A General-Purpose Tagger with Convolutional Neural Networks

Xiang, Yu; Faleńska, Agnieszka; Vu, Ngoc Thang

doi:10.18653/v1/w17-4118

Cited by 17 publications

(16 citation statements)

References 16 publications

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“…Neural Multiclass classifier (MC) As the second baseline, we employ the standard multiclass classifier used by both Heigold et al (2017) and Yu et al (2017). The proposed model consists of an LSTM-based encoder, identical to the one described above in section 3.3, and a softmax classifier over the full tagset.…”

Section: Baseline Modelsmentioning

confidence: 99%

Modeling Composite Labels for Neural Morphological Tagging

Tkachenko¹,

Sirts

2018

Proceedings of the 22nd Conference on Computational Natural Language Learning

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Neural morphological tagging has been regarded as an extension to POS tagging task, treating each morphological tag as a monolithic label and ignoring its internal structure. We propose to view morphological tags as composite labels and explicitly model their internal structure in a neural sequence tagger. For this, we explore three different neural architectures and compare their performance with both CRF and simple neural multiclass baselines. We evaluate our models on 49 languages and show that the neural architecture that models the morphological labels as sequences of morphological category values performs significantly better than both baselines establishing state-of-the-art results in morphological tagging for most languages. 1

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Section: Baseline Modelsmentioning

confidence: 99%

Modeling Composite Labels for Neural Morphological Tagging

Tkachenko¹,

Sirts

2018

Proceedings of the 22nd Conference on Computational Natural Language Learning

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“…To generate the pre-trained word embeddings, we have used FastText (https://fasttext.cc/docs/ en/crawl-vectors.html (accessed on 12 June 2018)) embeddings corresponding to each language. To construct the character-based word composition vector, we fix the input size as 32 for each word as in Reference [63]. Six convolutional filters with kernel sizes of 1, 2, 3, 4, 5 and 7 were used.…”

Section: Experiments and Resultsmentioning

confidence: 99%

An Improved Word Representation for Deep Learning Based NER in Indian Languages

2019

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Named Entity Recognition (NER) is the process of identifying the elementary units in a text document and classifying them into predefined categories such as person, location, organization and so forth. NER plays an important role in many Natural Language Processing applications like information retrieval, question answering, machine translation and so forth. Resolving the ambiguities of lexical items involved in a text document is a challenging task. NER in Indian languages is always a complex task due to their morphological richness and agglutinative nature. Even though different solutions were proposed for NER, it is still an unsolved problem. Traditional approaches to Named Entity Recognition were based on the application of hand-crafted features to classical machine learning techniques such as Hidden Markov Model (HMM), Support Vector Machine (SVM), Conditional Random Field (CRF) and so forth. But the introduction of deep learning techniques to the NER problem changed the scenario, where the state of art results have been achieved using deep learning architectures. In this paper, we address the problem of effective word representation for NER in Indian languages by capturing the syntactic, semantic and morphological information. We propose a deep learning based entity extraction system for Indian languages using a novel combined word representation, including character-level, word-level and affix-level embeddings. We have used ‘ARNEKT-IECSIL 2018’ shared data for training and testing. Our results highlight the improvement that we obtained over the existing pre-trained word representations.

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“…Many studies focus on the encoder portion of the model. Approaches include using convolutional neural networks (CNNs) [42], biLSTM's [43,44], and combinations of the two [40,45,46].…”

Section: Sequence Taggingmentioning

confidence: 99%

Viability of Neural Networks for Core Technologies for Resource-Scarce Languages

Loubser

Puttkammer

2020

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In this paper, the viability of neural network implementations of core technologies (the focus of this paper is on text technologies) for 10 resource-scarce South African languages is evaluated. Neural networks are increasingly being used in place of other machine learning methods for many natural language processing tasks with good results. However, in the South African context, where most languages are resource-scarce, very little research has been done on neural network implementations of core language technologies. In this paper, we address this gap by evaluating neural network implementations of four core technologies for ten South African languages. The technologies we address are part of speech tagging, named entity recognition, compound analysis and lemmatization. Neural architectures that performed well on similar tasks in other settings were implemented for each task and the performance was assessed in comparison with currently used machine learning implementations of each technology. The neural network models evaluated perform better than the baselines for compound analysis, are viable and comparable to the baseline on most languages for POS tagging and NER, and are viable, but not on par with the baseline, for Afrikaans lemmatization.

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A General-Purpose Tagger with Convolutional Neural Networks

Cited by 17 publications

References 16 publications

Modeling Composite Labels for Neural Morphological Tagging

Modeling Composite Labels for Neural Morphological Tagging

An Improved Word Representation for Deep Learning Based NER in Indian Languages

Viability of Neural Networks for Core Technologies for Resource-Scarce Languages

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