Steven Bird, Ewan Klein and Edward Loper: Natural Language Processing with Python, Analyzing Text with the Natural Language Toolkit

By Wiebke Wagner. 2010

3,530 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage (2022, methodology): “(2021): we extract an entity from the original dialogue response that also appears in the context using noun phrase targets found with the nltk library (Bird et al., 2009), and set that as the knowledge target for training.”
Grounded Language-Image Pre-training (2021, methodology): “Given a good grounding model (teacher), we can augment GLIP pre-training data by automatically generating grounding boxes for massive image-text-paired data, in which noun phrases are detected by an NLP parser [2].”
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset (2023, methodology): “For English, French, Portuguese, and Spanish, we use the NLTK tokenizer (Bird et al., 2009).”
Assessing Composition in Sentence Vector Representations (2018, methodology): “We use a grammar speciﬁed using the NLTK feature grammar framework (Bird et al., 2009).”
AudioGen: Textually Guided Audio Generation (2022, methodology): “Speciﬁcally, we remove stop words and numbers, ﬁnally we lemmatize the remaining words (e.g. “a dog is barking at the park” is transformed to ”dog bark park”) using the WordNet lemmatizer in NLTK (Bird et al., 2009).”
BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization (2019, methodology): “To identify these entities, we extract non-recursive noun phrases (regex NP → ADJ ∗ [NN]+ ) using NLTK (Loper and Bird, 2002).”
Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features (2017, methodology): “The Wikipedia and Toronto books sentences have been to-kenized using the Stanford NLP library (Manning et al., 2014), while for tweets we used the NLTK tweets tokenizer (Bird et al., 2009).”
SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) (2020, methodology): “…n -grams character n -grams lexicons for sentiment analysis and lexicon of offensive words Other representations included emoji priors extracted from the weakly supervised dataset, and sentiment analysis using NLTK (Bird et al., 2009), Vader (Hutto and Gilbert, 2014) and FLAIR (Akbik et al., 2018).”
Talking about other people: an endless range of possibilities (2018, methodology): “We use a context-free grammar (CFG, see Figure 1; implemented using the NLTK, Bird et al. 2009) because each label may consist of multiple modiﬁers from different categories.”
Fine-grained Sentiment Analysis with Faithful Attention (2019, methodology): “Each word had an additional 10 channels for a part-of-speech embedding (POS Tagging by nltk (Bird et al., 2009)), and another 10 for an embedding according to the word’s sentiment score induced from a Reddit news community corpus, using SocialSent (Hamilton et al., 2016).”
Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation (2021, positive): “The POS tag-ger, lemmatizer, and stopword list are from the Natural Language Toolkit (NLTK) package (Bird et al., 2009).”
Improving short text classification through global augmentation methods (2019, neutral): “Input: s : a sentence, run : a number Output: ˆ s a sentence with words replaced 1 def Augment( s,run ) : 2 #»”
Open-vocabulary Attribute Detection (2022, methodology): “[5] Steven Bird, Ewan Klein, and Edward Loper.”
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison (2019, methodology): “To obtain the universal dependency parse, we follow a procedure similar to Peng et al.(2018): ﬁrst, the report is split and tokenized into sentences using NLTK (Bird, Klein, and Loper 2009)”
Text Editing by Command (2020, methodology): “We use the word tokenizer from NLTK (Bird et al., 2009) to break a sentence down into words.”
Mining Dual Emotion for Fake News Detection (2019, methodology): “And we use the Vader package of NLTK[3] to calculate sentiment scores.”
Zero-training Sentence Embedding via Orthogonal Basis (2018, methodology): “For all experiments, sentences are tokenized using the NLTK tokenizer (Bird et al., 2009) wordpunct tokenize, and all punctuation is skipped. f(σj) = σtj in Equation (7).”