embeddings
This class and its companion object have been backported from Eidos.
Generates embeddings for lemmas, by averaging GloVe embeddings for words that have the same lemma The averaging of embedding vectors is weighted by the frequency of the corresponding words in Gigaword
Implements similarity metrics using the embedding matrix IMPORTANT: In our implementation, words are lower cased but NOT lemmatized or stemmed (see sanitizeWord) Note: matrixConstructor is lazy, meant to save memory space if we're caching features User: mihais, dfried, gus Date: 11/25/13 Last Modified: Fix compiler issue: import scala.io.Source.
This class and its companion object have been backported from Eidos. There it is/was an optional replacement for WordEmbeddingMap used for performance reasons. It loads data faster from disk and stores it more compactly in memory. It does not, however, include all the operations of processer's Word2Vec. For instance, logMultiplicativeTextSimilarity is not included, but could probably be added. Other methods like getWordVector, which in Word2Vec returns an Array[Double], would be inefficient to include because the arrays of doubles (or floats) are no longer part of the design. For more documentation other than that immediately below, both the companion object and the related test case (org.clulab.embeddings.TestCompactWord2Vec) may be helpful.
The class is typically instantiated by the apply method of the companion object which takes as arguments a filename and then two booleans: "resource", which specifies whether the named file exists as a resource or is alternatively stored on the broader filesystem, and "cached", which specifies that the data consists of Java-serialized objects (see the save method) or, alternatively, the standard vector text format. The apply method arranges for the file to be read in the appropriate way and converted into a map with the words being keys with values being the row numbers in an implied 2-dimentional matrix of the all vector values, also included in the constructor. So, rather than each word being mapped to an independent, mini array as in Word2Vec, they are mapped to an integer row number of a single, larger matrix/array.
To take advantage of the faster load times, the vector data file needs to be converted from text format into a binary (Java serialized objects) for loadBin below. The test case includes an example. In some preprocessing phase, call CompactWord2Vec(filename, resource = false, cached = false) on the file containing the vectors in text format, such as glove.840B.300d.txt. "resource" is usually false because it can be a very large file, too large to include as a resource. On the resulting return value, call save(compactFilename). Thereafter, for normal, speedy processing, use CompactWord2Vec(compactFilename, resource = false, cached = true).