase, en mi vida,
that equates to the negative temporal adverb
nunca `never'. Finally, (5) and (6) utilize
negation to support speech acts: (5) as a
negative command, and (6) as a leading question.</p>
      <p>Given this complexity in mind, the process
of annotating negation in Spanish is
daunting. Nevertheless, it is essential to
annotate corpora with such information in
order to train algorithms to perform to
human capacity. Ideally, an annotation
framework could be designed to capture negation
patterns cross-linguistically. However, since
such patterns are quite varied and distinct,
the task here focuses on annotating negation
in Spanish as a rst step towards potentially
broader research.
2</p>
    </sec>
    <sec id="sec-2">
      <title>Task De nition</title>
      <p>
        NEGES task 1 seeks to reach an agreement
on the guidelines to follow for the
annotation of negation in Spanish building o
previous, domain-speci c guidelines used to
annotate corpora built around news, clinical
reports and product reviews
        <xref ref-type="bibr" rid="ref8">(Jimenez-Zafra et
al., 2018a)</xref>
        . Here I present brief summaries
and analyses of the guidelines presented in
the ve projects in question, followed by a
complete evaluation of the guidelines across
projects.
      </p>
    </sec>
    <sec id="sec-3">
      <title>Review of Guidelines</title>
      <sec id="sec-3-1">
        <title>UAM Spanish TreeBank</title>
        <p>
          <xref ref-type="bibr" rid="ref15">Sandoval and Salazar (2013)</xref>
          present ndings
from annotating the UAM Spanish Treebank
          <xref ref-type="bibr" rid="ref13">(Moreno et al., 2003)</xref>
          , which consists of 1501
sentences taken from newspapers (El Pa s
and Compra Muestra) and annotated
syntactically following guidelines from the Penn
Treebank
          <xref ref-type="bibr" rid="ref9">(Marcus, Marcinkiewicz, and
Santorini, 1993)</xref>
          . 10.67 % of the sentences were
found to contain negation (160 sentences).
        </p>
        <p>Given that the UAM Treebank is in xml
format, the authors adapt their annotation
scheme to be compatible. The authors
distinguish two levels of annotation for negation:
sentence-level and lexical. The former is
further divided into sentential and phrase-level
negation; the latter is divided into pronouns
and adverbs. Annotations thus mark both
negation cues and the scope of negation.</p>
        <p>The use of a syntactically annotated
corpus is helpful for the overall annotation
scheme; this is especially true given the
widespread knowledge of the Penn Treebank, as
well as the concurrent annotation of
lexical features that specify POS. Theoretically,
such annotation will help identify the lexical
category of negation marker as well as its
syntactic scope of negation. Algorithms trained
on such data will then be able to recognize
patterns of use of negation for varying parts
of speech and compare how syntactic scope
relates to semantic and pragmatic scope.</p>
        <p>
          As the authors themselves note, there is
much room for improvement in the
annotation scheme presented. First, the type of
negation is not speci ed apart from &lt;YES&gt;
and &lt;NO&gt; (to mark scope), and &lt;NEG&gt;
(to mark negation cues). This fails to
capture gradations of negation (i.e. whether it
is an assertion or a speech act that is
negated
          <xref ref-type="bibr" rid="ref11">(Moeschler, 2010)</xref>
          ), di erences in
intensity of negation
          <xref ref-type="bibr" rid="ref8">(Jimenez-Zafra et al., 2018b)</xref>
          ,
and the function of the NPI within the scope
of negation
          <xref ref-type="bibr" rid="ref5">(Herburger, 2018)</xref>
          . Additionally,
there is no speci cation of event negation,
negation expressed morphologically, or negative
discourse and sentence connectors.
3.2
        </p>
      </sec>
      <sec id="sec-3-2">
        <title>IxaMed-GS</title>
        <p>
          <xref ref-type="bibr" rid="ref14">Oronoz et al. (2015)</xref>
          focus on the identi
cation of entities and events in clinical
reports with the goal of automatic extraction
of adverse drug reaction events using machine
learning. Annotators were experts in
pharmacology and pharmacovigilance, a notable
difference from annotators trained in linguistics
for other corpora in their knowledge of the
clinical domain yet informal training in
identifying functional linguistic elements such as
negation.
        </p>
        <p>The authors collected 142,154 anonymous
discharge reports from the outpatient
consultations of the Galdake-Usansolo Hospital
from 2008 to 2012. Negation (and
speculation) was annotated as a modi er of a
disorder or drug, and individual cues were left
unmarked. As such, the text span sin otras
alergias medicamentosas \without other drug
allergies" would possess negation on alergias
medicamentosas while sin would be left
bare. This practice was used to maintain
consistency in the domain: a disease-entity such
as afebril \afebrile" was also marked as
negative in the absence of surrounding negative
lexical material.</p>
        <p>Four entity types were annotated:
diseases, allergies, drugs, and procedures. For
diseases and allergies, a distinction was made
between negated entity, speculated entity and
entity (for non-speculative and non-negated
entities). 2,362 diseases were annotated, out
of which 490 (20.75 %) were tagged as
negated diseases and 40 (1.69 %) as speculated
diseases. 404 allergy entities were identi ed,
of which 273 (67.57 %) were negated and 13
(3.22 %), speculated. The quality of the
annotation process was assessed by measuring the
inter-annotator agreement (IAA), which was
90.53 % for entities and 82.86 % for events.</p>
        <p>This annotation scheme needs to be
adapted in order to extend beyond the clinical
domain. First and foremost, negation needs to
be treated linguistically and broken down
into its components apart from the disorders
and drugs it acts on. Nevertheless, the
recognition that entities and events may be marked
for negation in distinct ways (i.e.
syntactically versus morphologically, as in the above
examples), as well as the intuition that some
entities and events may possess qualities of
negation without being explicitly marked for
it, is an important contribution to developing
a comprehensive annotation scheme.
Additionally, the distinction between negation and
speculation is important to consider, as the
linguistic interaction between negation and
modality is complicated, yet merits attention.
3.3</p>
      </sec>
      <sec id="sec-3-3">
        <title>SFU ReviewSP-NEG</title>
        <p>Jimenez-Zafra et al. (2018b) present the SFU
ReviewSP-NEG corpus, the rst Spanish
corpus that includes event negation as part of
the annotation scheme as well as the
annotation of discontinuous negation markers. The
corpus was also the rst to have de ned a
typology of patterns involving negation
speci c to Spanish. In this SFU ReviewSP-NEG
corpus, syntactic negation, scope, focus, and
event were annotated. Yet, annotations on
the event and on how negation a ects the
polarity of the words within its scope were
included for whether there is a complete change
in the polarity of the span in question, or an
increment or reduction of its value.</p>
        <p>
          The Spanish SFU Review corpus,
originally intended for work on sentiment
analysis, consists of 400 reviews extracted from
the website ciao.es. The reviews span 8 di
erent produce areas: cars, hotels, washing
machines, books, cell phones, music, computers,
and movies. For each product area there are
50 positive and 50 negative reviews, which
provides an informative context for how to
interpret the e ects of negation at the discourse
level
          <xref ref-type="bibr" rid="ref17">(Taboada, Anthony, and Voll, 2006)</xref>
          .
        </p>
        <p>For the SFU ReviewSP-NEG corpus, each
review was automatically annotated at the
token level with POS-tags and lemmas;
negation cues and their corresponding scopes and
events were manually annotated at the
sentence level. The annotations were performed
by two senior researchers with in-depth
experience in corpus annotation who supervised
the two trained annotators who carried out
the annotation task. The nal corpus is
composed of 9,455 sentences, out of which 3,022
sentences (31.97 %) contain at least one
negation marker. The Kappa coe cient for IAA
was of 0.97 for negation cues, 0.95 for negated
events and 0.94 for scopes.</p>
        <p>
          Similar to the UAM Spanish Treebank,
the annotation scheme for SFU
ReviewSPNEG ought to be broadened to include
morphological, lexical, and discourse-oriented
negation. Nevertheless, the inclusion of
gradient interpretations of negation within its
scope captures the subtle meaning di
erences that negation markers, and their
combinations, may produce. In fact, this gradation
of meaning may be expanded to include even
ner-grained distinctions of meaning in
future work.
3.4
          <xref ref-type="bibr" rid="ref3">Cruz et al. (2017)</xref>
          annotate a corpus
composed of 604 clinical reports from the
Virgen del Roc o Hospital in Sevilla, Spain. 276
of this clinical documents correspond to
radiology reports and 328 to the personal
history of anamnesis reports written in free text.
Two domain expert annotators closely
followed the Thyme corpus guidelines
          <xref ref-type="bibr" rid="ref16">(Styler IV
et al., 2014)</xref>
          , developed for the annotation of
English clinical record. In the anamnesis
reports, 1,079 sentences (35.20 %) were found
to contain negations out of 3,065 sentences.
On the other hand, 1,219 sentences (22.80 %)
out of 5,347 sentences were annotated with
negations in the radiology reports. The Dice
coe cient for IAA was higher than 0.94 for
negation markers and higher than 0.72 for
negated events.
        </p>
        <p>
          In this corpus, all types of negation
were annotated: syntactic, morphological (a
xal negation), and lexical. Negation was
marked both linguistically and as a modi er of a
disorder of a drug, i.e. whether or not the
drug was e ective. Similar to
          <xref ref-type="bibr" rid="ref14">(Oronoz et al.,
2015)</xref>
          , full words that expressed negative
polarity were marked in their entirety (afebril
\afebrile") rather than just their negative
afx (a-).
        </p>
        <p>Either due to the domain of application or
presentation format, the annotation
guidelines presented for the UHU-HUVR corpus
often seemed unclear. For example, non-clinical
experts may have trouble di erentiated
negative test results from negative clinical events,
as the annotation scheme does. Additionally,
while some negation a xes are marked as
negating symptoms (a-febril \afebrile") others
are not, considered positive symptoms unto
themselves (in-continencia urinaria \urinary
incontinence"). Finally, the authors'
treatment of coordination as a single unit of
negation ought to be revised.
3.5</p>
      </sec>
      <sec id="sec-3-4">
        <title>IULA Spanish Clinical Record</title>
        <p>
          The IULA Spanish Clinical Record corpus
          <xref ref-type="bibr" rid="ref10">(Marimon, Vivaldi, and Bel, 2017)</xref>
          contains
300 anonymized clinical records from several
services of one of the main hospitals in
Barcelona, Spain. The corpus was annotated with
negation markers and their scopes with the
ultimate goal of extracting factual
knowledge from textual data; subgoals included
automatic encoding of clinical records; diagnosis
support; term extraction; and general study
of clinical texts. The corpus contains 3,194
sentences, out of which 1,093 (34.22 %) were
annotated with negation cues. In this corpus,
syntactic negation and lexical negation
were annotated; morphological negation was
excluded. Annotators were three computational
linguists annotators, advised by a clinician.
        </p>
        <p>
          Annotators did not include the negation
cue nor the subject in its scope as part of
annotation, unless the subject was located
after the verb. This practice seems to be based
on linear order alone, and does not take into
account the semantics of scope nor the
possibility of backwards scope
          <xref ref-type="bibr" rid="ref6">(Hoeksema, 2000)</xref>
          .
Additionally, it seems necessary to mark the
negation cue in some manner to signal
where the negation is coming from in order to
better train algorithms. This aside, the
annotation of scope seemed to be quite precise
(for example, annotating scope over verb
phrase versus just over adverb), and the project
on the whole was presented very thoroughly.
The authors note that they did not annotate
certain verbs with negative polarity
(desaparecer, retirar, suspenderse, eradicar, negar )
on the basis that such verbs still denoted
factuality. Such interactions between negation
and factuality seems worth while to discuss
for future annotation e orts.
        </p>
        <p>
          Similar to
          <xref ref-type="bibr" rid="ref14">(Oronoz et al., 2015)</xref>
          and
          <xref ref-type="bibr" rid="ref3">(Cruz
et al., 2017)</xref>
          , the IULA was biased towards
the clinical domain. Thus, teasing apart the
e ects of negative a xation (for example, in
the adjective asintomatico \asymptomatic")
will be necessary for future work to both be
faithful to linguistic negation yet still express
the desired level of factuality for clinical use.
4
        </p>
      </sec>
    </sec>
    <sec id="sec-4">
      <title>Discussion and Preliminary</title>
    </sec>
    <sec id="sec-5">
      <title>Proposal</title>
      <p>With any linguistic annotation task, striking
a balance between linguistic precision and
annotation feasibility is an inevitable and
essential question. For the annotation of negation
in Spanish, several components of the
proposals discussed above may be combined into
a set of complex guidelines that is both
linguistically accurate and domain neutral. Here
I summarize the main components I nd to
be worth annotating.</p>
      <p>
        Most basically, the semantics of negation
is represented (and ought to be annotated)
through (i) the identi cation of the negation
cue (the lexical element expressing
negation); (ii) its scope (the text section that
is negated); (iii) its focus (that part of the
scope that is prominently or explicitly
negated); and, if present, (iv) its reinforcement
(an auxiliary negation or NPI)
        <xref ref-type="bibr" rid="ref1 ref10">(Altuna,
Minard, and Speranza, 2017)</xref>
        . This may
be understood in an example such as the
following:
(7) Juan no come [carne] sino verduras.
      </p>
      <p>The negation cue (no) is represented in
bold; the scope (come carne sino verduras )
is in italics; the focus (carner) is in brackets;
and the NPI (sino) in bold and italics.
Negation markers that do not carry negative
polarity semantic information (nada mas \as
soon as") can be marked as such (for
example, as &lt;noneg &gt; instead of &lt;neg &gt;
(JimenezZafra et al., 2018b).</p>
      <p>
        Following
        <xref ref-type="bibr" rid="ref12">(Morante, Schrauwen, and
Daelemans, 2011)</xref>
        , negation cues could be
limited to just adverbs (no, nadie, ninguno,
nunca/jamas). However, it seems that
annotating morphological cues (pre xes such as a-,
in/im-, de(s)-, anti-) as well as negative
polarity verbs (retirar, deaparecer, suspenderse,
etc.) is worth while for application to clinical
domains.
      </p>
      <p>
        This could be accomplished with both the
annotation of the cues themselves and a
linking to some sort of lexical de nition or
modal e ect of the cue, as some combination
of
        <xref ref-type="bibr" rid="ref10">(Marimon, Vivaldi, and Bel, 2017)</xref>
        and
        <xref ref-type="bibr" rid="ref8">(Jimenez-Zafra et al., 2018b)</xref>
        could
produce. Figure 2 of
        <xref ref-type="bibr" rid="ref8">(Jimenez-Zafra et al., 2018b)</xref>
        seems adequately suited to capturing the
layers of complexity of negation. This, in
combination with the distinction a NegPred (for
(1), comer `to eat'), Negmarker (for (1),
no `does not'), and NegPolItem (for (1),
sino `but') from
        <xref ref-type="bibr" rid="ref10">(Marimon, Vivaldi, and Bel,
2017)</xref>
        could provide substantial coverage. It
seems that an additional feature such as
[+/realis] may be helpful to distinguish levels of
factuality of events in question, as well.
      </p>
      <p>
        As a closing point, the Brat annotation
tool
        <xref ref-type="bibr" rid="ref10">(Marimon, Vivaldi, and Bel, 2017)</xref>
        seems
suitable for any comprehensive annotation
task involving negation. The multi-colored,
layered format is accessible online,
facilitating collaborative annotation e orts and
the potential implementation of pilot
annotation tasks to gauge inter-annotator
agreement (IAA) as guidelines are developed.
      </p>
    </sec>
    <sec id="sec-6">
      <title>Conclusion</title>
      <p>This paper has presented an analysis and
evaluation of existing guidelines for the
annotation of negation in several domain-speci c
Spanish corpora. A preliminary proposal is
given for how to combine linguistically
accurate and precise annotation with more
practical concerns regarding domain of application
and ease of annotation. Future work points
in particular towards re ning the subtle
meaning e ects negation can have on words and
phrase meaning, as well as its interaction
with modality for ultimate interpretation of
event factuality.</p>
    </sec>
    <sec id="sec-7">
      <title>Acknowledgments</title>
      <p>Many thanks to Elena Herburger, one of my
doctoral advisors and an expert on negation.
Thanks as well to Claire Bonial and the team
at Army Research Lab (ARL) for supporting
the work behind this paper.</p>
    </sec>
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