pdfcoffee
Chapter 71971 5443 0.000 0.3481971 1377 0.000 0.3481971 3682 0.017 0.3281971 51 0.022 0.3221971 857 0.000 0.3181971 1161 0.000 0.3131971 4971 0.000 0.3131971 5168 0.000 0.3121971 3099 0.000 0.3111971 462 0.000 0.310The code for this embedding strategy is available in neurips_papers_node2vec.pyin the source code folder accompanying this chapter. Next, we will move on to lookat character and subword embeddings.Character and subword embeddingsAnother evolution of the basic word embedding strategy has been to look atcharacter and subword embeddings instead of word embeddings. Characterlevel embeddings were first proposed by Xiang and LeCun [17], and foundto have some key advantages over word embeddings.First, a character vocabulary is finite and small – for example, a vocabulary forEnglish would contain around 70 characters (26 characters, 10 numbers, and restspecial characters), leading to character models that are also small and compact.Second, unlike word embeddings, which provide vectors for a large but finite setof words, there is no concept of out-of-vocabulary for character embeddings, sinceany word can be represented by the vocabulary. Third, character embeddings tendto be better for rare and misspelled words because there is much less imbalance forcharacter inputs than for word inputs.Character embeddings tend to work better for applications that require the notionof syntactic rather than semantic similarity. However, unlike word embeddings,character embeddings tend to be task-specific and are usually generated inlinewithin a network to support the task. For this reason, third party characterembeddings are generally not available.Subword embeddings combine the idea of character and word embeddings bytreating a word as a bag of character n-grams, that is, sequences of n consecutivewords. They were first proposed by Bojanowski, et al. [18] based on research fromFacebook AI Research (FAIR), which they later released as fastText embeddings.fastText embeddings are available for 157 languages, including English. The paperhas reported state of the art performance on a number of NLP tasks.[ 259 ]
Word EmbeddingsfastText computes embeddings for character n-grams where n is between 3 and6 characters (default settings, can be changed), as well as for the words themselves.For example, character n-grams for n=3 for the word "green" would be "<gr", "gre","ree", "een", and "en>". Beginning and end of words are marked with "<" and ">"characters respectively, to distinguish between short words and their n-gramssuch as "<cat>" and "cat".During lookup, you can look up a vector from the fastText embedding using theword as the key if the word exists in the embedding. However, unlike traditionalword embeddings, you can still construct a fastText vector for a word that does notexist in the embedding. This is done by decomposing the word into its constituenttrigram subwords as shown in the preceding example, looking up the vectors forthe subwords, and then taking the average of these subword vectors. The fastTextPython API [19] will do this automatically, but you will need to do this manually ifyou use other APIs to access fastText word embeddings, such as gensim or NumPy.Next up, we will look at Dynamic embeddings.Dynamic embeddingsSo far, all the embeddings we have considered have been static; that is, they aredeployed as a dictionary of words (and subwords) mapped to fixed dimensionalvectors. The vector corresponding to a word in these embeddings is going to bethe same regardless of whether it is being used as a noun or verb in the sentence,for example the word "ensure" (the name of a health supplement when used asa noun, and to make certain when used as a verb). It also provides the same vectorfor polysemous words or words with multiple meanings, such as "bank" (whichcan mean different things depending on whether it co-occurs with the word"money" or "river"). In both cases, the meaning of the word changes dependingon clues available in its context, the sentence. Dynamic embeddings attempt touse these signals to provide different vectors for words based on its context.Dynamic embeddings are deployed as trained networks that convert your input(typically a sequence of one-hot vectors) into a lower dimensional dense fixed-sizeembedding by looking at the entire sequence, not just individual words. You caneither preprocess your input to this dense embedding and then use this as input toyour task-specific network, or wrap the network and treat it similar to the tf.keras.layers.Embedding layer for static embeddings. Using a dynamic embeddingnetwork in this way is usually much more expensive compared to generating itahead of time (the first option), or using traditional embeddings.[ 260 ]
- Page 244 and 245: Chapter 6Another interesting paper
- Page 246 and 247: Chapter 6To elaborate, let us say t
- Page 248 and 249: Chapter 6Figure 7: The architecture
- Page 250 and 251: Chapter 6Figure 11: Illegible initi
- Page 252 and 253: Chapter 6Bedrooms: Generated bedroo
- Page 254 and 255: Chapter 6The images need to be norm
- Page 256 and 257: Chapter 6initializer = tf.random_no
- Page 258 and 259: Cool, right? Now we can define the
- Page 260 and 261: Chapter 6d_loss = (dA_loss + dB_los
- Page 262 and 263: Chapter 6generator_AB.save_weights(
- Page 264: 6. Ledig, Christian, et al. Photo-R
- Page 267 and 268: Word EmbeddingsDeep learning models
- Page 269 and 270: Word EmbeddingsFor example, "crucia
- Page 271 and 272: Word EmbeddingsAssuming a window si
- Page 273 and 274: Word EmbeddingsGloVeThe Global vect
- Page 275 and 276: Word Embeddingsgensim is an open so
- Page 277 and 278: Word Embeddingsgensim also provides
- Page 279 and 280: Word EmbeddingsSpecifically, we wil
- Page 281 and 282: Word EmbeddingsWe will also convert
- Page 283 and 284: Word EmbeddingsE = np.zeros((vocab_
- Page 285 and 286: Word Embeddingsx = self.embedding(x
- Page 287 and 288: Word EmbeddingsThe change in valida
- Page 289 and 290: Word EmbeddingsThe dataset is a 114
- Page 291 and 292: Word Embeddingsprint("random walks
- Page 293: Word Embeddingssize=128, # size of
- Page 297 and 298: Word EmbeddingsIn the future, once
- Page 299 and 300: Word EmbeddingsA much earlier relat
- Page 301 and 302: Word EmbeddingsOnce you have the fi
- Page 303 and 304: Word EmbeddingsThis will create the
- Page 305 and 306: Word EmbeddingsClassifying with BER
- Page 307 and 308: Word Embeddings2. Each Transformer
- Page 309 and 310: Word EmbeddingsOnce trained, we sav
- Page 311 and 312: Word Embeddings4. Pennington, J., S
- Page 313 and 314: Word Embeddings34. Google Research,
- Page 315 and 316: Recurrent Neural NetworksWe will th
- Page 317 and 318: Recurrent Neural NetworksFor notati
- Page 319 and 320: Recurrent Neural NetworksThis probl
- Page 321 and 322: Recurrent Neural NetworksThe line a
- Page 323 and 324: Recurrent Neural NetworksGated recu
- Page 325 and 326: Recurrent Neural NetworksThis probl
- Page 327 and 328: Recurrent Neural NetworksThe topolo
- Page 329 and 330: Recurrent Neural Networkstexts = do
- Page 331 and 332: Recurrent Neural Networksdef call(s
- Page 333 and 334: Recurrent Neural Networks# callback
- Page 335 and 336: Recurrent Neural NetworksExample
- Page 337 and 338: Recurrent Neural NetworksAs can be
- Page 339 and 340: Recurrent Neural Networksdata_dir =
- Page 341 and 342: Recurrent Neural NetworksWe can als
- Page 343 and 344: Recurrent Neural NetworksIn order t
Chapter 7
1971 5443 0.000 0.348
1971 1377 0.000 0.348
1971 3682 0.017 0.328
1971 51 0.022 0.322
1971 857 0.000 0.318
1971 1161 0.000 0.313
1971 4971 0.000 0.313
1971 5168 0.000 0.312
1971 3099 0.000 0.311
1971 462 0.000 0.310
The code for this embedding strategy is available in neurips_papers_node2vec.py
in the source code folder accompanying this chapter. Next, we will move on to look
at character and subword embeddings.
Character and subword embeddings
Another evolution of the basic word embedding strategy has been to look at
character and subword embeddings instead of word embeddings. Character
level embeddings were first proposed by Xiang and LeCun [17], and found
to have some key advantages over word embeddings.
First, a character vocabulary is finite and small – for example, a vocabulary for
English would contain around 70 characters (26 characters, 10 numbers, and rest
special characters), leading to character models that are also small and compact.
Second, unlike word embeddings, which provide vectors for a large but finite set
of words, there is no concept of out-of-vocabulary for character embeddings, since
any word can be represented by the vocabulary. Third, character embeddings tend
to be better for rare and misspelled words because there is much less imbalance for
character inputs than for word inputs.
Character embeddings tend to work better for applications that require the notion
of syntactic rather than semantic similarity. However, unlike word embeddings,
character embeddings tend to be task-specific and are usually generated inline
within a network to support the task. For this reason, third party character
embeddings are generally not available.
Subword embeddings combine the idea of character and word embeddings by
treating a word as a bag of character n-grams, that is, sequences of n consecutive
words. They were first proposed by Bojanowski, et al. [18] based on research from
Facebook AI Research (FAIR), which they later released as fastText embeddings.
fastText embeddings are available for 157 languages, including English. The paper
has reported state of the art performance on a number of NLP tasks.
[ 259 ]
Change language