Word2vec: Text analysis through Word Embedding

In the turbulent world of automated text analysis, word embedding represents an important breakthrough. By converting individual words into numerical vectors, text is thereby transformed into a form that can be processed algorithmically. A particularly popular model is Word2vec, which deals with context and coherence of words. Since there are now many sophisticated analysis techniques with varying strengths, it makes sense to use a flexible runtime environment such as Konfuzio. This allows Word2vec to be combined with a wide variety of AI models for precise and customized text analysis.

What is Word2vec?

Word2vec is an AI technique that enables algorithmic text analysis by converting words into numerical vectors. This basic principle is called word embedding, and it is a proven means of putting text into a mathematically detectable form. It is used in different variants for a number of models, but finds one of its most popular implementations in Word2vec. It typically uses two-layer neural networks that process an input in the form of text corpora. The output is a vector set that can be understood by a deep neural network. Thus, Word2vec alone does not enable a fully comprehensive understanding of the text, but requires the interaction with other techniques for which it first prepares the text. The generic term for this type of speech analysis is natural language processing (NLP).

In Word Embedding, Word2vec focuses particularly on the semantics and relationships between words. These become detectable for the network through a supervised learning procedure in which large text corpora are fed in at the input layer. The basic assumption is that similar words are also frequently used in similar contexts, about which the model makes a probabilistic prediction based on the training data. Thus, for example, it can complete sentences, form synonyms, make recommendations in online stores, or generate search engine suggestions. Word2vec also originated in this environment: a team of researchers from Google developed the technology and introduced it in 2013. Today, some experts already consider it obsolete. In the NLP environment, Transformer models are now often preferred for these types of tasks.

word embedding pre-training google
One of the most popular Word2vec models has undergone pre-training with 100 billion words from Google News

How does Word Embedding work?

Word embedding is an important method for transforming text into a mathematically comprehensible form and forms the basis for Word2vec. Numerical vectors are created from individual words. These can represent by length and dimensions clearly more information about a word than single numbers, with which in the early time of NLP still one worked. First, the length of the vector is determined. It defines the amount of possible context information that can be mapped for the word. This also depends on the complexity and uniqueness of the word or possible usability for different contexts. The larger the vector, the more computationally intensive the processing will be. The dimensions of vectors are usually represented by numbers written on top of each other. In typical examples, these are often three pieces that make the vector representable in a coordinate system in three-dimensional space. In practice, however, word embeddings can have hundreds or even thousands of dimensions - depending on the size of the text corpus used.

Word Embedding is well suited to make relationships between words measurable. If they are similar, they are also close to each other in vector space. For example, if the words are "tree" and "flower", they match in their plantness. This characteristic can be coded within the vectors in a corresponding dimension. The more of them that are alike, the closer the vectors are in vector space and the more likely the associated words will be used in a similar context - as in the case of plantings. Word embedding models all use these principles, but differ in their technical or mathematical approach as well as their learning procedure - and thus in their strengths and weaknesses. GloVe, for example, a popular competitor of Word2vec, relies on matrix factorization for dimensionality reduction. Word2vec, on the other hand, usually uses the basic architecture of neural feedforward networks. But even here there are different variants.

word2vec model

2 model types for Word2vec

With the introduction of Word2vec, Google's research team directly introduced two concrete models, which have also remained the most relevant to this day. They differ in the functioning of the neural networks used. Therefore, they provide a different kind of output and can be used for different purposes accordingly.

The Continuous Bag-of-Words Model

The neural network used here focuses particularly on the syntactic relationships of words, which it extracts from an input word group. This can be structured in the form of a sentence, but the neural network considers the words independently of their order. It forms "bags of words" - word pairs are the simplest - to determine the most important noun. Considering the surrounding context words, it outputs a word that fits the context. Because both training and this transfer use the surrounding syntax as basic information, the output is often closely syntactically related to the main word when it does not match it. For example, one gets a different declension of it or a closely related word. Thus, the CBOW model uses context to determine a target word. In the second method, virtually the opposite is the case.

The Continuous Skip Gram Model

This model outputs multiple context words to a single input word, to which there is a semantic relationship. Both the logic and the architecture of the network behave inversely to CBOW. Its target word corresponds here in principle to the singular input, which meets a hidden neuron layer after the input. There the vector of the input word is computed with neuronal weights, which are adjusted on the basis of the pre-training. On this basis, a result is obtained at the output layer of the network consisting of some words or their vectors, which are used in similar contexts as the input word. In principle, this is a more complex performance compared to CBOW, but it is also more versatile. Significantly more applications therefore use the skip-gram model.

Word2vec: Classification in the NLP cosmos

As a subfield of artificial intelligence, it deals with natural language processing with automated speech analysis. For this purpose, it includes a large number of different techniques. Word2vec also shows why this is the case: the model is very specific in how it works and therefore in the tasks it can perform. Using neural networks, it performs vector-based word embedding and ultimately makes statements about the context and relationships of words. This may be very helpful for search engines and online stores, but it is only one component in the complex world of language analysis. It includes many sub-areas such as text classification, semantic analysis, text summarization and more.

Popular alternatives to Word2vec are models such as ELMo, which undergo similar training but are able to infer from one word to the next in text and retain previous contexts. With Konfuzio, however, there is no need to commit to one of these models. As Enterprise AI solution, Konfuzio is the only data-centric IDP Software automate even heterogeneous documents including multilingualism through the highly flexible choice of AI. In some cases, Word2vec offers the fastest solution for a specific problem and can be integrated into the runtime environment for this purpose. However, especially for the analysis of complex documents, any other AI model can also play its part. An important cornerstone for this today are Large Language Models (LLM), which are also applied to Konfuzio.

Large Language Models as a new pace setter

LLMs are large language models that have undergone very extensive pre-training with immense amounts of text and thus have the basic prerequisites for solving a wide variety of NLP problems. Through subsequent fine-tuning, LLMs can in principle perform the same tasks as Word2vec - and even achieve better results. However, they can also be used for almost any other part of NLP. Compared to pre-training, only manageable domain-specific data sets are required for fine-tuning. And yet, even industry-specific solutions can be developed for each individual task. Konfuzio uses such fine-tuning, for example, to tailor LLMs to specific document types such as delivery bills, payment advices or invoices. The combination with other NLP techniques such as Word2vec thus enables a fully comprehensive Document Understanding, with the accuracy of automated text analysis steadily increasing.

word2vec scanned text

Probably the best-known example of an LLM is ChatGPT. It is based on a modern GPT architecture, which differs from conventional neural networks such as Word2vec especially in its high complexity. "Generative Pretrained Transformers" are superior to Word2vec in its application domain, but Word Embeddings remain relevant because they are particularly fast to train and offer simple solutions. In addition, they expand the spectrum of applicable analysis techniques, so that industry-specific, precisely fitting results are also possible...

Application example: Automated text analysis in the insurance industry

With the help of Konfuzio, models such as Word2vec can be combined with large language models and various other techniques. This opens up unimagined possibilities for automated text analysis that do not even stop at very specific requirements. This can be seen, for example, in the insurance industry. There, in addition to the usual Invoices with very specialized documents whose manual processing is time-consuming and has a high potential for damage. For many of these cases, Konfuzio offers suitable automation approaches by putting corresponding AI models for analyzing text through individual training:

Policy Documents: With AI-based OCR technology from Konfuzio, insurers can analyze their competitors' policy documents in the blink of an eye. This enables them to compare and optimize their insurance offerings and conditions virtually in real time. This leads to a significantly shortened response time and a decisive advantage in the competitive insurance market.

Car registration documents: The automated analysis of registration documents is an uncomplicated way to open up additional sales channels or to optimize existing contracts without much additional effort. Through the flexible use of various AI techniques, any relevant text of automotive documents can be captured and prepared for further processing. For this accuracy, Konfuzio's AI OCR needs only 50 training samples.

In addition, Konfuzio offers solutions for almost every type of document processing that minimize errors, save resources and optimize processes - not only for insurance companies, but also for your company! If you want to know how Konfuzio can boost your business processes, feel free to leave us a message directly.

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