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In this article, we will try to learn about how we can perform word embedding.

#Word Embedding #Python #Gensim Package #InsideAIML #Word Embedding Using Python Gensim Package #Word Embedding Using Python #Word Embedding Using Python Gensim

LDA vs word2vec, really?

These two are related but not comparable. LDA’s intent isn’t to identify the hidden topics out of corpora (plural of corpus), while word2vec is to represent words in an high dimensional embedding space with reserving of the context. So word2vec is contextual, well at least to some degree.

Some says to compare LDA to doc2vec, those are not comparable either. As doc2vec is just the sum of word2vec. If we really like to do it then word2vec (doc2vec) + k-mean (or other clustering techniques) might be something to compare LDA topic modelling to.

As things go more interesting way, could we just take the word2vec representation to LDA calculations to model topics instead of tf-idf?

#nlp #lda #word2vec #word embedding #topic model

maiden voyage with word2vec on spark

from pyspark.ml.feature import Tokenizer, RegexTokenizer from pyspark.ml.feature import StopWordsRemover from pyspark.sql.functions import col, udf from pyspark.sql.types import IntegerType from pyspark.ml.feature import Word2Vec from pyspark.sql import SparkSession import numpy as np import pandas as pd spark = SparkSession.builder.appName('abc').getOrCreate() sc = spark.sparkContext l = pd.DataFrame((['hello world', 1], ['alice wonderland', 2], ['simplicity is thy ultimate sophisitication', 3])) df = spark.createDataFrame(l) in_col = df.columns[0] regexTokenizer = RegexTokenizer(inputCol=in_col, outputCol='words', pattern='\\W') regexTokenized = regexTokenizer.transform(df) # remove stop words though not necessary remover = StopWordsRemover(inputCol='words', outputCol='filtered') filtered = remover.transform(regexTokenized) word2vec = Word2Vec(vectorSize = 20, minCount = 1, inputCol = 'filtered', outputCol = 'result') model = word2vec.fit(filtered) result = model.transform(filtered) V = model.getVectors() # find top 3 synonyms model.findSynonyms('simplicity', 3) # f.... sync as spark bug insync V.sql_ctx.sparkSession._jsparkSession = spark._jsparkSession V._sc = spark._sc # put results into local pandas V_pd = V.toPandas() spark.stop()

#word2vec #word embedding #spark

Methods of name matching and their respective strengths and weaknesses In a structured database, names are often treated the same as metadata for some other field like an email, phone number, or an ID number. But what happens if you only have a name to lookup a record? This happens quite frequently since humans tend …

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