Proceedings of the third workshop on NLP for computer-assisted language learning at SLTC 2014, Uppsala University
The workshop series on NLP for Computer-Assisted Language Learning (NLP4CALL) is a meeting place for researchers working on the integration of Natural Language Processing and Speech technologies in CALL systems and exploring the theoretical and methodological issues arising in this connection. The papers in the proceedings volume from the third NLP4CALL workshop cover three main topic areas: resources for development of ICALL applications (e.g., learner corpora and coursebook corpora), tools and algorithms for the analysis of learner language (e.g., focusing on collocations, reading tasks, cloze items, pronunciation, spelling, level classification of learner production), and the generation of learning materials (e.g., exercise generators).
The paper describes the learner corpus composed of English essays written by native Russian speakers. REALEC (Russian Error-Annotated Learner English Corpus) is an error-annotated, available online corpus, now containing more than 200 thousand word tokens in almost 800 essays. It is one of the first Russian ESL corpora, dynamically developing and striving to improve both in size and in features offered to users. We describe our perspective on the corpus, data sources and tools used in compiling it. Elaborate self-made classification of learners’ errors types is thoroughly described. The paper also presents a pilot experiment on creating test sets for particular learners’ problems using corpus data.
Concept discovery is a Knowledge Discovery in Databases (KDD) research field that uses human-centered techniques such as Formal Concept Analysis (FCA), Biclustering, Triclustering, Conceptual Graphs etc. for gaining insight into the underlying conceptual structure of the data. Traditional machine learning techniques are mainly focusing on structured data whereas most data available resides in unstructured, often textual, form. Compared to traditional data mining techniques, human-centered instruments actively engage the domain expert in the discovery process. This volume contains the contributions to CDUD 2011, the International Workshop on Concept Discovery in Unstructured Data (CDUD) held in Moscow. The main goal of this workshop was to provide a forum for researchers and developers of data mining instruments working on issues with analyzing unstructured data. We are proud that we could welcome 13 valuable contributions to this volume. The majority of the accepted papers described innovative research on data discovery in unstructured texts. Authors worked on issues such as transforming unstructured into structured information by amongst others extracting keywords and opinion words from texts with Natural Language Processing methods. Multiple authors who participated in the workshop used methods from the conceptual structures field including Formal Concept Analysis and Conceptual Graphs. Applications include but are not limited to text mining police reports, sociological definitions, movie reviews, etc.
Software system Cordiet-FCA is presented, which is designed for knowledge discovery in big dynamic data collections, including texts in natural language. Cordiet-FCA allows one to compose ontology-controlled queries and outputs concept lattice, implication bases, association rules, and other useful concept-based artifacts. Efficient algorithms for data preprocessing, text processing, and visualization of results are discussed. Examples of applying the system to problems of medical diagnostics, criminal investigations are considered.
Proceeding of the 15th International Conference on Artificial Intelligence: Methodology, Systems, Applications , AIMSA 2012, Varna, Bulgaria, September 12-15, 2012.
This paper is an overview of the current issues and tendencies in Computational linguistics. The overview is based on the materials of the conference on computational linguistics COLING’2012. The modern approaches to the traditional NLP domains such as pos-tagging, syntactic parsing, machine translation are discussed. The highlights of automated information extraction, such as fact extraction, opinion mining are also in focus. The main tendency of modern technologies in Computational linguistics is to accumulate the higher level of linguistic analysis (discourse analysis, cognitive modeling) in the models and to combine machine learning technologies with the algorithmic methods on the basis of deep expert linguistic knowledge.
Compared with the area of spatial relations force interactions haven’t been in the limelight of attention of ontologists working on natural language processing. This article gives an example of text meaning representation based on the ontology and the lexicon of force interactions.
In this paper, we consider opinion word extraction, one of the key problems in sentiment analysis. Sentiment analysis (or opinion mining) is an important research area within computational linguistics. Opinion words, which form an opinion lexicon, describe the attitude of the author towards certain opinion targets, i.e., entities and their attributes on which opinions have been expressed. Hence, the availability of a representative opinion lexicon can facilitate the extraction of opinions from texts. For this reason, opinion word mining is one of the key issues in sentiment analysis. We designed and implemented several methods for extracting opinion words. We evaluated these approaches by testing how well the resulting opinion lexicons help improve the accuracy of methods for determining the polarity of the reviews if the extracted opinion words are used as features. We used several machine learning methods: SVM, Logistic Regression, Naive Bayes, and KNN. By using the extracted opinion words as features we were able to improve over the baselines in some cases. Our experiments showed that, although opinion words are useful for polarity detection, they are not su fficient on their own and should be used only in combination with other features.
The CCIS series is devoted to the publication of proceedings of computer science conferences. Its aim is to efficiently disseminate original research results in informatics in printed and electronic form. While the focus is on publication of peer-reviewed full papers presenting mature work, inclusion of reviewed short papers reporting on work in progress is welcome, too. Besides globally relevant meetings with internationally representative program committees guaranteeing a strict peer-reviewing and paper selection process, conferences run by societies or of high regional or national relevance are also considered for publication.
The paper concerns discourse-new referent detection. The task of coreference resolution is essential in many text-mining applications. The focus in this task is to detect noun phrases (NPs) that refer to the same entity. In languages without articles, there are no overt grammatical clues in an NP for whether it introduces a new referent into discourse or it refers to one of before-mentioned entities. However, there are some theoretical researches which claim that referent first-mentioning NPs have some specific features. In our research, we examine features that serve as discourse-new detectors for NPs corresponding to discourse salient referents and provide an experiment on different features contribution to this detection. The first-mention detection could help the quality of coreference resolution systems.
The paper is focused on the study of reaction of italian literature critics on the publication of the Boris Pasternak's novel "Doctor Jivago". The analysys of the book ""Doctor Jivago", Pasternak, 1958, Italy" (published in Russian language in "Reka vremen", 2012, in Moscow) is given. The papers of italian writers, critics and historians of literature, who reacted immediately upon the publication of the novel (A. Moravia, I. Calvino, F.Fortini, C. Cassola, C. Salinari ecc.) are studied and analised.
In the article the patterns of the realization of emotional utterances in dialogic and monologic speech are described. The author pays special attention to the characteristic features of the speech of a speaker feeling psychic tension and to the compositional-pragmatic peculiarities of dialogic and monologic text.