Abstract: Word clusters have been empirically shown to offer important performance improvements on various tasks. Despite their importance, their incorporation in the standard pipeline of feature engineering relies more on a trial-and-error procedure where one evaluates several hyper-parameters, like the number of clusters to be used. In order to better understand the role of such features we systematically evaluate their effect on four tasks, those of named entity segmentation and classification as well as, those of five-point sentiment classification and quantification. Our results strongly suggest that cluster membership features improve the performance.

Introduction: Many research attempts have proposed novel features that improve the performance of learning algorithms in particular tasks. Such features are often motivated by domain knowledge or manual labor. Although useful and often state-of-the-art, adapting such solutions on NLP systems across tasks can be tricky and time-consuming BIBREF0 . Therefore, simple yet general and powerful methods that perform well across several datasets are valuable BIBREF1 . An approach that has become extremely popular lately in NLP tasks, is to train word embeddings in an unsupervised way. These embeddings are dense vectors that project words or short text spans like phrases in a vector space where dimensions are supposed to capture text properties. Such embeddings can then be used either as features with off-the-shelf algorithms like Support Vector Machines, or to initialize deep learning systems BIBREF2 . However, as shown in BIBREF3 linear architectures perform better in high-dimensional discrete spaces compared to continuous ones. The latter is probably the main reason of the high performance of the vector space model BIBREF4 in tasks like text classification with linear models like SVMs. Using linear algorithms, while taking advantage of the expressiveness of text embeddings is the focus of this work. In this paper, we explore a hybrid approach, that uses text embeddings as a proxy to create features. Motivated by the argument that text embeddings manage to encode the semantics of text, we explore how clustering text embeddings can impact the performance of different NLP tasks. Although such an approach has been used in different studies during feature engineering, the selection of word vectors and the number of clusters remain a trial-end-error procedure. In this work we present an empirical evaluation across diverse tasks to verify whether and when such features are useful. Word clusters have been used as features in various tasks like Part-of-Speech tagging and NER. Owoputi et al. Owoputi13 use Brown clusters BIBREF5 in a POS tagger showing that this type of features carry rich lexical knowledge as they can substitute lexical resources like gazetteers. Kiritchenko et al. KiritchenkoZM14 discusses their use on sentiment classification while Hee et al. HeeLH16 incorporate them in the task of irony detection in Twitter. Ritter et al. Ritter2011 inject also word clusters in a NER tagger. While these works show that word clusters are beneficial no clear guidelines can be concluded of how and when to use them. In this work, we empirically demonstrate that using different types of embeddings on three NLP tasks with twitter data we manage to achieve better or near to the state-of-the art performance on three NLP tasks: (i) Named Entity Recognition (NER) segmentation, (ii) NER classification, (iii) fine-grained sentiment analysis and (iv) fine-grained sentiment quantification. For each of the three tasks, we achieve higher performance than without using features which indicates the effectiveness of the cluster membership features. Importantly, our evaluation compared to previous work BIBREF6 who focus on old and well studied datasets uses recent and challenging datasets composed by tweets. The obtained results across all the tasks permits us to reveal important aspects of the use of word clusters and therefore provide guidelines. Although our obtained scores are state-of-the-art, our analysis reveals that the performance in such tasks is far from perfect and, hence, identifies that there is still much space for improvement and future work.

Question: Which hyperparameters were varied in the experiments on the four tasks?
Answer: number of clusters, seed value in clustering, selection of word vectors, window size and dimension of embedding