| Challenge: | Recent approaches based on end-to-end Deep Neural Networks (DNNs) have shown promising results for Natural Language Processing (NLP). |
| Approach: | They propose a neural network model for part-of-speech (POS) tagging of User-Generated Content (UGC) such as Twitter, Facebook and Web forums that uses character and word representations. |
| Outcome: | The proposed model is end-to-end and uses character and word representations . it is compared with existing models on social media in English, german, french, italian and spanish . |
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Domain adaptation for part-of-speech tagging of noisy user-generated text (N19-1)
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| Challenge: | Existing POS taggers for canonical German text achieve good results around 97% accuracy, but when applying these trained models to out-of-domain data the performance decreases drastically. |
| Approach: | They propose a neural network that trains an out-of-domain model on a large newswire corpus and transfers those weights by using them as a prior for a model trained on the target domain. |
| Outcome: | The proposed model achieves a tagging accuracy of slightly over 90%, improving on the previous state of the art for this task. |
Handling Normalization Issues for Part-of-Speech Tagging of Online Conversational Text (L18-1)
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Géraldine Damnati, Jeremy Auguste, Alexis Nasr, Delphine Charlet, Johannes Heinecke, Frédéric Béchet
| Challenge: | a new approach to POS tagging noisy user generated text is proposed . word embeddings are trained on a noisy corpus to address both normalization and POS. |
| Approach: | They propose to use word embeddings to normalize text before tagging it, while a gated neural network based tagger handles the remaining errors. |
| Outcome: | The proposed approach normalizes some errors before tagging, while a gated neural network handles the remaining errors. |
Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings (P18-1)
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| Challenge: | recurrent neural networks have produced significant advances in part-of-speech tagging accuracy . a common feature of these models is the presence of rich initial word encodings . however, word or sub-word information interacts only through subsequent recursive layers . |
| Approach: | They propose to use recurrent neural networks with sentence-level context for initial character and word-based representations. |
| Outcome: | The proposed model has the highest accuracy of all participating systems in the CoNLL 2017 task. |
A Supervised Part-Of-Speech Tagger for the Greek Language of the Social Web (2020.lrec-1)
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| Challenge: | Part-of-speech tagging is a fundamental part of NLP, but it is not widely used in unstructured text processing. |
| Approach: | They propose to use part-of-speech tags to extract information from unstructured social text in Greek and a supervised part-off-seech tagger to do so. |
| Outcome: | The proposed method performs better on unstructured microblogging text than existing methods on structured text processing. |
Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities (2020.coling-main)
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| Challenge: | Domain names such as openresearch are being added to a growing set of tokens that an NLP system may need to deal with. |
| Approach: | They propose a tagging model that uses characters as input to break domain names into component words . they propose taagging methods that use concatenated entity names in a large knowledge database . |
| Outcome: | The proposed model improves on concatenated entity names in a knowledge database by 33% . the proposed model can be used for a wide range of languages, including Chinese and Japanese . |
Joint Chinese Word Segmentation and Part-of-speech Tagging via Multi-channel Attention of Character N-grams (2020.coling-main)
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| Challenge: | Chinese word segmentation and part-of-speech tagging can be performed in a sequential order . existing studies have shown that jointly performing them can be effective . |
| Approach: | They propose a character-based neural model enhanced by multi-channel attention of n-grams. |
| Outcome: | The proposed model outperforms baseline models on five benchmark datasets. |
Topic-Aware Neural Keyphrase Generation for Social Media Language (P19-1)
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| Challenge: | Existing methods to extract words from source posts to form keyphrases do not exploit latent topics. |
| Approach: | They propose a sequence-to-sequence-based neural keyphrase generation framework . it allows absent keyphrases to be created, and it allows joint modeling of latent topic representations . |
| Outcome: | The proposed model outperforms extraction and generation models without exploiting latent topics. |
End-to-End Graph-Based TAG Parsing with Neural Networks (N18-1)
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| Challenge: | Using BiLSTMs, highway connections, and character-level CNNs, we propose a graph-based Tree Adjoining Grammar (TAG) parser. |
| Approach: | They propose a graph-based Tree Adjoining Grammar parser that uses BiLSTMs, highway connections, and character-level CNNs. |
| Outcome: | The proposed parser outperforms the previously reported best by more than 2.2 LAS and UAS points. |
A Deep Neural Network based Approach for Entity Extraction in Code-Mixed Indian Social Media Text (L18-1)
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| Challenge: | a huge number of people use social media to express and exchange information in their own languages. |
| Approach: | They propose to use a code-mixed environment to extract higher level features from text . they use 'gadget' algorithm that automatically discovers higher level feature from text. |
| Outcome: | The proposed approach is generic and does not make use of handcrafted features or rules. |
Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging (N19-1)
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| Challenge: | Pre-trained neural networks struggle with learning uncommon target-specific patterns. |
| Approach: | They propose to augment the target-network with normalised, weighted and randomly initialised units that beget a better adaptation while maintaining valuable source knowledge. |
| Outcome: | The proposed method achieves state-of-the-art on 3 commonly used datasets. |