scholarly journals Learning Tag Dependencies for Sequence Tagging

2018 ◽  
Author(s):  
Yuan Zhang ◽  
Hongshen Chen ◽  
Yihong Zhao ◽  
Qun Liu ◽  
Dawei Yin
Keyword(s):  
Language Processing ◽  
Channel Model ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Proposed Model ◽  
Speech Tagging

Sequence tagging is the basis for multiple applications in natural language processing. Despite successes in learning long term token sequence dependencies with neural network, tag dependencies are rarely considered previously. Sequence tagging actually possesses complex dependencies and interactions among the input tokens and the output tags. We propose a novel multi-channel model, which handles different ranges of token-tag dependencies and their interactions simultaneously. A tag LSTM is augmented to manage the output tag dependencies and word-tag interactions, while three mechanisms are presented to efficiently incorporate token context representation and tag dependency. Extensive experiments on part-of-speech tagging and named entity recognition tasks show that  the proposed model outperforms the BiLSTM-CRF baseline by effectively incorporating the tag dependency feature.

scholarly journals Bodo Resources for NLP - An Overview of Existing Primary Resources for Bodo

2021 ◽  
Author(s):  
Mwnthai Narzary ◽  
Gwmsrang Muchahary ◽  
Maharaj Brahma ◽  
Sanjib Narzary ◽  
Pranav Kumar Singh ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Parallel Corpus ◽  
Named Entity ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Challenges And Opportunities ◽  
Speech Tagging

With over 1.4 million Bodo speakers, there is a need for Automated Language Processing systems such as Machine translation, Part Of Speech tagging, Speech recognition, Named Entity Recognition, and so on. In order to develop such a system it requires a sufficient amount of dataset. In this paper we present a detailed description of the primary resources available for Bodo language that can be used as datasets to study Natural Language Processing and its applications. We have listed out different resources available for Bodo language: 8,005 Lexicon dataset collected from agriculture and health, Raw corpus dataset of 2,915,544 words, Tagged corpus consisting of 30,000 sentences, Parallel corpus of 28,359 sentences from tourism, agriculture and health and Tagged and Parallel corpus dataset of 37,768 sentences. We further discuss the challenges and opportunities present in Bodo language.

scholarly journals Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling

2017 ◽  
Vol 5 ◽  
pp. 247-261 ◽  
Author(s):  
Gáabor Berend
Keyword(s):  
Sparse Coding ◽  
Named Entity Recognition ◽  
Training Data ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech Tagging ◽  
Pos Tagging ◽  
Sequence Labeling ◽  
Part Of Speech ◽  
Proposed Model

In this paper we propose and carefully evaluate a sequence labeling framework which solely utilizes sparse indicator features derived from dense distributed word representations. The proposed model obtains (near) state-of-the art performance for both part-of-speech tagging and named entity recognition for a variety of languages. Our model relies only on a few thousand sparse coding-derived features, without applying any modification of the word representations employed for the different tasks. The proposed model has favorable generalization properties as it retains over 89.8% of its average POS tagging accuracy when trained at 1.2% of the total available training data, i.e. 150 sentences per language.

scholarly journals LVBERT: Transformer-Based Model for Latvian Language Understanding

2020 ◽  
Author(s):  
Artūrs Znotiņš ◽  
Guntis Barzdiņš
Keyword(s):  
State Of The Art ◽  
Language Model ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Future Research ◽  
Dependency Parsing ◽  
Named Entity ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Speech Tagging

This paper presents LVBERT – the first publicly available monolingual language model pre-trained for Latvian. We show that LVBERT improves the state-of-the-art for three Latvian NLP tasks including Part-of-Speech tagging, Named Entity Recognition and Universal Dependency parsing. We release LVBERT to facilitate future research and downstream applications for Latvian NLP.

scholarly journals An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 79 ◽  
Author(s):  
Xiaoyu Han ◽  
Yue Zhang ◽  
Wenkai Zhang ◽  
Tinglei Huang
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Additional Information ◽  
Named Entity ◽  
Proposed Model ◽  
The Relationship ◽  
Crucial Part

Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information contained in the sentence, additional information about the entities is verified to be helpful in relation extraction. Additional information such as entity type getting by NER (Named Entity Recognition) and description provided by knowledge base both have their limitations. Nevertheless, there exists another way to provide additional information which can overcome these limitations in Chinese relation extraction. As Chinese characters usually have explicit meanings and can carry more information than English letters. We suggest that characters that constitute the entities can provide additional information which is helpful for the relation extraction task, especially in large scale datasets. This assumption has never been verified before. The main obstacle is the lack of large-scale Chinese relation datasets. In this paper, first, we generate a large scale Chinese relation extraction dataset based on a Chinese encyclopedia. Second, we propose an attention-based model using the characters that compose the entities. The result on the generated dataset shows that these characters can provide useful information for the Chinese relation extraction task. By using this information, the attention mechanism we used can recognize the crucial part of the sentence that can express the relation. The proposed model outperforms other baseline models on our Chinese relation extraction dataset.

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