Implementation methods and related technology evolution of zero-sample text classification

Zero-sample document classification refers to classifying documents of a certain category without seeing training samples of that category. This problem is very common in practical applications because many times we cannot obtain samples of all possible categories. Therefore, zero-shot document classification is a very important text classification problem. In zero-shot document classification, we can classify using existing training samples and semantic information of categories. A common approach is to use word vectors to represent documents and categories, and then perform classification by calculating the similarity between documents and categories. Another approach is to use a knowledge graph or an external knowledge base to map documents and categories to entities or concepts in the knowledge graph and then classify them through relationships on the graph. Zero-shot document classification has wide applications in many fields. In the field of information retrieval, it can help users quickly find relevant documents

What is zero-shot document classification?

In traditional text classification tasks, a set of training samples that have been labeled with categories are usually used to train a classifier, and then the classifier is used to classify new documents. However, in zero-shot document classification, no training samples of any known class are available. Therefore, we need to adopt other methods to classify documents of unknown categories. In this case, zero-shot learning methods can be used. For example, zero-shot learning performs classification by associating samples of known categories with samples of unknown categories. Another approach is to use transfer learning, which uses existing training models and knowledge to classify documents of unknown categories. In addition, you can also consider using generative models to generate new samples for classification. In summary, zero-shot document classification is a challenging task that requires the help of other methods to handle the situation where there are no training samples of known categories.

Zero-sample document classification method

1. Word vector-based method

Based on The word vector method is a commonly used zero-shot document classification method. Its basic idea is to learn a word vector space by using training samples of known categories, and then use this space to represent documents of unknown categories. Specifically, for each document, we can represent it as a vector consisting of word vectors. We can then use the word vectors in the training samples of known categories to compare them with the word vectors in the document to be classified to determine its category. Usually, we can use some similarity measures, such as cosine similarity, to measure the degree of similarity between documents. If the document to be classified has a high similarity with the training samples of a certain category, then we can classify it into that category. In this way, word vector-based methods can achieve classification of documents of unknown categories.

There are many different variations of word vector-based methods, the most common of which are based on pre-trained word vectors. This method uses pre-trained word vectors, such as Word2Vec or GloVe, to learn the word vector space. We can then use this space to represent documents and train a classifier using training samples of known categories. For a document of an unknown category, we can determine its category by comparing its word vector representation with that of a training sample of a known category.

2. Knowledge graph-based method

The knowledge graph-based method is another commonly used zero-sample document classification method. The basic idea of ​​this method is to use the semantic information in training samples of known categories to construct a knowledge graph, and then use this knowledge graph to represent documents. For documents of unknown categories, we can represent them as nodes in the knowledge graph and use nodes of known categories in the graph for classification.

The method based on knowledge graph requires semantic analysis and knowledge extraction of training samples, so it is more complicated. However, it can capture the high-level semantic information of the document and therefore achieve better classification results in some cases.

3. Meta-learning based method

Meta-learning based method is a recently proposed zero-shot document classification method. The basic idea of ​​this method is to use training samples of known categories to train a meta-classifier that can predict the category of a document based on its meta-features (such as the length of the document, word frequency distribution, etc.). Then, for a document of unknown category, we can use a meta-classifier to predict its category.

Meta-learning based methods require a large number of training samples and computing resources, but can accurately classify documents of unknown categories.

Application of zero-shot document classification

Zero-shot document classification has a wide range of applications in the field of natural language processing, such as:

1. Multilingual text classification

In the case of multiple languages, we may not be able to obtain training samples in all languages. Therefore, zero-shot document classification can be used to classify text in unknown languages.

2. News classification

In news classification, a variety of news topics appear every day, and it is difficult to obtain training samples for all topics. Therefore, zero-shot document classification can be used to classify new topics.

3. Product classification

In the field of e-commerce, we may encounter new product categories, and it is difficult to obtain all categories Training samples. Therefore, zero-shot document classification can be used to classify new product categories.

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