891 research outputs found
Text as scene: discourse deixis and bridging relations
En este artículo se presenta un nuevo marco, “el texto como escena”, que establece
las bases para la anotación de dos relaciones de correferencia: la deixis discursiva y las
relaciones de bridging. La incorporación de lo que llamamos escenas textuales y contextuales
proporciona unas directrices de anotación más flexibles, que diferencian claramente entre tipos
de categorías generales. Un marco como éste, capaz de tratar la deixis discursiva y las
relaciones de bridging desde una perspectiva común, tiene como objetivo mejorar el bajo grado
de acuerdo entre anotadores obtenido por esquemas de anotación anteriores, que son incapaces
de captar las referencias vagas inherentes a estos dos tipos de relaciones. Las directrices aquí
presentadas completan el esquema de anotación diseñado para enriquecer el corpus español
CESS-ECE con información correferencial y así construir el corpus CESS-Ancora.This paper presents a new framework, “text as scene”, which lays the foundations for
the annotation of two coreferential links: discourse deixis and bridging relations. The
incorporation of what we call textual and contextual scenes provides more flexible annotation
guidelines, broad type categories being clearly differentiated. Such a framework that is capable
of dealing with discourse deixis and bridging relations from a common perspective aims at
improving the poor reliability scores obtained by previous annotation schemes, which fail to
capture the vague references inherent in both these links. The guidelines presented here
complete the annotation scheme designed to enrich the Spanish CESS-ECE corpus with
coreference information, thus building the CESS-Ancora corpus.This paper has been supported by the FPU
grant (AP2006-00994) from the Spanish
Ministry of Education and Science. It is based
on work supported by the CESS-ECE
(HUM2004-21127), Lang2World (TIN2006-
15265-C06-06), and Praxem (HUM2006-
27378-E) projects
AnaPro, Tool for Identification and Resolution of Direct Anaphora in Spanish
Introduction Anaphora is a relation of coreference between linguistic terms. According to Webster’s dictionary: “It is the use of a grammatical substitute (as a pronoun or a pro-verb) to refer to the denotation of a preceding word or group of words; also : the relation between a grammatical substitute and its antecedent.” Therefore, anaphora is a discourse relation. Anaphora resolution is very important in Natural Language Processing (NLP). This work is part of Project OM* (Ontology Merging), which seeks to build a large ontology by fusing smaller ontologies extracted from textual documents. An important part of the project is to analyze the sentences in a document with the goal to transform that text into an ontology that comprises its contents. A brief description of Project OM* follows.AnaPro is software that solves direct anaphora in Spanish, specifically pronouns: it finds the noun or group of words to which the pronoun refers. It locates in the previous sentenc es the referent or antecedent which the pronoun replaces. An example of a direct anaphora solved is the pronoun “ he” in the sentence “He is sad.” Much of the work on anaphora has been done for texts in English; thus , we specifically focus on Spanish documents. AnaPro directly supports text analys is (to understand what a document says ), a non trivial task since there are different writing styles, references, idiomatic expressions, etc. The problem grows if t he analyzer is a computer, because they lack “common sense” (which persons possess) . Hence, before text analysis, its preprocessing is required, in order to assign tags (noun, verb,...) to each word, find the stems, disambiguate nouns, verbs, prepositions, identify colloquial expressions, i dentify and resolve anaphor a, among other chores. AnaPro works for Spanish sentences. It is a novel procedure, since it is automatic (no user intervenes during the resolution) and it does not need dictionaries. It employs heu ristics procedures to discover the semantics and help in the decisions; they are rather easy to implement and use li mited knowledge. Nevertheless, its results are good (81% of correct answers, at least). However, more tests will give a better idea of its goodness.Authors I.T. and E.V. would like to acknowledge ESCOM-IPN, where they defended their thesis, #20110083 , which gives a more detailed description of AnaPro. Work herein reported was partially sponsored by CONACYT Grant #128163 (Project OM*), by IPN and by SNI and UAEM
Translation of Pronominal Anaphora between English and Spanish: Discrepancies and Evaluation
This paper evaluates the different tasks carried out in the translation of
pronominal anaphora in a machine translation (MT) system. The MT interlingua
approach named AGIR (Anaphora Generation with an Interlingua Representation)
improves upon other proposals presented to date because it is able to translate
intersentential anaphors, detect co-reference chains, and translate Spanish
zero pronouns into English---issues hardly considered by other systems. The
paper presents the resolution and evaluation of these anaphora problems in AGIR
with the use of different kinds of knowledge (lexical, morphological,
syntactic, and semantic). The translation of English and Spanish anaphoric
third-person personal pronouns (including Spanish zero pronouns) into the
target language has been evaluated on unrestricted corpora. We have obtained a
precision of 80.4% and 84.8% in the translation of Spanish and English
pronouns, respectively. Although we have only studied the Spanish and English
languages, our approach can be easily extended to other languages such as
Portuguese, Italian, or Japanese
Computational Approach to Anaphora Resolution in Spanish Dialogues
This paper presents an algorithm for identifying noun-phrase antecedents of
pronouns and adjectival anaphors in Spanish dialogues. We believe that anaphora
resolution requires numerous sources of information in order to find the
correct antecedent of the anaphor. These sources can be of different kinds,
e.g., linguistic information, discourse/dialogue structure information, or
topic information. For this reason, our algorithm uses various different kinds
of information (hybrid information). The algorithm is based on linguistic
constraints and preferences and uses an anaphoric accessibility space within
which the algorithm finds the noun phrase. We present some experiments related
to this algorithm and this space using a corpus of 204 dialogues. The algorithm
is implemented in Prolog. According to this study, 95.9% of antecedents were
located in the proposed space, a precision of 81.3% was obtained for pronominal
anaphora resolution, and 81.5% for adjectival anaphora
An Empirical Approach to Temporal Reference Resolution
This paper presents the results of an empirical investigation of temporal
reference resolution in scheduling dialogs. The algorithm adopted is primarily
a linear-recency based approach that does not include a model of global focus.
A fully automatic system has been developed and evaluated on unseen test data
with good results. This paper presents the results of an intercoder reliability
study, a model of temporal reference resolution that supports linear recency
and has very good coverage, the results of the system evaluated on unseen test
data, and a detailed analysis of the dialogs assessing the viability of the
approach.Comment: 13 pages, latex using aclap.st
Review of coreference resolution in English and Persian
Coreference resolution (CR) is one of the most challenging areas of natural
language processing. This task seeks to identify all textual references to the
same real-world entity. Research in this field is divided into coreference
resolution and anaphora resolution. Due to its application in textual
comprehension and its utility in other tasks such as information extraction
systems, document summarization, and machine translation, this field has
attracted considerable interest. Consequently, it has a significant effect on
the quality of these systems. This article reviews the existing corpora and
evaluation metrics in this field. Then, an overview of the coreference
algorithms, from rule-based methods to the latest deep learning techniques, is
provided. Finally, coreference resolution and pronoun resolution systems in
Persian are investigated.Comment: 44 pages, 11 figures, 5 table
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