You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
The bag-of-words model is a standard representation of text for many linear classifier learners. In many problem domains, linear classifiers are preferred over more complex models due to their efficiency, robustness and interpretability, and the bag-of-words text representation can capture sufficient information for linear classifiers to make highly accurate predictions. However in settings where there is a large vocabulary, large variance in the frequency of terms in the training corpus, many classes and very short text (e.g., single sentences or document titles) the bag-of-words representation becomes extremely sparse, and this can reduce the accuracy of classifiers. A particular issue in such settings is that short texts tend to contain infrequently occurring or rare terms which lack class-conditional evidence. In this work we introduce a method for enriching the bag-of-words model by complementing such rare term information with related terms from both general and domain-specific Word Vector models. By reducing sparseness in the bag-of-words models, our enrichment approach achieves improved classification over several baseline classifiers in a variety of text classification problems. Our approach is also efficient because it requires no change to the linear classifier before or during training, since bag-of-words enrichment applies only to text being classified.

Cited Abstract(s):
[1]: 

[2]: Unsupervised vector-based approaches to semantics can model rich lexical meanings, but they largely fail to capture sentiment information that is central to many word meanings and important for a wide range of NLP tasks. We present a model that uses a mix of unsupervised and supervised techniques to learn word vectors capturing semantic term--document information as well as rich sentiment content. The proposed model can leverage both continuous and multi-dimensional sentiment information as well as non-sentiment annotations. We instantiate the model to utilize the document-level sentiment polarity annotations present in many online documents (e.g. star ratings). We evaluate the model using small, widely used sentiment and subjectivity corpora and find it out-performs several previously introduced methods for sentiment classification. We also introduce a large dataset of movie reviews to serve as a more robust benchmark for work in this area.

[3]: 

[4]: Wikipedia provides a knowledge base for computing word relatedness in a more structured fashion than a search engine and with more coverage than WordNet. In this work we present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datasets. Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet when applied to the largest available dataset designed for that purpose. The best results on this dataset are obtained by integrating Google, WordNet and Wikipedia based measures. We also show that including Wikipedia improves the performance of an NLP application processing naturally occurring texts.

[5]: 

[6]: 1. ABSTRACT Text categorization – the assignment of natural language texts to one or more predefined categories based on their content – is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text categorization in terms of learning speed, realtime classification speed, and classification accuracy. We also examine training set size, and alternative document representations. Very accurate text classifiers can be learned automatically from training examples. Linear Support Vector Machines (SVMs) are particularly promising because they are very accurate, quick to train, and quick to evaluate. 1.1

[7]: Document classification presents difficult challenges due to the sparsity and the high dimensionality of text data, and to the complex semantics of the natural language. The traditional document representation is a word-based vector (Bag of Words, or BOW), where each dimension is associated with a term of the dictionary containing all the words that appear in the corpus. Although simple and commonly used, this representation has several limitations. It is essential to embed semantic information and conceptual patterns in order to enhance the prediction capabilities of classification algorithms. In this paper, we overcome the shortages of the BOW approach by embedding background knowledge derived from Wikipedia into a semantic kernel, which is then used to enrich the representation of documents. Our empirical evaluation with real data sets demonstrates that our approach successfully achieves improved classification accuracy with respect to the BOW technique, and to other recently developed methods.

[8]: 

[9]: Abstract : A probabilistic analysis of the Rocchio relevance feedback algorithm, one of the most popular learning methods from information retrieval, is presented in a text categorization framework. The analysis results in a probabilistic version of the Rocchio classifier and offers an explanation for the TFIDF word weighting heuristic. The Rocchio classifier, its probabilistic variant and a standard naive Bayes classifier are compared on three text categorization tasks. The results suggest that the probabilistic algorithms are preferable to the heuristic Rocchio classifier.

[10]: Traditional term weighting schemes in text categorization, such as TF-IDF, only exploit the statistical information of terms in documents. Instead, in this paper, we propose a novel term weighting scheme by exploiting the semantics of categories and indexing terms. Specifically, the semantics of categories are represented by senses of terms appearing in the category labels as well as the interpretation of them by WordNet. Also, the weight of a term is correlated to its semantic similarity with a category. Experimental results on three commonly used data sets show that the proposed approach outperforms TF-IDF in the cases that the amount of training data is small or the content of documents is focused on well-defined categories. In addition, the proposed approach compares favorably with two previous studies.
A number of approaches for transforming the bag-of-words model or modifying linear models to increase the classification accuracy have been proposed in previous works. These approaches generally involve: 1) Reducing the dimensionality of models by removing stop words [1] , stemming terms [5] and removing the least frequent words [8] ; 2) Transforming word frequencies by TF-IDF and other measures to identify the key terms in particular classes [9] ; 3) Limiting classes to a maximum vocabulary size through methods such as mutual information metrics [6] ; 4) Using dictionaries [2] [1] [3] and encyclopaedias [4] [7] to find synonyms for rare terms. However, the names of people and organisations are unlikely to occur in dictionaries [10] [1] and may be used rarely in training text.