GloVe: Global Vectors For Word Representation

GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.

Actived: Friday Jul 1, 2022



The Stanford Natural Language Processing Group (7 days ago)

About | Questions | Mailing lists | Download | Extensions | Release history | FAQ. About. A Part-Of-Speech Tagger (POS Tagger) is a piece of software that reads text in some language and assigns parts of speech to each word (and other token), such as noun, verb, adjective, etc., although generally computational applications use more fine-grained POS tags like 'noun-plural'.


The Stanford Natural Language Processing Group (8 days ago)

The code can also be invoked programatically, using Stanford CoreNLP.For this, simply include the annotators natlog and openie in the annotators property, and add any of the flags described above to the properties file prepended with the string "openie.", e.g., "openie.format = ollie". Note that openie depends on the annotators "tokenize,ssplit,pos,depparse".


Software - The Stanford Natural Language Processing Group (10 days ago)

A Python natural language analysis package that provides implementations of fast neural network models for tokenization, multi-word token expansion, part-of-speech and morphological features tagging, lemmatization and dependency parsing using the Universal Dependencies formalism.Pretrained models are provided for more than 70 human languages.


The Stanford Natural Language Processing Group (7 days ago)

The Stanford NLP Group The Natural Language Processing Group at Stanford University is a team of faculty, postdocs, programmers and students who work together on algorithms that allow computers to process, generate, and understand human languages.


Recursive Deep Models for Semantic Compositionality Over a … (6 days ago)

This website provides a live demo for predicting the sentiment of movie reviews. Most sentiment prediction systems work just by looking at words in isolation, giving positive points for positive words and negative points for negative words and then summing up these points. That way, the order of words is ignored and important information is lost. In constrast, our new deep learning …


The Stanford Natural Language Processing Group (9 days ago)

Neural Machine Translation. This page contains information about latest research on neural machine translation (NMT) at Stanford NLP group. We release our codebase which produces state-of-the-art results in various translation tasks such as English-German and English-Czech. In addtion, to encourage reproducibility and increase transparency, we release the preprocessed …


The Stanford Natural Language Processing Group (9 days ago)

About | Citing | Questions | Download | Included Tools | Extensions | Release history | Sample output | Online | FAQ. About. A natural language parser is a program that works out the grammatical structure of sentences, for instance, which groups of words go together (as "phrases") and which words are the subject or object of a verb. Probabilistic parsers use …


Christopher Manning, Stanford NLP (6 days ago)

Jan 13, 2019  · M: Dept of Computer Science, Gates Building 2A, 353 Jane Stanford Way, Stanford CA 94305-9020, USA E: T: @chrmanning: W …


Introduction to Information Retrieval - Stanford University (12 days ago)

Introduction to Information Retrieval. This is the companion website for the following book. Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008.. You can order this book at CUP, at your local bookstore or on the internet.The best search term to use is the ISBN: 0521865719.

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Stanford TACRED Homepage (9 days ago)

Introduction. TACRED is a large-scale relation extraction dataset with 106,264 examples built over newswire and web text from the corpus used in the yearly TAC Knowledge Base Population (TAC KBP) challenges.Examples in TACRED cover 41 relation types as used in the TAC KBP challenges (e.g., per:schools_attended and org:members) or are labeled as no_relation if no …


Single-Link, Complete-Link & Average-Link Clustering (7 days ago)

There is now an updated and expanded version of this page in form of a book chapter. Single-Link, Complete-Link & Average-Link Clustering. Hierarchical clustering treats each data point as a singleton cluster, and then successively merges clusters until all points have been merged into a single remaining cluster.

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Introduction to Information Retrieval: Slides (9 days ago)

Introduction to Information Retrieval: Slides Powerpoint slides are from the Stanford CS276 class and from the Stuttgart IIR class. Latex slides are from the Stuttgart IIR class.


Support vector machines: The linearly separable case (9 days ago)

Again, the points closest to the separating hyperplane are support vectors. The geometric margin of the classifier is the maximum width of the band that can be drawn separating the support vectors of the two classes. That is, it is twice the minimum value over data points for given in Equation 168, or, equivalently, the maximal width of one of the fat separators shown in Figure …


Basic XML concepts - Stanford University (7 days ago)

Figure 10.2 shows Figure 10.1 as a tree. The leaf nodes of the tree consist of text, e.g., Shakespeare, Macbeth, and Macbeth's castle. The tree's internal nodes encode either the structure of the document (title, act, and scene) or metadata functions (author).. The standard for accessing and processing XML documents is the XML Document Object Model or DOM. ...


Linear versus nonlinear classifiers - Stanford University (12 days ago)

In two dimensions, a linear classifier is a line. Five examples are shown in Figure 14.8.These lines have the functional form .The classification rule of a linear classifier is to assign a document to if and to if .Here, is the two-dimensional vector representation of the document and is the parameter vector that defines (together with ) the decision boundary.


Evaluation of clustering - Stanford University (8 days ago)

where, again, the second equation is based on maximum likelihood estimates of the probabilities. in Equation 184 measures the amount of information by which our knowledge about the classes increases when we are told what the clusters are. The minimum of is 0 if the clustering is random with respect to class membership. In that case, knowing that a document is in a particular …


K-means - Stanford University (7 days ago)

The first step of -means is to select as initial cluster centers randomly selected documents, the seeds.The algorithm then moves the cluster centers around in space in order to minimize RSS. As shown in Figure 16.5, this is done iteratively by repeating two steps until a stopping criterion is met: reassigning documents to the cluster with the closest centroid; and recomputing each …


Hubs and Authorities - Stanford University (9 days ago)

Here, is the main authority - two hubs (and ) are pointing to it via highly weighted jaguar links. End worked example. Since the iterative updates captured the intuition of good hubs and good authorities, the high-scoring pages we output would give us good hubs and authorities from the target subset of web pages.


Mutual information - Stanford University (12 days ago)

End worked example. To select terms for a given class, we use the feature selection algorithm in Figure 13.6: We compute the utility measure as and select the terms with the largest values.. Mutual information measures how much information - in the information-theoretic sense - a term contains about the class.


The Stanford Natural Language Processing Group (11 days ago)

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