Sentiment Classification of Russian Texts Using Automatically Generated Thesaurus

This paper is devoted to an approach for sentiment classification of Russian texts applying an automatic thesaurus of the subject area. This approach consists of a standard machine learning classifier and a procedure embedded into it, that uses the- saurus relationships for better sentiment analysis. The thesaurus is generated fully automatically and does not require expert’s involvement into classification process. Experiments conducted with the approach and four Russian-language text corpora, show effectiveness of thesaurus application to sentiment classification

Sentiment classification of long newspaper articles based on automatically generated thesaurus with various semantic relationships

The paper describes a new approach for sentiment classification of long texts from newspapers using an automatically generated thesaurus. An important part of the proposed approach is specialized thesaurus creation and computation of term's sentiment polarities based on relationships between terms. The approach's efficiency has been proved on a corpus of articles about American immigrants. The experiments showed that the automatically created thesaurus provides better classification quality than manual ones, and generally for this task our approach outperforms existing ones

Using distributional similarity to organise biomedical terminology

We investigate an application of distributional similarity techniques to the problem of structural organisation of biomedical terminology. Our application domain is the relatively small GENIA corpus. Using terms that have been accurately marked-up by hand within the corpus, we consider the problem of automatically determining semantic proximity. Terminological units are dened for our purposes as normalised classes of individual terms. Syntactic analysis of the corpus data is carried out using the Pro3Gres parser and provides the data required to calculate distributional similarity using a variety of dierent measures. Evaluation is performed against a hand-crafted gold standard for this domain in the form of the GENIA ontology. We show that distributional similarity can be used to predict semantic type with a good degree of accuracy

Human-Level Performance on Word Analogy Questions by Latent Relational Analysis

This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, machine translation, and information retrieval. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason/stone is analogous to the pair carpenter/wood; the relations between mason and stone are highly similar to the relations between carpenter and wood. Past work on semantic similarity measures has mainly been concerned with attributional similarity. For instance, Latent Semantic Analysis (LSA) can measure the degree of similarity between two words, but not between two relations. Recently the Vector Space Model (VSM) of information retrieval has been adapted to the task of measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus (they are not predefined), (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data (it is also used this way in LSA), and (3) automatically generated synonyms are used to explore reformulations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying noun-modifier relations, LRA achieves similar gains over the VSM, while using a smaller corpus

Learning to distinguish hypernyms and co-hyponyms

This work is concerned with distinguishing different semantic relations which exist between distributionally similar words. We compare a novel approach based on training a linear Support Vector Machine on pairs of feature vectors with state-of-the-art methods based on distributional similarity. We show that the new supervised approach does better even when there is minimal information about the target words in the training data, giving a 15% reduction in error rate over unsupervised approaches

Sentiment Classification into Three Classes Applying Multinomial Bayes Algorithm, N-grams, and Thesaurus

The paper is devoted to development of the method that classi?es texts in English and Russian by sentiments into positive, negative, and neutral. The proposed method is based on the Multinomial Naive Bayes classi?er with additional n-grams application. The classi?er is trained either on three classes, or on two contrasting classes with a threshold to separate neutral texts. Experiments with texts on various topics showed signi?cant improvement of classification quality for reviews from a particular domain. Besides, the analysis of thesaurus relationships application to sentiment classification into three classes was done, however it did not show significant improvement of the classification results

Electronic Disclosure and Financial Knowledge Management

In this paper we report the benefits of using eXtended Markup Language (XML) to support financial knowledge management, which include indexing, organizing, association generation, cross-referencing, and retrieval of financial information to support the generation of knowledge. The current searching engines cannot provide sufficient performance, such as, recall, precision, extensibility, etc, to support users of financial information. XML is able to partially solve such problem by providing tags to create structures. XML provides a vendor-neutral approach to structure and organize contents. XML authors are allowed to create arbitrary tags to describe the format or structure of data, rather than restricted to a specific number of tags given in the specification of HTML. A prototype of XML-based ELectronic Financial Filing System (ELFFS-XML) has been developed to illustrate how to apply XML to model and add value to traditional HTML-based financial information by cross-linking related information from different data sources, which is an important step in moving from traditional information management to knowledge management. We compared the functionality of XML-based ELFFS with the original HTML-based ELFFS and SEDAR, an electronic filing system used in Canada, and recommended some directions for future development of similar electronic filing systems

Rječnik suvremenoga slovenskog jezika: od slovenske leksičke baze do digitalne rječničke baze

The ability to process language data has become fundamental to the development of technologies in various areas of human life in the digital world. The development of digitally readable linguistic resources, methods, and tools is, therefore, also a key challenge for the contemporary Slovene language. This challenge has been recognized in the Slovene language community both at the professional and state level and has been the subject of many activities over the past ten years, which will be presented in this paper. The idea of a comprehensive dictionary database covering all levels of linguistic description in modern Slovene, from the morphological and lexical levels to the syntactic level, has already formulated within the framework of the European Social Fund’s Communication in Slovene (2008-2013) project; the Slovene Lexical Database was also created within the framework of this project. Two goals were pursued in designing the Slovene Lexical Database (SLD): creating linguistic descriptions of Slovene intended for human users that would also be useful for the machine processing of Slovene. Ever since the construction of the first Slovene corpus, it has become evident that there is a need for a description of modern Slovene based on real language data, and that it is necessary to understand the needs of language users to create useful language reference works. It also became apparent that only the digital medium enables the comprehensiveness of language description and that the design of the database must be adapted to it from the start. Also, the description must follow best practices as closely as possible in terms of formats and international standards, as this enables the inclusion of Slovene into a wider network of resources, such as Open Linked Data, babelNet and ELExIS. Due to time pressures and trends in lexicography, procedures to automate the extraction of linguistic data from corpora and the inclusion of crowdsourcing into the lexicographic process were taken into consideration. Following the essential idea of creating an all-inclusive digital dictionary database for Slovene, a few independent databases have been created over the past two years: the Collocations Dictionary of Modern Slovene, and the automatically generated Thesaurus of Modern Slovene, both of which also exist as independent online dictionary portals. One of the novelties that we put forward together with both dictionaries is the ‘responsive dictionary’ concept, which includes crowdsourcing methods. Ultimately, the Digital Dictionary Database provides all (other) levels of linguistic description: the morphological level with the Sloleks database upgrade, the phraseological level with the construction of a multi-word expressions lexicon, and the syntactic level with the formalization of Slovene verb valency patterns. Each of these databases contains its specific language data that will ultimately be included in the comprehensive Slovene Digital Dictionary Database, which will represent basic linguistic descriptions of Slovene both for the human and machine user.Ideja sveobuhvatne rječničke baze koja uključuje sve razine jezičnoga opisa suvremenoga slovenskog jezika od morfološke i leksičke do sintaktičke prvotno je formulirana u okviru projekta Sporazumijevanje na slovenskomu jeziku (2008. – 2013.). U cilju ostvarenja ideje o stvaranju sveobuhvatne digitalne rječničke baze stvorene su dvije neovisne baze podataka: Kolokacijski rječnik suvremenoga slovenskoga jezika i automatski generiran Tezaurus modernoga slovenskoga jezika. Jedna od novina u obama rječnicima koncept je responzivnoga rječnika, koji uključuje masovnu podršku. Digitalna rječnička baza sadržava sve razine jezičnoga opisa: morfološku nadograđenu Sloleksom, izraznu s opisom konstrukcija višerječnih jedinica te sintaktičku s formalizacijom modela glagolskih valencija. Svaka od postojećih baza podataka sadržava specifične jezične podatke koji će biti uključeni u sveobuhvatnu Slovensku digitalnu rječničku bazu podataka, koja će sadržavati temeljni jezikoslovni opis slovenskoga jezika čiji korisnici mogu biti ljudi i strojevi

Could we automatically reproduce semantic relations of an information retrieval thesaurus?

A well constructed thesaurus is recognized as a valuable source of semantic information for various applications, especially for Information Retrieval. The main hindrances to using thesaurus-oriented approaches are the high complexity and cost of manual thesauri creation. This paper addresses the problem of automatic thesaurus construction, namely we study the quality of automatically extracted semantic relations as compared with the semantic relations of a manually crafted thesaurus. The vector-space model based on syntactic contexts was used to reproduce relations between the terms of a manually constructed thesaurus. We propose a simple algorithm for representing both single word and multiword terms in the distributional space of syntactic contexts. Furthermore, we propose a method for evaluation quality of the extracted relations. Our experiments show significant difference between the automatically and manually constructed relations: while many of the automatically generated relations are relevant, just a small part of them could be found in the original thesaurus

Experiments on the difference between semantic similarity and relatedness

Proceedings of the 17th Nordic Conference of Computational Linguistics NODALIDA 2009. Editors: Kristiina Jokinen and Eckhard Bick. NEALT Proceedings Series, Vol. 4 (2009), 81-88. © 2009 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/9206