Text classification is a crucial task encountered frequently in practical scenarios, yet it is still under-explored in the era of large language models (LLMs). This study shows that LLMs are vulnerable to changes in the number and arrangement of options in text classification. Our extensive empirical analyses reveal that the key bottleneck arises from ambiguous decision boundaries and inherent biases towards specific tokens and positions. To mitigate these issues, we make the first attempt and propose a novel two-stage classification framework for LLMs. Our approach is grounded in the empirical observation that pairwise comparisons can effectively alleviate boundary ambiguity and inherent bias. Specifically, we begin with a self-reduction technique to efficiently narrow down numerous options, which contributes to reduced decision space and a faster comparison process. Subsequently, pairwise contrastive comparisons are employed in a chain-of-thought manner to draw out nuances and distinguish confusable options, thus refining the ambiguous decision boundary. Extensive experiments on four datasets (Banking77, HWU64, LIU54, and Clinic150) verify the effectiveness of our framework. Furthermore, benefitting from our framework, various LLMs can achieve consistent improvements. Our code and data are available in \url{https://github.com/Chuge0335/PC-CoT}.

This paper presents the novel way combining the BERT embedding method and the graph convolutional neural network. This combination is employed to solve the text classification problem. Initially, we apply the BERT embedding method to the texts (in the BBC news dataset and the IMDB movie reviews dataset) in order to transform all the texts to numerical vector. Then, the graph convolutional neural network will be applied to these numerical vectors to classify these texts into their ap-propriate classes/labels. Experiments show that the performance of the graph convolutional neural network model is better than the perfor-mances of the combination of the BERT embedding method with clas-sical machine learning models.

Deep Learning, particularly Natural Language Processing (NLP), has been gathering a huge interest nowadays. Some time ago, there was an NLP competition on Kaggle called Quora Question insincerity challenge. The competition is a text classification problem and it becomes easier to understand after working through the competition, as well as by going through the invaluable kernels put up by the Kaggle experts. First, let's start by explaining a little more about the text classification problem in the competition. Text classification is a common task in natural language processing, which transforms a sequence of a text of indefinite length into a category of text.

I chose to use the AG news benchmark dataset. I recuperated the training and test test from John Snow Labs (a must see reference for all things NLP). This dataset is divided into four balanced categories for a total of 120,000 rows as seen below. The dataset is formatted into 2 columns, category and description. Because I want this to be a succinct post, I will refer you to my previous article to find out how to use Spark NLP in Colab.

We propose a new approach to address the text classification problems when learning with partial labels is beneficial. Instead of offering each training sample a set of candidate labels, we assign negative-oriented labels to the ambiguous training examples if they are unlikely fall into certain classes. We construct our new maximum likelihood estimators with self-correction property, and prove that under some conditions, our estimators converge faster. Also we discuss the advantages of applying one of our estimator to a fully supervised learning problem. The proposed method has potential applicability in many areas, such as crowdsourcing, natural language processing and medical image analysis.

Mining such data to determine how people feel about your product, brand, or service, is called Sentiment Analysis. When applied to social media channels, it can be used to identify spikes in sentiment, thereby allowing you to identify potential product advocates or social media influencers. Companies such as Microsoft, IBM and smaller emerging companies offer REST APIs that integrate easily with your existing software applications. For example, using the following publicly available Sentiment Analysis REST API from a small start-up called Social Opinion, we pass in the text, "this phone is awesome", to the following URL: In the response, we can see the text has been identified as expressing positive emotion, with a 64% probability of that being true.