Document Classification by Inversion of Distributed Language Representations
There have been many recent advances in the structure and measurement of distributed language models: those that map from words to a vector-space that is rich in information about word choice and composition. This vector-space is the distributed language representation. The goal of this note is to point out that any distributed representation can be turned into a classifier through inversion via Bayes rule. The approach is simple and modular, in that it will work with any language representation whose training can be formulated as optimizing a probability model. In our application to 2 million sentences from Yelp reviews, we also find that it performs as well as or better than complex purpose-built algorithms.
Code (1)
Tasks
ClassificationDocument ClassificationGeneral ClassificationSimilar Papers 제목 키워드 기반
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
In this paper we propose a general framework for learning distributed representations of attributes: characteristics of text whose representations can be jointly learned with word embeddings. Attributes can correspond to…
AttributeAuthorship AttributionCross-Lingual Document ClassificationDocument Classification+7Recurrent Neural Network Language Model Adaptation Derived Document Vector
In many natural language processing (NLP) tasks, a document is commonly modeled as a bag of words using the term frequency-inverse document frequency (TF-IDF) vector. One major shortcoming of the frequency-based TF-IDF f…
General ClassificationGenre classificationLanguage ModelingLanguage ModellingImprove Document Embedding for Text Categorization Through Deep Siamese Neural Network
Due to the increasing amount of data on the internet, finding a highly-informative, low-dimensional representation for text is one of the main challenges for efficient natural language processing tasks including text cla…
ClassificationDocument ClassificationDocument EmbeddingGeneral Classification+3Derivation of Document Vectors from Adaptation of LSTM Language Model
In many natural language processing (NLP) tasks, a document is commonly modeled as a bag of words using the term frequency-inverse document frequency (TF-IDF) vector. One major shortcoming of the frequency-based TF-IDF f…
General ClassificationGenre classificationLanguage ModelingLanguage ModellingMultilingual Distributed Representations without Word Alignment
Distributed representations of meaning are a natural way to encode covariance relationships between words and phrases in NLP. By overcoming data sparsity problems, as well as providing information about semantic relatedn…
Cross-Lingual Document ClassificationDocument ClassificationSentenceSentiment Analysis+1