141 research outputs found
New Treebank or Repurposed? On the Feasibility of Cross-Lingual Parsing of Romance Languages with Universal Dependencies
This is the final peer-reviewed manuscript that was accepted for publication in Natural Language Engineering. Changes resulting from the publishing process, such as editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document.[Abstract] This paper addresses the feasibility of cross-lingual parsing with Universal Dependencies (UD) between Romance languages, analyzing its performance when compared to the use of manually annotated resources of the target languages. Several experiments take into account factors such as the lexical distance between the source and target varieties, the impact of delexicalization, the combination of different source treebanks or the adaptation of resources to the target language, among others. The results of these evaluations show that the direct application of a parser from one Romance language to another reaches similar labeled attachment score (LAS) values to those obtained with a manual annotation of about 3,000 tokens in the target language, and unlabeled attachment score (UAS) results equivalent to the use of around 7,000 tokens, depending on the case. These numbers can noticeably increase by performing a focused selection of the source treebanks. Furthermore, the removal of the words in the training corpus (delexicalization) is not useful in most cases of cross-lingual parsing of Romance languages. The lessons learned with the performed experiments were used to build a new UD treebank for Galician, with 1,000 sentences manually corrected after an automatic cross-lingual annotation. Several evaluations in this new resource show that a cross-lingual parser built with the best combination and adaptation of the source treebanks performs better (77 percent LAS and 82 percent UAS) than using more than 16,000 (for LAS results) and more than 20,000 (UAS) manually labeled tokens of Galician.Ministerio de Economía y Competitividad; FJCI-2014-22853Ministerio de Economía y Competitividad; FFI2014-51978-C2-1-RMinisterio de Economía y Competitividad; FFI2014-51978-C2-2-
Multi-facet rating of online hotel reviews: issues, methods and experiments
Online product reviews are becoming increasingly popular, and are being
used more and more frequently by consumers in order to choose among
competing products. Tools that rank competing products in terms of the
satisfaction of consumers that have purchased the product before, are thus
also becoming popular. We tackle the problem of rating (i.e., attributing
a numerical score of satisfaction to) consumer reviews based on their tex-
tual content. In this work we focus on multi-facet rating of hotel reviews,
i.e., on the case in which the review of a hotel must be rated several times,
according to several aspects (e.g., cleanliness, dining facilities, centrality of
location). We explore several aspects of the problem, including the vectorial
representation of the text based on sentiment analysis, collocation analysis,
and feature selection for ordinal-regression learning. We present the results
of experiments conducted on a corpus of approximately 15,000 hotel reviews
that we have crawled from a popular hotel review site
Calculating the error percentage of an automated part-of-speech tagger when analyzing Estonian learner English: an empirical analysis
Teksti sõnaliikideks jaotamine sündis koos lingvistikaga, kuid selle protsessi automatiseerimine on muutunud võimalikuks alles viimastel kümnenditel ning seda tänu arvutite võimsuse kasvule. Tekstitöötluse algoritmid on alates sellest ajast iga aastaga üha paranenud. Selle magistritöö raames pannakse üks selle valdkonna lipulaevadest proovile korpuse peal, mis hõlmab eesti keelt emakeelena kõnelevate inglise keele õppijate tekste (TCELE korpus). Korpuse suurus on antud hetkel ca. 25 000 sõna (127 kirjalikku esseed) ning 11 transkribeeritud intervjuud (~100 minutit). Eesmärk on hinnata TCELE ja muude sarnaste korpuste veaprotsenti. Töö esimeses osas tutvustatakse lugejale korpuse kokkupanemist, annoteerimist ja väljavõtet (ingl. retrieval ) ning antakse ülevaade sõnaliikide määramisest ja veaprotsendist. Pärast seda antakse ülevaade varasematest uuringutest ning vastatakse muuhulgas, järgnevatele küsimustele: mida on eelnevalt tehtud? Mis olid uuringute leiud? Millised automaatsed märgendajad (ingl. taggers) ja sõnaliikide loendeid (ingl. tagset ) kasutati?http://www.ester.ee/record=b5142572*es
A time-sensitive historical thesaurus-based semantic tagger for deep semantic annotation
Automatic extraction and analysis of meaning-related information from natural language data has been an important issue in a number of research areas, such as natural language processing (NLP), text mining, corpus linguistics, and data science. An important aspect of such information extraction and analysis is the semantic annotation of language data using a semantic tagger. In practice, various semantic annotation tools have been designed to carry out different levels of semantic annotation, such as topics of documents, semantic role labeling, named entities or events. Currently, the majority of existing semantic annotation tools identify and tag partial core semantic information in language data, but they tend to be applicable only for modern language corpora. While such semantic analyzers have proven useful for various purposes, a semantic annotation tool that is capable of annotating deep semantic senses of all lexical units, or all-words tagging, is still desirable for a deep, comprehensive semantic analysis of language data. With large-scale digitization efforts underway, delivering historical corpora with texts dating from the last 400 years, a particularly challenging aspect is the need to adapt the annotation in the face of significant word meaning change over time. In this paper, we report on the development of a new semantic tagger (the Historical Thesaurus Semantic Tagger), and discuss challenging issues we faced in this work. This new semantic tagger is built on existing NLP tools and incorporates a large-scale historical English thesaurus linked to the Oxford English Dictionary. Employing contextual disambiguation algorithms, this tool is capable of annotating lexical units with a historically-valid highly fine-grained semantic categorization scheme that contains about 225,000 semantic concepts and 4,033 thematic semantic categories. In terms of novelty, it is adapted for processing historical English data, with rich information about historical usage of words and a spelling variant normalizer for historical forms of English. Furthermore, it is able to make use of knowledge about the publication date of a text to adapt its output. In our evaluation, the system achieved encouraging accuracies ranging from 77.12% to 91.08% on individual test texts. Applying time-sensitive methods improved results by as much as 3.54% and by 1.72% on average
Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)
Peer reviewe
Application of a POS Tagger to a Novel Chronological Division of Early Modern German Text
This paper describes the application of a part-of-speech tagger to a particular configuration of historical German documents. Most natural language processing (NLP) is done on contemporary documents, and historical documents can present difficulties for these tools. I compared the performance of a single high-quality tagger on two stages of historical German (Early Modern German) materials. I used the TnT (Trigrams 'n' Tags) tagger, a probabilistic tagger developed by Thorsten Brants in a 2000 paper. I applied this tagger to two subcorpora which I derived from the University of Manchester's GerManC corpus, divided by date of creation of the original document, with each one used for both training and testing. I found that the earlier half, from a period with greater variability in the language, was significantly more difficult to tag correctly. The broader tag categories of punctuation and "other" were overrepresented in the errors.Master of Science in Information Scienc
An Urdu semantic tagger - lexicons, corpora, methods and tools
Extracting and analysing meaning-related information from natural language data has attracted the attention of researchers in various fields, such as Natural Language Processing (NLP), corpus linguistics, data sciences, etc. An important aspect of such automatic information extraction and analysis is the semantic annotation of language data using semantic annotation tool (a.k.a semantic tagger). Generally, different semantic annotation tools have been designed to carry out various levels of semantic annotations, for instance, sentiment analysis, word sense disambiguation, content analysis, semantic role labelling, etc. These semantic annotation tools identify or tag partial core semantic information of language data, moreover, they tend to be applicable only for English and other European languages. A semantic annotation tool that can annotate semantic senses of all lexical units (words) is still desirable for the Urdu language based on USAS (the UCREL Semantic Analysis System) semantic taxonomy, in order to provide comprehensive semantic analysis of Urdu language text. This research work report on the development of an Urdu semantic tagging tool and discuss challenging issues which have been faced in this Ph.D. research work. Since standard NLP pipeline tools are not widely available for Urdu, alongside the Urdu semantic tagger a suite of newly developed tools have been created: sentence tokenizer, word tokenizer and part-of-speech tagger. Results for these proposed tools are as follows: word tokenizer reports of 94.01\%, and accuracy of 97.21\%, sentence tokenizer shows F of 92.59\%, and accuracy of 93.15\%, whereas, POS tagger shows an accuracy of 95.14\%. The Urdu semantic tagger incorporates semantic resources (lexicon and corpora) as well as semantic field disambiguation methods. In terms of novelty, the NLP pre-processing tools are developed either using rule-based, statistical, or hybrid techniques. Furthermore, all semantic lexicons have been developed using a novel combination of automatic or semi-automatic approaches: mapping, crowdsourcing, statistical machine translation, GIZA++, word embeddings, and named entity. A large multi-target annotated corpus is also constructed using a semi-automatic approach to test accuracy of the Urdu semantic tagger, proposed corpus is also used to train and test supervised multi-target Machine Learning classifiers. The results show that Random k-labEL Disjoint Pruned Sets and Classifier Chain multi-target classifiers outperform all other classifiers on the proposed corpus with a Hamming Loss of 0.06\% and Accuracy of 0.94\%. The best lexical coverage of 88.59\%, 99.63\%, 96.71\% and 89.63\% are obtained on several test corpora. The developed Urdu semantic tagger shows encouraging precision on the proposed test corpus of 79.47\%
Dialect-robust Evaluation of Generated Text
Evaluation metrics that are not robust to dialect variation make it
impossible to tell how well systems perform for many groups of users, and can
even penalize systems for producing text in lower-resource dialects. However,
currently, there exists no way to quantify how metrics respond to change in the
dialect of a generated utterance. We thus formalize dialect robustness and
dialect awareness as goals for NLG evaluation metrics. We introduce a suite of
methods and corresponding statistical tests one can use to assess metrics in
light of the two goals. Applying the suite to current state-of-the-art metrics,
we demonstrate that they are not dialect-robust and that semantic perturbations
frequently lead to smaller decreases in a metric than the introduction of
dialect features. As a first step to overcome this limitation, we propose a
training schema, NANO, which introduces regional and language information to
the pretraining process of a metric. We demonstrate that NANO provides a
size-efficient way for models to improve the dialect robustness while
simultaneously improving their performance on the standard metric benchmark
Developing Methods and Resources for Automated Processing of the African Language Igbo
Natural Language Processing (NLP) research is still in its infancy in Africa. Most of
languages in Africa have few or zero NLP resources available, of which Igbo is among those
at zero state. In this study, we develop NLP resources to support NLP-based research in
the Igbo language. The springboard is the development of a new part-of-speech (POS)
tagset for Igbo (IgbTS) based on a slight adaptation of the EAGLES guideline as a result
of language internal features not recognized in EAGLES. The tagset consists of three
granularities: fine-grain (85 tags), medium-grain (70 tags) and coarse-grain (15 tags). The
medium-grained tagset is to strike a balance between the other two grains for practical
purpose. Following this is the preprocessing of Igbo electronic texts through normalization
and tokenization processes. The tokenizer is developed in this study using the tagset
definition of a word token and the outcome is an Igbo corpus (IgbC) of about one million
tokens.
This IgbTS was applied to a part of the IgbC to produce the first Igbo tagged corpus
(IgbTC). To investigate the effectiveness, validity and reproducibility of the IgbTS, an
inter-annotation agreement (IAA) exercise was undertaken, which led to the revision of the
IgbTS where necessary. A novel automatic method was developed to bootstrap a manual
annotation process through exploitation of the by-products of this IAA exercise, to improve
IgbTC. To further improve the quality of the IgbTC, a committee of taggers approach
was adopted to propose erroneous instances on IgbTC for correction. A novel automatic
method that uses knowledge of affixes to flag and correct all morphologically-inflected
words in the IgbTC whose tags violate their status as not being morphologically-inflected
was also developed and used.
Experiments towards the development of an automatic POS tagging system for Igbo
using IgbTC show good accuracy scores comparable to other languages that these taggers
have been tested on, such as English. Accuracy on the words previously unseen during
the taggers’ training (also called unknown words) is considerably low, and much lower
on the unknown words that are morphologically-complex, which indicates difficulty in
handling morphologically-complex words in Igbo. This was improved by adopting a
morphological reconstruction method (a linguistically-informed segmentation into stems
and affixes) that reformatted these morphologically-complex words into patterns learnable
by machines. This enables taggers to use the knowledge of stems and associated affixes
of these morphologically-complex words during the tagging process to predict their
appropriate tags. Interestingly, this method outperforms other methods that existing
taggers use in handling unknown words, and achieves an impressive increase for the
accuracy of the morphologically-inflected unknown words and overall unknown words.
These developments are the first NLP toolkit for the Igbo language and a step towards
achieving the objective of Basic Language Resources Kits (BLARK) for the language. This
IgboNLP toolkit will be made available for the NLP community and should encourage
further research and development for the language
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