![]() We have implemented two al- gorithms of heuristic nature that follow the approach. This paper presents an approach for Multilingual News Doc- ument Clustering in comparable corpora. ![]() ![]() In the NI type, the Num-CL sequence follows a caseless N, whereas in the NC type both the head noun and the following Num-CL are case-marked. Noun-Case (NC) Type: haksayng-i sey myeng-i o-ass-ta student-NOM three CL-NOM come-PST-DECL In the GC type, the Num-CL appears with the genitive case marking, preceding the modifying NP. Noun Initial (NI) Type: haksayng sey myeng(-i) o-ass-ta student three CL-NOM come-PST-DECL c. Genitive-Case (GC) Type: sey myeng-uy haksayng-i o-ass-ta three CL-GEN student-NOM come-PST-DECL ‘Three students came.’ b. 3 There exist at least three different environments where the numeral-classifier (Num-CL) expression can appear: 4 (1) a. 1 Basic Data and Issues One of the most salient features of languages like Korean is the complex behavior of numeral classifiers. We provide a constraint-based analysis of these constructions within the framework of HPSG with the semantic representations of MRS (Minimal Recursion Semantics) and reports its implementation in the LKB (Linguistic Knowledge Building) system. The syntactic and semantic complexity of the so-called numeral classifier (Num-Cl) constructions in Korean challenges theoretical as well as computational linguists. As for naive Bayes, it also achieved good performance. KNN, this algorithm continues to achieve very good results and scales up well with the number of documents, which is not theĬase for SVM. Result is that SVM was not a clear winner, despite quite good overall performance. Our results show all the classifiers achieved comparable performance on most problems. An important issue is to compare optimized versions of these algorithms, We have decided to investigate this issue and compared SVM to kNN and naive Bayes on binary classification tasks. So should we just not bother about other classification algorithms and opt always for SVM? Interest towards the Support Vector Machine, various studies showed that the SVM outperforms other classification algorithms. Space transformation whereas some others compared the performance of different algorithms. In fact, some studies compared feature selection techniques or feature Within the context of the analytical framework.ĭocument classification has already been widely studied. The paper then describesĮxamples of existing models for two core affective processes, cognitive appraisal and emotion-induced effects on cognition, Theįramework provides a basis for identifying the functional and architectural requirements on one hand, and alternative modelingĪpproaches on the other, thereby laying the groundwork for a set of model development guidelines. The paper first identifies key model characteristics that define an analytical framework. ![]() This paper discusses the motivation and alternatives for incorporatingĮmotions within user models. Emotions can also provide disambiguating information for speech recognition and natural language understanding,Īnd enhance the effectiveness of dialogue systems. The integration of emotions within user models would therefore enhance their Neuroscience and psychology research has demonstrated a close connection between cognition and affect, and a number of emotion-inducedĮffects on perception, cognition, and behavior. Their talks provided insight into important issues, applications and techniques related to the conference topics. We are also grateful to the invited speakers for their contribution. We would like to thank all the authors for the efforts they put into their submissions and the members of the Program Committee and reviewers who did a wonderful job helping us to select the most appropriate papers. ![]() The number of the submissions this year was the highest so far. Following the review process, 87 papers were accepted out of 175 submitted, an acceptance rate of 49.7%. This volume containsthe proceedings of the Ninth TSD Conference, held in Brno, Czech Republic in September 2006. Indeed, one of its goals has always been to bring together NLP researchers with different interests from different parts of the world and to promote their mutual cooperation. The conference attracts researchers not only from Central and Eastern Europe but also from other parts of the world. It has become an interdisciplinary forum, interweaving the themes of speech technology and language processing. TSD constitutes a recognized forum for the presentation and discussion of state-of-the-art technology and recent achievements in the ?eld of natural language processing. During this time almost 400 authors from 36 countries have contributed to the proceedings. The annual Text, Speech and Dialogue Conference (TSD), which originated in 1998, is now coming to the end of its ?rst decade. ![]()
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