From Word to Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence Researc

Improving Sentiment Analysis of Short Informal Indonesian Product Reviews using Synonym Based Feature Expansion

Sentiment analysis in short informal texts like product reviews is more challenging. Short texts are sparse, noisy, and lack of context information. Traditional text classification methods may not be suitable for analyzing sentiment of short texts given all those difficulties. A common approach to overcome these problems is to enrich the original texts with additional semantics to make it appear like a large document of text. Then, traditional classification methods can be applied to it. In this study, we developed an automatic sentiment analysis system of short informal Indonesian texts using Naïve Bayes and Synonym Based Feature Expansion. The system consists of three main stages, preprocessing and normalization, features expansion and classification. After preprocessing and normalization, we utilize Kateglo to find some synonyms of every words in original texts and append them. Finally, the text is classified using Naïve Bayes. The experiment shows that the proposed method can improve the performance of sentiment analysis of short informal Indonesian product reviews. The best sentiment classification performance using proposed feature expansion is obtained by accuracy of 98%.The experiment also show that feature expansion will give higher improvement in small number of training data than in the large number of them

An efficient Particle Swarm Optimization approach to cluster short texts

This is the author’s version of a work that was accepted for publication in Information Sciencies. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, VOL 265, MAY 1 2014 DOI 10.1016/j.ins.2013.12.010.Short texts such as evaluations of commercial products, news, FAQ's and scientific abstracts are important resources on the Web due to the constant requirements of people to use this on line information in real life. In this context, the clustering of short texts is a significant analysis task and a discrete Particle Swarm Optimization (PSO) algorithm named CLUDIPSO has recently shown a promising performance in this type of problems. CLUDIPSO obtained high quality results with small corpora although, with larger corpora, a significant deterioration of performance was observed. This article presents CLUDIPSO*, an improved version of CLUDIPSO, which includes a different representation of particles, a more efficient evaluation of the function to be optimized and some modifications in the mutation operator. Experimental results with corpora containing scientific abstracts, news and short legal documents obtained from the Web, show that CLUDIPSO* is an effective clustering method for short-text corpora of small and medium size. (C) 2013 Elsevier Inc. All rights reserved.The research work is partially funded by the European Commission as part of the WIQ-EI IRSES research project (Grant No. 269180) within the FP 7 Marie Curie People Framework and it has been developed in the framework of the Microcluster VLC/Campus (International Campus of Excellence) on Multimodal Intelligent Systems. The research work of the first author is partially funded by the program PAID-02-10 2257 (Universitat Politecnica de Valencia) and CONICET (Argentina).Cagnina, L.; Errecalde, M.; Ingaramo, D.; Rosso, P. (2014). An efficient Particle Swarm Optimization approach to cluster short texts. Information Sciences. 265:36-49. https://doi.org/10.1016/j.ins.2013.12.010S364926

On the Impact of Entity Linking in Microblog Real-Time Filtering

Microblogging is a model of content sharing in which the temporal locality of posts with respect to important events, either of foreseeable or unforeseeable nature, makes applica- tions of real-time filtering of great practical interest. We propose the use of Entity Linking (EL) in order to improve the retrieval effectiveness, by enriching the representation of microblog posts and filtering queries. EL is the process of recognizing in an unstructured text the mention of relevant entities described in a knowledge base. EL of short pieces of text is a difficult task, but it is also a scenario in which the information EL adds to the text can have a substantial impact on the retrieval process. We implement a start-of-the-art filtering method, based on the best systems from the TREC Microblog track realtime adhoc retrieval and filtering tasks , and extend it with a Wikipedia-based EL method. Results show that the use of EL significantly improves over non-EL based versions of the filtering methods.Comment: 6 pages, 1 figure, 1 table. SAC 2015, Salamanca, Spain - April 13 - 17, 201

Exploiting extensible background knowledge for clustering-based automatic keyphrase extraction

Keyphrases are single- or multi-word phrases that are used to describe the essential content of a document. Utilizing an external knowledge source such as WordNet is often used in keyphrase extraction methods to obtain relation information about terms and thus improves the result, but the drawback is that a sole knowledge source is often limited. This problem is identified as the coverage limitation problem. In this paper, we introduce SemCluster, a clustering-based unsupervised keyphrase extraction method that addresses the coverage limitation problem by using an extensible approach that integrates an internal ontology (i.e., WordNet) with other knowledge sources to gain a wider background knowledge. SemCluster is evaluated against three unsupervised methods, TextRank, ExpandRank, and KeyCluster, and under the F1-measure metric. The evaluation results demonstrate that SemCluster has better accuracy and computational efficiency and is more robust when dealing with documents from different domains

End-to-end Learning for Short Text Expansion

Effectively making sense of short texts is a critical task for many real world applications such as search engines, social media services, and recommender systems. The task is particularly challenging as a short text contains very sparse information, often too sparse for a machine learning algorithm to pick up useful signals. A common practice for analyzing short text is to first expand it with external information, which is usually harvested from a large collection of longer texts. In literature, short text expansion has been done with all kinds of heuristics. We propose an end-to-end solution that automatically learns how to expand short text to optimize a given learning task. A novel deep memory network is proposed to automatically find relevant information from a collection of longer documents and reformulate the short text through a gating mechanism. Using short text classification as a demonstrating task, we show that the deep memory network significantly outperforms classical text expansion methods with comprehensive experiments on real world data sets.Comment: KDD'201