134 research outputs found
A Large-Scale Comparison of Historical Text Normalization Systems
There is no consensus on the state-of-the-art approach to historical text
normalization. Many techniques have been proposed, including rule-based
methods, distance metrics, character-based statistical machine translation, and
neural encoder--decoder models, but studies have used different datasets,
different evaluation methods, and have come to different conclusions. This
paper presents the largest study of historical text normalization done so far.
We critically survey the existing literature and report experiments on eight
languages, comparing systems spanning all categories of proposed normalization
techniques, analysing the effect of training data quantity, and using different
evaluation methods. The datasets and scripts are made publicly available.Comment: Accepted at NAACL 201
Text Normalisation of Dialectal Finnish
Tekstin normalisointi on prosessi, jossa epästandardia kirjoitettua kieltä muutetaan standardisoituun muotoon. Murteet ovat yksi esimerkki epästandardista kielestä, joka voi poiketa huomattavastikin standardisoidusta yleiskielestä. Lisäksi suomen kieli on ortografialtaan varsin pitkälti foneemista, minkä ansiosta myös puhutun kielen ominaispiirteet on mahdollista tuoda esille kirjoitetussa muodossa. Etenkin epävirallisilla alustoilla ja arkikielisessä kontekstissa, kuten sosiaalisessa mediassa, suomen kielen puhujat saattavat kirjoittaa sanat kuten ääntäisivät ne normaalisti puhuessaan. Tällaista epästandardista kielestä koostuvaa aineistoa voi löytää myös luonnollisen kielen käsittelyn tarpeisiin esimerkiksi Twitteristä. Perinteiselle yleiskieliselle tekstiaineistolle suunnatut luonnollisen kielen käsittelyn työkalut eivät kuitenkaan välttämättä saavuta toivottavia tuloksia puhekieliselle aineistolle sovellettuna, jolloin ratkaisuna voidaan käyttää välivaiheena tekstin normalisointia. Normalisointiprosessissa syötteenä käytettävä puhekielinen tai muutoin epästandardia kieltä sisältävä teksti muutetaan standardisoituun kirjoitusasuun, jota luonnollisen kielen käsittelyn työkalut paremmin ymmärtävät.
Tämä työ pohjaa aiempaan tutkimukseen, jota on tehty suomen murteiden normalisoinnin parissa. Aiemmissa tutkimuksissa on todettu, että merkkipohjaiset BRNN-neuroverkkomallit (Bidirectional Recurrent Neural Nerwork) saavuttavat hyviä tuloksia suomen kielen murteiden normalisoinnissa, kun syötteenä käytetään sanoja kolmen kappaleen lohkoissa. Tämä tarkoittaa, että järjestelmä saa syötteenä kerrallaan kolmen sanan joukon, ja jokainen sana on edelleen pilkottu välilyönnein eroteltuihin kirjoitusmerkkeihin. Tässä työssä pyrittiin käyttämään samoja metodeja ja aineistoa kuin aiemmassa tutkimuksessa, jotta tulokset olisivat vertailukelpoisia. Aineistona on käytetty Kotimaisten kielten keskuksen ylläpitämää Suomen kielen näytteitä -korpusta, ja normalisointiin on käytetty OpenNMT-nimistä avoimen lähdekoodin kirjastoa. Työssä toteutetuista kokeiluista saadut tulokset näyttävät vahvistavan aiempien tutkimustulosten pohjalta tehdyt löydökset, mutta lisäksi on viitteitä siitä, että neuroverkkomallit saattaisivat pidemmistä lohkoista koostuvista syötteistä. BRNN-mallin lisäksi työssä kokeillaan myös muita neuroverkkoarkkitehtuureja, mutta vertailtaessa sanavirheiden suhdelukua mittaavaa WER-arvoa (Word Error Rate) voidaan todeta, että BRNN-malli suoriutuu normalisointitehtävästä muita neuroverkkoarkkitehtuureja paremmin
Noise or music? Investigating the usefulness of normalisation for robust sentiment analysis on social media data
In the past decade, sentiment analysis research has thrived, especially on social media. While this data genre is suitable to extract opinions and sentiment, it is known to be noisy. Complex normalisation methods have been developed to transform noisy text into its standard form, but their effect on tasks like sentiment analysis remains underinvestigated. Sentiment analysis approaches mostly include spell checking or rule-based normalisation as preprocess- ing and rarely investigate its impact on the task performance. We present an optimised sentiment classifier and investigate to what extent its performance can be enhanced by integrating SMT-based normalisation as preprocessing. Experiments on a test set comprising a variety of user-generated content genres revealed that normalisation improves sentiment classification performance on tweets and blog posts, showing the model’s ability to generalise to other data genres
A transformer-based standardisation system for Scottish Gaelic
The transition from rule-based to neural-based architectures has made it more difficult for low-resource languages like Scottish Gaelic to participate in modern language technologies. The performance of deep-learning approaches correlates with the availability of training data, and low-resource languages have limited data reserves by definition. Historical and non-standard orthographic texts could be used to supplement training data, but manual conversion of these texts is expensive and timeconsuming. This paper describes the development of a neuralbased orthographic standardisation system for Scottish Gaelic and compares it to an earlier rule-based system. The best performance yielded a precision of 93.92, a recall of 92.20 and a word error rate of 11.01. This was obtained using a transformerbased mixed teacher model which was trained with augmented dat
Few-Shot and Zero-Shot Learning for Historical Text Normalization
Historical text normalization often relies on small training datasets. Recent
work has shown that multi-task learning can lead to significant improvements by
exploiting synergies with related datasets, but there has been no systematic
study of different multi-task learning architectures. This paper evaluates
63~multi-task learning configurations for sequence-to-sequence-based historical
text normalization across ten datasets from eight languages, using
autoencoding, grapheme-to-phoneme mapping, and lemmatization as auxiliary
tasks. We observe consistent, significant improvements across languages when
training data for the target task is limited, but minimal or no improvements
when training data is abundant. We also show that zero-shot learning
outperforms the simple, but relatively strong, identity baseline.Comment: Accepted at DeepLo-201
From Arabic user-generated content to machine translation: integrating automatic error correction
With the wide spread of the social media and online forums,
individual users have been able to actively participate in the generation
of online content in different languages and dialects. Arabic is one of the
fastest growing languages used on Internet, but dialects (like Egyptian
and Saudi Arabian) have a big share of the Arabic online content. There
are many differences between Dialectal Arabic and Modern Standard
Arabic which cause many challenges for Machine Translation of informal
Arabic language. In this paper, we investigate the use of Automatic Error Correction method to improve the quality of Arabic User-Generated
texts and its automatic translation. Our experiments show that the new
system with automatic correction module outperforms the baseline system by nearly 22.59% of relative improvement
Plague Dot Text:Text mining and annotation of outbreak reports of the Third Plague Pandemic (1894-1952)
The design of models that govern diseases in population is commonly built on
information and data gathered from past outbreaks. However, epidemic outbreaks
are never captured in statistical data alone but are communicated by
narratives, supported by empirical observations. Outbreak reports discuss
correlations between populations, locations and the disease to infer insights
into causes, vectors and potential interventions. The problem with these
narratives is usually the lack of consistent structure or strong conventions,
which prohibit their formal analysis in larger corpora. Our interdisciplinary
research investigates more than 100 reports from the third plague pandemic
(1894-1952) evaluating ways of building a corpus to extract and structure this
narrative information through text mining and manual annotation. In this paper
we discuss the progress of our ongoing exploratory project, how we enhance
optical character recognition (OCR) methods to improve text capture, our
approach to structure the narratives and identify relevant entities in the
reports. The structured corpus is made available via Solr enabling search and
analysis across the whole collection for future research dedicated, for
example, to the identification of concepts. We show preliminary visualisations
of the characteristics of causation and differences with respect to gender as a
result of syntactic-category-dependent corpus statistics. Our goal is to
develop structured accounts of some of the most significant concepts that were
used to understand the epidemiology of the third plague pandemic around the
globe. The corpus enables researchers to analyse the reports collectively
allowing for deep insights into the global epidemiological consideration of
plague in the early twentieth century.Comment: Journal of Data Mining & Digital Humanities 202
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