Proceedings of the Workshop Semantic Content Acquisition and Representation (SCAR) 2007

This is the proceedings of the Workshop on Semantic Content Acquisition and Representation, held in conjunction with NODALIDA 2007, on May 24 2007 in Tartu, Estonia.</p

A time-sensitive historical thesaurus-based semantic tagger for deep semantic annotation

Automatic extraction and analysis of meaning-related information from natural language data has been an important issue in a number of research areas, such as natural language processing (NLP), text mining, corpus linguistics, and data science. An important aspect of such information extraction and analysis is the semantic annotation of language data using a semantic tagger. In practice, various semantic annotation tools have been designed to carry out different levels of semantic annotation, such as topics of documents, semantic role labeling, named entities or events. Currently, the majority of existing semantic annotation tools identify and tag partial core semantic information in language data, but they tend to be applicable only for modern language corpora. While such semantic analyzers have proven useful for various purposes, a semantic annotation tool that is capable of annotating deep semantic senses of all lexical units, or all-words tagging, is still desirable for a deep, comprehensive semantic analysis of language data. With large-scale digitization efforts underway, delivering historical corpora with texts dating from the last 400 years, a particularly challenging aspect is the need to adapt the annotation in the face of significant word meaning change over time. In this paper, we report on the development of a new semantic tagger (the Historical Thesaurus Semantic Tagger), and discuss challenging issues we faced in this work. This new semantic tagger is built on existing NLP tools and incorporates a large-scale historical English thesaurus linked to the Oxford English Dictionary. Employing contextual disambiguation algorithms, this tool is capable of annotating lexical units with a historically-valid highly fine-grained semantic categorization scheme that contains about 225,000 semantic concepts and 4,033 thematic semantic categories. In terms of novelty, it is adapted for processing historical English data, with rich information about historical usage of words and a spelling variant normalizer for historical forms of English. Furthermore, it is able to make use of knowledge about the publication date of a text to adapt its output. In our evaluation, the system achieved encouraging accuracies ranging from 77.12% to 91.08% on individual test texts. Applying time-sensitive methods improved results by as much as 3.54% and by 1.72% on average

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

PersoNER: Persian named-entity recognition

© 1963-2018 ACL. Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network

Computational Sociolinguistics: A Survey

Language is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics (CL) community in the social dimension of language. In this article we present a survey of the emerging field of "Computational Sociolinguistics" that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions employed in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication: 18th February, 201