648 research outputs found
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence
Researc
PersoNER: Persian named-entity recognition
© 1963-2018 ACL. Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network
Exploiting word embeddings for modeling bilexical relations
There has been an exponential surge of text data in the recent years. As a consequence, unsupervised methods that make use of this data have been steadily growing in the field of natural language processing (NLP). Word embeddings are low-dimensional vectors obtained using unsupervised techniques on the large unlabelled corpora, where words from the vocabulary are mapped to vectors of real numbers. Word embeddings aim to capture syntactic and semantic properties of words.
In NLP, many tasks involve computing the compatibility between lexical items under some linguistic relation. We call this type of relation a bilexical relation. Our thesis defines statistical models for bilexical relations
that centrally make use of word embeddings. Our principle aim is that the word embeddings will favor generalization to words not seen during the training of the model.
The thesis is structured in four parts. In the first part of this thesis, we present a bilinear model over word embeddings that leverages a small supervised dataset for a binary linguistic relation. Our learning algorithm exploits low-rank bilinear forms and induces a low-dimensional embedding tailored for a target linguistic relation. This results in compressed task-specific embeddings.
In the second part of our thesis, we extend our bilinear model to a ternary
setting and propose a framework for resolving prepositional phrase attachment ambiguity using word embeddings. Our models perform competitively with state-of-the-art models. In addition, our method obtains significant improvements on out-of-domain tests by simply using word-embeddings induced from source and target domains.
In the third part of this thesis, we further extend the bilinear models for expanding vocabulary in the context of statistical phrase-based machine translation. Our model obtains a probabilistic list of possible translations of target language words, given a word in the source language. We do this by projecting pre-trained embeddings into a common subspace using a log-bilinear model. We empirically notice a significant improvement on an out-of-domain test set.
In the final part of our thesis, we propose a non-linear model that maps initial word embeddings to task-tuned word embeddings, in the context of a neural network dependency parser. We demonstrate its use for improved dependency parsing, especially for sentences with unseen words. We also show downstream improvements on a sentiment analysis task.En els darrers anys hi ha hagut un sorgiment notable de dades en format textual. Conseqüentment, en el camp del Processament del Llenguatge Natural (NLP, de l'anglès "Natural Language Processing") s'han desenvolupat mètodes no supervistats que fan ús d'aquestes dades. Els anomenats "word embeddings", o embeddings de paraules, són vectors de dimensionalitat baixa que s'obtenen mitjançant tècniques no supervisades aplicades a corpus textuals de grans volums. Com a resultat, cada paraula del diccionari es correspon amb un vector de nombres reals, el propòsit del qual és capturar propietats sintà ctiques i semà ntiques de la paraula corresponent. Moltes tasques de NLP involucren calcular la compatibilitat entre elements lèxics en l'à mbit d'una relació lingüÃstica. D'aquest tipus de relació en diem relació bilèxica. Aquesta tesi proposa models estadÃstics per a relacions bilèxiques que fan ús central d'embeddings de paraules, amb l'objectiu de millorar la generalització del model lingüÃstic a paraules no vistes durant l'entrenament. La tesi s'estructura en quatre parts. A la primera part presentem un model bilineal sobre embeddings de paraules que explota un conjunt petit de dades anotades sobre una relaxió bilèxica. L'algorisme d'aprenentatge treballa amb formes bilineals de poc rang, i indueix embeddings de poca dimensionalitat que estan especialitzats per la relació bilèxica per la qual s'han entrenat. Com a resultat, obtenim embeddings de paraules que corresponen a compressions d'embeddings per a una relació determinada. A la segona part de la tesi proposem una extensió del model bilineal a trilineal, i amb això proposem un nou model per a resoldre ambigüitats de sintagmes preposicionals que usa només embeddings de paraules. En una sèrie d'avaluacións, els nostres models funcionen de manera similar a l'estat de l'art. A més, el nostre mètode obté millores significatives en avaluacions en textos de dominis diferents al d'entrenament, simplement usant embeddings induïts amb textos dels dominis d'entrenament i d'avaluació. A la tercera part d'aquesta tesi proposem una altra extensió dels models bilineals per ampliar la cobertura lèxica en el context de models estadÃstics de traducció automà tica. El nostre model probabilÃstic obté, donada una paraula en la llengua d'origen, una llista de possibles traduccions en la llengua de destÃ. Fem això mitjançant una projecció d'embeddings pre-entrenats a un sub-espai comú, usant un model log-bilineal. EmpÃricament, observem una millora significativa en avaluacions en dominis diferents al d'entrenament. Finalment, a la quarta part de la tesi proposem un model no lineal que indueix una correspondència entre embeddings inicials i embeddings especialitzats, en el context de tasques d'anà lisi sintà ctica de dependències amb models neuronals. Mostrem que aquest mètode millora l'analisi de dependències, especialment en oracions amb paraules no vistes durant l'entrenament. També mostrem millores en un tasca d'anà lisi de sentiment
A survey of cross-lingual word embedding models
Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural language processing models for low-resource languages. In this survey, we provide a comprehensive typology of cross-lingual word embedding models. We compare their data requirements and objective functions. The recurring theme of the survey is that many of the models presented in the literature optimize for the same objectives, and that seemingly different models are often equivalent, modulo optimization strategies, hyper-parameters, and such. We also discuss the different ways cross-lingual word embeddings are evaluated, as well as future challenges and research horizons.</jats:p
Unveiling Biases in Word Embeddings: An Algorithmic Approach for Comparative Analysis Based on Alignment
openWord embeddings are state-of-the-art vectorial representation of words with the goal of preserving semantic similarity. They are the result of specific learning algorithms trained on usually large corpora. Consequently, they inherit all biases of the corpora on which they have been trained on. The goal of the thesis is to devise and adapt an efficient algorithm to compare two different word embeddings in order to highlight the biases they are subjected to. Specifically, we look for an alignment between the two vector spaces, corresponding to the two word embeddings, that minimises the difference between the stable words, i.e. the ones that have not changed in the two embeddings, thus highlighting the differences between the ones that did changed. In this work, we test this idea adapting a machine translation framework called MUSE that, after some improvements, can run over multiple cores in a HPC framework, specifically managed with SLURM. We also provide an amplpy implementation of linear and convex programming algorithms adapted to our case. We then test these techniques on a corpus of text taken from Italian newspapers in order to identify which words are more subject to change among the different pairs of corpora.Word embeddings are state-of-the-art vectorial representation of words with the goal of preserving semantic similarity. They are the result of specific learning algorithms trained on usually large corpora. Consequently, they inherit all biases of the corpora on which they have been trained on. The goal of the thesis is to devise and adapt an efficient algorithm to compare two different word embeddings in order to highlight the biases they are subjected to. Specifically, we look for an alignment between the two vector spaces, corresponding to the two word embeddings, that minimises the difference between the stable words, i.e. the ones that have not changed in the two embeddings, thus highlighting the differences between the ones that did changed. In this work, we test this idea adapting a machine translation framework called MUSE that, after some improvements, can run over multiple cores in a HPC framework, specifically managed with SLURM. We also provide an amplpy implementation of linear and convex programming algorithms adapted to our case. We then test these techniques on a corpus of text taken from Italian newspapers in order to identify which words are more subject to change among the different pairs of corpora
Coherence in Machine Translation
Coherence ensures individual sentences work together to form a meaningful document. When properly translated, a coherent document in one language should result in a coherent document in another language. In Machine Translation, however, due to reasons of modeling and computational complexity, sentences are pieced together from words or phrases based on short context windows and
with no access to extra-sentential context.
In this thesis I propose ways to automatically assess the coherence of machine translation output. The work is structured around three dimensions: entity-based coherence, coherence as evidenced via syntactic patterns, and coherence as
evidenced via discourse relations.
For the first time, I evaluate existing monolingual coherence models on this new task, identifying issues and challenges that are specific to the machine translation setting. In order to address these issues, I adapted a state-of-the-art syntax
model, which also resulted in improved performance for the monolingual task. The results clearly indicate how much more difficult the new task is than the task of detecting shuffled texts. I proposed a new coherence model, exploring the crosslingual transfer of discourse relations in machine translation. This model is novel in that it measures the correctness of the discourse relation by comparison to the source text rather than to a reference translation. I identified patterns of incoherence common across different language pairs, and created a corpus of machine translated output annotated with coherence errors for evaluation purposes. I then examined
lexical coherence in a multilingual context, as a preliminary study for crosslingual transfer. Finally, I determine how the new and adapted models correlate with human judgements of translation quality and suggest that improvements in general evaluation within machine translation would benefit from having a coherence component that evaluated the translation output with respect to the source text
Distributional semantics and machine learning for statistical machine translation
[EU]Lan honetan semantika distribuzionalaren eta ikasketa automatikoaren erabilera aztertzen
dugu itzulpen automatiko estatistikoa hobetzeko. Bide horretan, erregresio logistikoan
oinarritutako ikasketa automatikoko eredu bat proposatzen dugu hitz-segiden itzulpen-
probabilitatea modu dinamikoan modelatzeko. Proposatutako eredua itzulpen automatiko
estatistikoko ohiko itzulpen-probabilitateen orokortze bat dela frogatzen dugu, eta testuinguruko nahiz semantika distribuzionaleko informazioa barneratzeko baliatu ezaugarri
lexiko, hitz-cluster eta hitzen errepresentazio bektorialen bidez. Horretaz gain, semantika
distribuzionaleko ezagutza itzulpen automatiko estatistikoan txertatzeko beste hurbilpen
bat lantzen dugu: hitzen errepresentazio bektorial elebidunak erabiltzea hitz-segiden
itzulpenen antzekotasuna modelatzeko. Gure esperimentuek proposatutako ereduen baliagarritasuna erakusten dute, emaitza itxaropentsuak eskuratuz oinarrizko sistema sendo
baten gainean. Era berean, gure lanak ekarpen garrantzitsuak egiten ditu errepresentazio
bektorialen mapaketa elebidunei eta hitzen errepresentazio bektorialetan oinarritutako
hitz-segiden antzekotasun neurriei dagokienean, itzulpen automatikoaz haratago balio
propio bat dutenak semantika distribuzionalaren arloan.[EN]In this work, we explore the use of distributional semantics and machine learning to
improve statistical machine translation. For that purpose, we propose the use of a logistic
regression based machine learning model for dynamic phrase translation probability mod-
eling. We prove that the proposed model can be seen as a generalization of the standard
translation probabilities used in statistical machine translation, and use it to incorporate
context and distributional semantic information through lexical, word cluster and word
embedding features. Apart from that, we explore the use of word embeddings for phrase
translation probability scoring as an alternative approach to incorporate distributional
semantic knowledge into statistical machine translation. Our experiments show the
effectiveness of the proposed models, achieving promising results over a strong baseline.
At the same time, our work makes important contributions in relation to bilingual word
embedding mappings and word embedding based phrase similarity measures, which go be-
yond machine translation and have an intrinsic value in the field of distributional semantics
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