2 research outputs found
Extração de informação aplicada a comentários da área do turismo
Motivation: The primary motivation of this dissertation was to show
that it is possible to construct an NLP solution for the Portuguese
language capable of helping in the hotel industry.
Objective(s): The main objective of this dissertation was to extract
useful information from hotel commentaries using NLP.
Method: An NLP pipeline was created to extract useful information,
and then sentimental analyse was used to characterise that information.
Results: After processing all the commentaries of a hotel was possible
to extract what people like or dislike about it.
Conclusions: The two main conclusions were that is possible to create
a Portuguese NLP pipeline for the hotel industry, and that is possible
to extract useful information from thousands of commentaries.Motivação: A principal motivação por trás desta tese foi mostrar que
é possível escrever um programa para NLP usando a língua portuguesa.
Objetivo(s): O principal objetivo desta tese foi extrair informação
hotel dos comentários feitos a hotéis usando NLP.
Método: Foi criado um pipeline de NLP para extrair informação útil.
Depois foi usado análise de sentimentos para caracterizar essa informação.
Resultados: Depois de todos os comentários serem processados foi
possível descobrir o que as pessoas gostam ou desgostam sobre um
hotel.
Conclusões: As duas principais conclusões foram que era possível
fazer NLP em português e que era possível extrair informação útil de
milhar de comentários.Mestrado em Engenharia Eletrónica e Telecomunicaçõe
Incorporating pronoun function into statistical machine translation
Pronouns are used frequently in language, and perform a range of functions.
Some pronouns are used to express coreference, and others are not. Languages
and genres differ in how and when they use pronouns and this poses a problem
for Statistical Machine Translation (SMT) systems (Le Nagard and Koehn,
2010; Hardmeier and Federico, 2010; Novák, 2011; Guillou, 2012; Weiner, 2014;
Hardmeier, 2014). Attention to date has focussed on coreferential (anaphoric)
pronouns with NP antecedents, which when translated from English into a language
with grammatical gender, must agree with the translation of the head of
the antecedent. Despite growing attention to this problem, little progress has
been made, and little attention has been given to other pronouns.
The central claim of this thesis is that pronouns performing different functions
in text should be handled differently by SMT systems and when evaluating
pronoun translation. This motivates the introduction of a new framework to
categorise pronouns according to their function: Anaphoric/cataphoric reference,
event reference, extra-textual reference, pleonastic, addressee reference, speaker
reference, generic reference, or other function. Labelling pronouns according to
their function also helps to resolve instances of functional ambiguity arising from
the same pronoun in the source language having multiple functions, each with different
translation requirements in the target language. The categorisation framework
is used in corpus annotation, corpus analysis, SMT system development and
evaluation.
I have directed the annotation and conducted analyses of a parallel corpus of
English-German texts called ParCor (Guillou et al., 2014), in which pronouns
are manually annotated according to their function. This provides a first step
toward understanding the problems that SMT systems face when translating pronouns.
In the thesis, I show how analysis of manual translation can prove useful in
identifying and understanding systematic differences in pronoun use between two
languages and can help inform the design of SMT systems. In particular, the analysis
revealed that the German translations in ParCor contain more anaphoric and
pleonastic pronouns than their English originals, reflecting differences in pronoun
use. This raises a particular problem for the evaluation of pronoun translation.
Automatic evaluation methods that rely on reference translations to assess pronoun
translation, will not be able to provide an adequate evaluation when the
reference translation departs from the original source-language text. I also show
how analysis of the output of state-of-the-art SMT systems can reveal how well
current systems perform in translating different types of pronouns and indicate
where future efforts would be best directed. The analysis revealed that biases
in the training data, for example arising from the use of “it” and “es” as both
anaphoric and pleonastic pronouns in both English and German, is a problem
that SMT systems must overcome. SMT systems also need to disambiguate the
function of those pronouns with ambiguous surface forms so that each pronoun
may be translated in an appropriate way.
To demonstrate the value of this work, I have developed an automated post-editing
system in which automated tools are used to construct ParCor-style annotations
over the source-language pronouns. The annotations are then used to resolve
functional ambiguity for the pronoun “it” with separate rules applied to the
output of a baseline SMT system for anaphoric vs. non-anaphoric instances. The
system was submitted to the DiscoMT 2015 shared task on pronoun translation
for English-French. As with all other participating systems, the automatic post-editing
system failed to beat a simple phrase-based baseline. A detailed analysis,
including an oracle experiment in which manual annotation replaces the automated
tools, was conducted to discover the causes of poor system performance.
The analysis revealed that the design of the rules and their strict application to
the SMT output are the biggest factors in the failure of the system.
The lack of automatic evaluation metrics for pronoun translation is a limiting
factor in SMT system development. To alleviate this problem, Christian Hardmeier
and I have developed a testing regimen called PROTEST comprising (1)
a hand-selected set of pronoun tokens categorised according to the different problems
that SMT systems face and (2) an automated evaluation script. Pronoun
translations can then be automatically compared against a reference translation,
with mismatches referred for manual evaluation. The automatic evaluation was
applied to the output of systems submitted to the DiscoMT 2015 shared task
on pronoun translation. This again highlighted the weakness of the post-editing
system, which performs poorly due to its focus on producing gendered pronoun
translations, and its inability to distinguish between pleonastic and event reference
pronouns