🤧Some tasks for competition by gunjianpan
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|NLP senior Task3: Chinese traditional Sequence Annotation||Sequence annotation||Classify||BiLSTM-CRF, Bert-CRF||190626|
|DataMining Project||Semantic Representation||Classify||LSTM-ATT||190620|
|NLP cbb Task2: Medicine Corpus processing with Corpus||Sequence annotation||Classify||BiLSTM-CRF, CRF||190601|
|NLP senior Task2: SemEval2013-Task13-WordSenseInduction||WordSenseInduction||Cluster, LM||BiLM + Clustering/WordNet||190528|
|Semantic Course Task3: NLPCC 2019 Task2||Semantic Parsing||Generation||Seq2Seq||190520|
|Deecamp 2019 Exam A||Basic Knowledge||Test||-||190427|
|NLP cbb Task1: Medicine Corpus processing||Linguistics||Generate||Jieba + Dict + Rules||190418|
|Semantic Task2: SemEval2017-Task4-SentimentAnalysis||Sentiment||Classify||TextCNN & Bert||190414|
|NLP senior Task1: SemEval2018-Task7-RelationClassification||Semantic Relation||Multi Classify||TextCNN & LR & LinearSV||190411|
|Semantic Course Task1||Word Similarity||Regression||word2vec & Bert & WordNet||190324|
NLP senior Task3: Chinese traditional Sequence Annotation
- Task: Chinese traditional Sequence Annotation, CWS & NER
- Code: iofu728/Chinese_T-Sequence-annotation
- Final paper: iofu728/Chinese_T-Sequence-annotation
- Result: CWS: 97.78%/Dev F1; NER: 96.92%/Dev F1
- Task Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder
- Final paper dataMining
NLP cbb Task2: Medicine Corpus processing with Corpus
- Task: Chinese word segmentation, part-of-speech tagging, named-entity recognition
- Code: pku-nlp-forfun/CWS_POS_NER
- Final paper: pku-nlp-forfun/CWS_POS_NER
- Result: CWS: 90.55/Dev F1, 91.16%/Test F1; POS: 96.06/Dev F1, 95.86%/Test F1(Top 10)
NLP senior Task2: SemEval2013-Task13-WordSenseInduction
- Word Sense Induction (WSI) seeks to identify the different senses (or uses) of a target word in a given text in an automatic and fully-unsupervised manner. It is a key-enabling technology that aims to overcome the limitations associated with traditional knowledge-based & supervised Word Sense Disambiguation (WSD) methods, such as:
- their limited adaptation to new languages and domains
- the fixed granularity of senses
- their inability to detect new senses (uses) not present in a given dictionary
Result: 11.06%/Fuzzy NMI, 57.72%/Fuzzy B-Cubed, 25.27%/Average
Semantic Course Task3: NLPCC 2019 Task2 Open Domain Semantic Parsing
Task: Senantic Parsing
- In this task, a Multi-perspective Semantic ParSing (or MSParS) dataset will be released, which can be used to evaluate the performance of a semantic parser from different aspects. This dataset includes more than 80,000 human-generated questions, where each question is annotated with entities, the question type and the corresponding logical form. We split MSParS into a train set, a development set and a test set
Final paper: semantic/task3
- Result: BLEU 0.538
DeeCamp 2019 Exam A
- Problem: DeeCamp 2019 Exam A
NLP cbb Task1: Medicine Corpus processing
- Task: Chinese word segmentation, part-of-speech tagging, named-entity recognition in Rule
- Code: nlpcbb/task1
- Final paper: nlpcbb/task1
Semantic Course Task2: SemEval2017-Task4-SentimentAnalysis
- Subtask A. (rerun): Message Polarity Classification: Given a message, classify whether the message is of positive, negative, or neutral sentiment.
Result: 69.39%/Recall, 69.91%/Macro-F1, 70.03%/accuracy-subTaskA(No.1)
NLP senior Task1: SemEval2018-Task7-RelationClassification
- The NLP tasks that underlie intelligent processing of scientific documents are those of information extraction: identifying concepts and recognizing the semantic relation that holds between them. The current task adresses semantic relation extraction and classification into 6 categories, all of them specific to scientific literature.
Final paper: pku-nlp-forfun/SemEval2018-Task7-Paper
Result: Macro_f1: 67.74%/subTask1.1(No.8), 77.35%/subTask1.2(No.7)
Semantic Course Task1
Task: Word similarity
- Two way to calculate word similarity. one for dictionary, one for corpus.
- Data: Mturk-771
Final result: semanticTask1
- Description: This is a very simple binary classification task, you can design your model in any way you want.
- Evaluation: AUC, area under the ROC curve
- Data: The data file are provided in your answer submission folder in SharePoint (the link shown above).
- train.csv: used to train model.
- test.csv: used to metric your model's performance.
- Feedback / Submission:
- Your AUC score at the test dataSet.
- A brief description of your model and feature engineering.
- Your code.
Final result: Code record
Result: macro_f1: 84.707%/Train, 82.429%/Test