| academic chapter | Q21481766 |
| P356 | DOI | 10.1007/978-3-030-37250-7_3 |
| P953 | full work available at URL | https://people.compute.dtu.dk/faan/ps/Nielsen2017Software.pdf |
| P2699 | URL | https://link.springer.com/chapter/10.1007/978-3-030-37250-7_3 |
| P50 | author | Finn Årup Nielsen | Q20980928 |
| Lars Kai Hansen | Q20984804 | ||
| P792 | chapter | 3 | |
| P2860 | cites work | Wikidata: A Free Collaborative Knowledgebase | Q18507561 |
| GloVe: Global Vectors for Word Representation | Q22827276 | ||
| A Good Part-of-Speech Tagger in about 200 Lines of Python | Q47153883 | ||
| Pattern for Python | Q47404953 | ||
| Morphological Priors for Probabilistic Neural Word Embeddings | Q47456175 | ||
| WordRank: Learning Word Embeddings via Robust Ranking | Q47456344 | ||
| It Depends: Dependency Parser Comparison Using A Web-based Evaluation Tool | Q47524819 | ||
| Learning Word Vectors for 157 Languages | Q49985142 | ||
| SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets | Q51316600 | ||
| Natural Language Processing with Python | Q130234790 | ||
| Efficient Estimation of Word Representations in Vector Space | Q24699014 | ||
| Excavating the mother lode of human-generated text: A systematic review of research that uses the wikipedia corpus | Q27615040 | ||
| Software framework for topic modelling with large corpora | Q28042398 | ||
| Scikit-learn: Machine Learning in Python | Q28365500 | ||
| node2vec: Scalable Feature Learning for Networks | Q28595865 | ||
| Enriching Word Vectors with Subword Information | Q28775150 | ||
| Bag of Tricks for Efficient Text Classification | Q28942761 | ||
| TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems | Q29040034 | ||
| Poincaré Embeddings for Learning Hierarchical Representations | Q30524983 | ||
| Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm | Q35255801 | ||
| WordNet: a lexical database for English | Q36655252 | ||
| Learning word vectors for sentiment analysis | Q36845160 | ||
| The Protégé Project: A Look Back and a Look Forward | Q36943877 | ||
| Caffe: Convolutional Architecture for Fast Feature Embedding | Q37804409 | ||
| Wembedder: Wikidata entity embedding web service | Q41799598 | ||
| Polyglot: Distributed Word Representations for Multilingual NLP | Q42293702 | ||
| MultiVec: a Multilingual and Multilevel Representation Learning Toolkit for NLP | Q43304092 | ||
| AllenNLP: A Deep Semantic Natural Language Processing Platform | Q45363611 | ||
| Advances in Pre-Training Distributed Word Representations | Q47001695 | ||
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | software | Q7397 |
| natural language processing | Q30642 | ||
| semantics | Q39645 | ||
| Natural Language Toolkit | Q1635410 | ||
| Word2vec | Q22673982 | ||
| GloVe | Q22826110 | ||
| Pattern | Q47405152 | ||
| SpaCy | Q28406945 | ||
| fastText | Q29124045 | ||
| P1104 | number of pages | 19 | |
| P304 | page(s) | 33-51 | |
| P577 | publication date | 2020-06-09 | |
| P1433 | published in | Statistical Semantics: Methods and Applications | Q96210573 |
| P859 | sponsor | DABAI | Q28609468 |
| P1476 | title | Software for Creating and Analyzing Semantic Representations |
| Q117803982 | Innovative Methods for Affectivity Profiling: Quantitative Semantics |
| Q96210703 | Introduction to Statistical Semantics |
| Q96210715 | Linguistic: Application of LSA to Predict Linguistic Maturity and Language Disorder in Children |
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