I am fairly new to BERT, and I am willing to test two approaches to get "the most similar words" to a given word to use in Snorkel labeling functions for weak supervision. Fist approach was to use word2vec with pre-trained word embedding of "word2vec-google-news-300" to find the most similar words @labeling_function() def lf_find_good_synonyms(x): good_synonyms = word_vectors.most_similar("good", topn=25) ##Similar words are extracted here good_list = syn_list(good_synonyms) ##syn_list just returns the stemmed similar word return POSITIVE if any(word in x.stemmed for …

I am working a data-set with more than 100,000 records. This is how the data looks like: email_id cust_id campaign_name 123 4567 World of Zoro 123 4567 Boho XYz 123 4567 Guess ABC 234 5678 Anniversary X 234 5678 World of Zoro 234 5678 Fathers day 234 5678 Mothers day 345 7890 Clearance event 345 7890 Fathers day 345 7890 Mothers day 345 7890 Boho XYZ 345 7890 Guess ABC 345 7890 Sale I am trying to understand the campaign …

In two-layer perceptrons that slide across words of text, such as word2vec and fastText, hidden layer heights may be a product of two random variables such as positional embeddings and word embeddings (Mikolov et al. 2017, Section 2.2): $$v_c = \sum_{p\in P} d_p \odot u_{t+p}$$ However, it's unclear to me how to best initialize the two variables. When only word embeddings are used for the hidden layer weights, word2vec and fastText initialize them to $\mathcal{U}(-1 / \text{fan_out}; 1 / \text{fan_out})$. …

I have scientific database with articles and coauthors. using this database I am training word2vec model on co-authors. Use use case here is to disambiguate authors. I was wondering my approach here can be improved or any suggestions will greatly be appreciated. Code

I am trying to convert categorical values (zipcodes) with Cat2Vec into a matrix which can be used as an input shape for categorical prediction of a target with binary values. After reading several articles, among which: https://www.yanxishe.com/TextTranslation/1656?from=csdn I am having trouble to understand two things: 1) With respect to which y in Cat2Vec encoding are you creating embeddings. Is it with respect to the actual target in the dataset you are trying to predict, or can you randomly choose any …

In the skip-gram model, the probability that a word $w$ is part of the set of context words $\{w_o^{(i)}\}$ $(i= 1:m)$ where $m$ is the context window around the central word, is given by: $$p(w_o | w_c) = \frac{\exp{(\vec{u_o}\cdot \vec{v_c)}}}{\sum_{i\in V}\exp{(\vec{u_i}\cdot \vec{v_c)}}} $$ where $V$ is the number of words in the training set, $\vec{u_i}$ is the word embedding for the context word and $\vec{v_i}$ is the word embedding for the central word. But this type of model is defining …

I have to use Fasttext model to return word embeddings. In test I was calling it through API. Since there are too many words to compute embeddings, API call seems to be expensive. I would like to use fasttext without API. For that I need to load the model once and keep it in memory for further calls. How can this be done without using API. Any help is highly appreciated.

I am new to NLP and I'm trying to perform embedding for a clustering problem. I have created the word2vec model using Python's gensim library, but I am wondering the following: The word2vec model embeds the words to vectors of size vector_size. However, in further steps of the clustering approach, I realised I was clustering based on single words instead of the sentences I had in my dataset at the beginning. Let's say my vocabulary is composed of the two …

So, say I have the following sentences ["The dog says woof", "a king leads the country", "an apple is red"] I can embed each word using an N dimensional vector, and represent each sentence as either the sum or mean of all the words in the sentence (e.g Word2Vec). When we represent the words as vectors we can do something like vector(king)-vector(man)+vector(woman) = vector(queen) which then combines the different "meanings" of each vector and create a new, where the mean …

Will word2vec fail if sentences contain only similar words, or in other words, if the window size is equal to the sentence size? I suppose this question boils down to whether word to vec considers words from other sentences as negative samples, or only words from the same sentence but outside of the window

In Word2Vec trainable model, there are two different weight matrix. The matrix $W$ from input-to-hidden layer and the matrix $W'$ from hidden-to-output layer. Referring to this article, I understand that the reason we have the matrix $W'$ is basically to compensate for the lack of activation function in the output layer. As activation function is not needed during runtime, there is no activation function in the output layer. But we need to update the input-to-hidden layer weight matrix $W$ through …

I have thousands of headlines and I would like to build a semantic network using word2vec, specifically google news files. My sentences look like Titles Dogs are humans’ best friends A dog died because of an accident You can clean dogs’ paws using natural products. A cat was found in the kitchen And so on. What I would like to do is finding some specific pattern within this data, e.g. similarity in topics on dogs and cats, using semantic networks. …

My task is to predict relevant words based on a short description of an idea. for example "SQL is a domain-specific language used in programming and designed for managing data held in a relational database" should produce words like "mysql", "Oracle", "Sybase", "Microsoft SQL Server" etc... My thinking is to treat the initial text as a set of words (after lemmatization and stop words removal) and predict words that should be in that set. I can then take all of …

I am interested in a framework for learning the similarity of different input representations based on some common context. I have looked into word2vec, SVD and other recommender systems, which does more or less what I want. I want to know if anyone here has any experience or resources on a more generalized version of this, where I am able to feed in representations on different objects, and learn how similar they are. For example: Say we have some customers …

I have the data set with many documents of 50 to 100 words each. I need to clean those data by correcting misspelled words in those documents. I have an algorithm which predicts possible correct words for misspelled word. The problem is I need to choose or verify the predictions made by that algorithm in order to clean the spelling errors in the documents. Can I use all the possible correct words predicted for correct spelling in word vector to …

I have a set of documents and I want to identify and remove the outlier documents. I am just wondering if doc2vec can be used for this task. Or are there any recently evolved, promising algorithms that I can use for this task? EDIT I am currently using a bag of words model to identify outliers.

In NLP while computing word to vector we try to maximize log(P(o|c)). Where P(o|c) is probability that o is outside word, given that c is center word. Uo is word vector for outside word Vc is word vector for center word T is number of words in vocabulary Above equation is softmax. And dot product of Uo and Vc acts as score, which should be higher the better. If words o and c are closer then their dot product should …

I get really confused on why we need to 'train word2vec' when word2vec itself is said to be 'pretrained'? I searched for word2vec pretrained embedding, thinking i can get a mapping table directly mapping my vocab on my dataset to a pretrained embedding but to no avail. Instead, what I only find is how we literally train our own: Word2Vec(sentences=common_texts, vector_size=100, window=5, min_count=1, workers=4) But I'm confused: isn't word2vec already pretrained? Why do we need to 'train' it again? If …

I am very new to Machine Learning and I have recently been exposed to word2vec and BERT. From what I know, word2vec provides a vector representation of words, but is limited to its dictionary definition. This would mean the algorithm may output the unwanted definition of a word with multiple meanings. BERT on the other hand, is able to use context clues in the sentence to describe the true meaning of the word. To me, it sounds like BERT would …

I am working on a project that detects anomalies in a time series. I wonder if I can use word2vec for anomaly detection for non-string inputs like exchange rates?