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Posted by Yinfei Yang, Software Engineer and Chris Tar, Engineering Manager, Google AI The recent rapid progress of neural network-based natural language understanding research, especially on learning semantic text representations, can enable truly novel products such as Smart Compose and Talk to Books.
It can also help improve performance on a variety of natural language tasks which have limited amounts of training data, such as building strong text classifiers from as few as labeled examples. Below, we discuss two papers reporting recent progress on semantic representation research at Google, as well as two new models available for download on TensorFlow Hub that we hope developers will use to build new and exciting applications.
The intuition is that sentences are semantically similar if they have a similar distribution of responses. Sentences are semantically similar if they can be answered by the same responses.
Otherwise, they are semantically different. In this work, we aim to learn semantic similarity by way of a response classification task: given a pvc card inkjet input, we wish to classify the correct response from a batch of randomly selected responses.
But, the ultimate goal is to learn a model that can return encodings representing a variety of natural language relationships, including similarity and relatedness. This is because logical entailment is quite different from simple equivalence and provides more signal for learning complex semantic representations.
For a given input, classification is considered a ranking problem against potential candidates. However, instead of the encoder-decoder architecture in the original skip-thought model, we make use of an encode-only architecture by way of a shared encoder to drive the prediction tasks.
In this way, training time is greatly reduced while preserving the performance on a variety of transfer tasks including sentiment and semantic similarity classification. The aim is to provide a single encoder that can support as wide a variety of applications as possible, including paraphrase detection, relatedness, clustering and custom text classification.
As described in our paper, one version of the Universal Sentence Encoder model uses a deep average network DAN encoder, while a second version uses a more complicated self attended network architecture, Transformer. With the more complicated architecture, the model performs better than the simpler DAN model on a variety of sentiment and similarity classification tasks, and for short sentences is only moderately slower.
However, compute time for the model using Transformer increases noticeably as sentence length increases, whereas the compute time for the DAN model stays nearly constant as sentence length is increased. These are pretrained Tensorflow models that return a semantic encoding for variable-length text inputs.
The encodings can be used for semantic similarity measurement, relatedness, classification, or clustering of natural language text. The Large model is trained with the Transformer encoder described in our second paper. The Lite model is trained on a Sentence Piece vocabulary instead of words in order to significantly reduce the vocabulary size, which is a major contributor of model size.
It targets scenarios where resources like memory and CPU are limited, such as on-device or browser based implementations. We're excited to share this research, and these models, with the community. We believe that what we're showing here is just the beginning, and that there remain important research problems to be addressed, such as extending the techniques to more languages the models discussed above currently support English. We also hope to further develop this technology so it can understand text at the paragraph or even document level.
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Text Classification in Spark NLP with Bert and Universal Sentence Encoders
Coupled with powerful processing units which are coming up fast, DL applications are growing increasingly popular. Universal Sentence Encoder is a transformer-based NLP model widely used for embedding sentences or words.
Further, the embedding can be used used for text clustering, classification and more. Though we can use tags to search in a database, using a semantic search proves to be better. Because a search tool that understands the semantic meaning of text could provide better results. The Universal Sentence Encoder model is open-source and is freely available to use.
And tf-hub models are extremely simple to download, use and even train. We have our Universal Sentence Encoder model, we have our libraries. The model accepts a list of strings as input and returns a dimensional vector as output. The embedding can be generated by just passing the list of strings which is our text data bank. The embedding vector produced by the Universal Sentence Encoder model is already normalized.
Because inner product between normalized vectors is the same as finding the cosine similarity. The correlation matrix would have a shape of N,1 where N is the number of strings in the text bank list.
Each of the rows in the N,1 matrix gives is similarity score. In fact, this gives a clear view of how similar is each one of the sentences to the query.
Then, we can use the index to print the string from the text bank. This kind of semantic search can be used in answering customer queries that are similar to already answered ones.
Rather than answering again, we can pull out the answer from an answered query which is similar. And in many more applications! Find the Jupyter notebook of this tutorial in this repository.In this post, we will learn a tool called Universal Sentence Encoder by Google that allows you to convert any sentence into a vector.
First, let us examine what a vector is. A vector is simply an array of numbers of a particular dimension. If you have two vectors in this 3D space, you can calculate the distance between the points. In other words, you can say which vectors are close together and which are not. A lot of effort in machine learning research is devoted to converting data into a vector because once you have converted data into a vector, you can use the notion of distances to figure out if two data points are similar to each other or not.
Similarly, if you build a system for converting an image into a vector where images of pedestrians cluster together and anything that is not a pedestrian is far away from this cluster, you can build a pedestrian detector. Histogram of Oriented Gradients does exactly this — it converts an image patch to a vector.
Now you can probably see how it would be really cool to represent words and sentences using a vector. You can use a tool like word2vec or GloVe to convert a word into a vector. Now, going from words to sentences is not immediately obvious. You cannot simply use word2vec naively on each word of the sentence and expect to get meaningful results.
So, we need a tool that can convert an entire sentence into a vector. What can we expect from a good sentence encoder? Consider the three sentences below. It is obvious that 1 and 2 are semantically the same even though 1 and 3 have more common words. A good sentence encoder will encode the three sentences in such a way that the vectors for 1 and 2 are closer to each other than say 1 and 3.
The best sentence encoders available right now are the two Universal Sentence Encoder models by Google. They are pre-trained on a large corpus and can be used in a variety of tasks sentimental analysis, classification and so on.
Both models take a word, sentence or a paragraph as input and output a dimensional vector. The transformer model is designed for higher accuracy, but the encoding requires more memory and computational time.
The DAN model on the other hand is designed for speed and efficiency, and some accuracy is sacrificed. The table below summarizes the two models. Next, we import the required modules.Natural language processing NLP is a key component in many data science systems that must understand or reason about a text. Common use cases include text classification, question answering, paraphrasing or summarising, sentiment analysis, natural language BI, language modeling, and disambiguation.
NLP is essential in a growing number of AI applications. Extracting accurate information from free text is a must if you are building a chatbot, searching through a patent database, matching patients to clinical trials, grading customer service or sales calls, extracting facts from financial reports or solving for any of these 44 use cases across 17 industries. Text classification is one of the main tasks in modern NLP and it is the task of assigning a sentence or document an appropriate category.
The categories depend on the chosen dataset and can range from topics. Every text classification problem follows similar steps and is being solved with different algorithms. Let alone classical and popular Machine Learning classifiers like Random Forest or Logistic Regression, there are more than deep learning frameworks proposed for various text classification problems.
There are several benchmark datasets being used in Text Classification problems and the latest benchmarks can be tracked on nlpprogress. Here is the basic statistics regarding these datasets.
A simple text classification application usually follows these steps:. Then we will compare this with other ML and DL approaches and text vectorization methods. There are several text classification options in Spark NLP:. As discussed thoroughly in our seminal article about Spark NLPall these text processing steps before ClassifierDL can be implemented in a pipeline that is specified as a sequence of stages, and each stage is either a Transformer or an Estimator.
These stages are run in order, and the input DataFrame is transformed as it passes through each stage. That is, the data are passed through the fitted pipeline in order. With the help of Pipelines, we can ensure that training and test data go through identical feature processing steps.Introducing BERT: Google's New AI Algorithm
The text embedding converts text words or sentences into a numerical vector. Basically, the text embedding methods encode words and sentences in fixed-length dense vectors to drastically improve the processing of textual data.
The idea is simple: Words that occur in the same contexts tend to have similar meanings. Techniques like Word2vec and Glove do that by converting a word to vector. But while embedding a sentence, along with words the context of the whole sentence needs to be captured in that vector. The Universal Sentence Encoder encodes text into high dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.The text embedding converts text words or sentences into a numerical vector.
Why do we convert texts into vectors? A vector is an array of numbers of a particular dimension. If there are two vectors each of dimension 5, they can be thought of two points in a 5D space.
Thus we can calculate how close or distant those two vectors are, depending on the distance measure between them. Hence, lots of efforts in machine learning research are bring put to converting data into a vector as once data is converted into a vector, we can say two data points are similar or not by calculating their distance. Techniques like Word2vec and Glove do that by converting a word to vector. But while embedding a sentence, along with words the context of the whole sentence needs to be captured in that vector.
The Universal Sentence Encoder encodes text into high dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks. The pre-trained Universal Sentence Encoder is publicly available in Tensorflow-hub. It comes with two variations i.
The two have a trade-off of accuracy and computational resource requirement. While the one with Transformer encoder has higher accuracy, it is computationally more intensive. The one with DNA encoding is computationally less expensive and with little lower accuracy. Here, we will go with the transformer encoder version. It worked well for us while running it along with 5 other heavy deep learning models in a 5 GB ram instance. Also, we could be able to train a classifier with 1.
Few use cases of Universal Sentence Encoder I have come across are :. The encoder is optimized for greater-than-word length text, hence can be applied to sentences, phrases or short paragraphs.
For example official site example :. The output it gives :. As it can be seen whether it is a word, sentence or phrase, the sentence encoder is able to give an embedding vector of size How to use in Rest API.Users misspell things. Having spell-check and synonyms helps a lot, but doesn't catch everything. One solution would be to use the python metaphone package 's implementation of the Double Metaphone algorithm.
At component train time, it could look at the normal entity lists, find the DM representation of all the synonyms, and store them. Currently jann is set up to use the Universal Sentence Encoder, which handles English text.
Universal Sentence Encoder
It could be interesting to investigate multilingual interaction using multilingual models. Use-cases of Google's Universal Sentence Encoder e. Evaluates a client-side model built on top of the Universal Sentence Encoder to detect hateful content selected for the analysis. Deep learning tool to determine similarity of given two question. Add a description, image, and links to the universal-sentence-encoder topic page so that developers can more easily learn about it.
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Universal Sentence Encoder For Semantic Search
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Skip to content. Here are 24 public repositories matching this topic Language: All Filter by language. Sort options. Star Code Issues Pull requests. Updated Sep 25, Python.With transformer models such as BERT and friends taking the NLP research community by storm, it might be tempting to just throw the latest and greatest model at a problem and declare it done.
However, in industry, we have compute and memory limitations to consider and might not even have a dedicated GPU for inference. Cer et. We want to learn a model that can map a sentence to a fixed-length vector representation.
This vector encodes the meaning of the sentence and thus can be used for downstream tasks such as searching for similar documents. A naive technique to get sentence embedding is to average the embeddings of words in a sentence and use the average as the representation of the whole sentence. This approach has some challenges. We first install spacy and create an nlp object to load the medium version of their model. Challenge 2: No Respect for Order In this example, we swap the order of words in a sentence resulting in a sentence with a different meaning.
We could fix some of these challenges with hacky manual feature engineering like skipping stop-words, weighting the words by their TF-IDF scores, adding n-grams to respect order when averaging, concatenating embeddings, stacking max pooling and averaged embeddings and so on.
On a high level, the idea is to design an encoder that summarizes any given sentence to a dimensional sentence embedding. We use this same embedding to solve multiple tasks and based on the mistakes it makes on those, we update the sentence embedding. Since the same embedding has to work on multiple generic tasks, it will capture only the most informative features and discard noise. The intuition is that this will result in an generic embedding that transfers universally to wide variety of NLP tasks such as relatedness, clustering, paraphrase detection and text classification.
First, the sentences are converted to lowercase and tokenized into tokens using the Penn Treebank PTB tokenizer. This is the component that encodes a sentence into fixed-length dimension embedding.
In the paper, there are two architectures proposed based on trade-offs in accuracy vs inference speed. In this variant, we use the encoder part of the original transformer architecture. The architecture consists of 6 stacked transformer layers.
Each layer has a self-attention module followed by a feed-forward network. The self-attention process takes word order and surrounding context into account when generating each word representation.