Titlebook: Computational Linguistics and Intelligent Text Processing; 14th International C Alexander Gelbukh Conference proceedings 2013 Springer-Verl

審問

Chinese Emotion Lexicon Developing via Multi-lingual Lexical Resources Integrationurce developed on WordNet. The approach consists of three steps, namely translation, filtering and extension. Initially, all English words in WordNet-Affect synsets are translated into Chinese words. Thereafter, with the help of Chinese synonyms dictionary (Tongyici Cilin), we build a bilingual undi

子女

N-Gram-Based Recognition of Threatening Tweetshe single tweet (without further context) and using only very simple recognition features, namely n-grams. We present two different methods of n-gram-based recognition, one based on manually constructed n-gram patterns and the other on machine learned patterns. Our evaluation is not restricted to pr

Microgram

Distinguishing the Popularity between Topics: A System for Up-to-Date Opinion Retrieval and Mining imajority of the comparative studies in this field focus on analyzing fixed (offline) collections from certain domains, genres, or topics. In this paper, we present an online system for opinion mining and retrieval that is able to discover up-to-date web pages on given topics using focused crawling a

變化無常

sphincter

Domain Adaptation in Statistical Machine Translation Using Comparable Corpora: Case Study for Englisplied not only to widely used language pairs, but also to under-resourced languages. However, under-resourced languages and narrow domains face the problem of insufficient parallel data for building SMT systems of reasonable quality for practical applications. In this paper we show how broad domain

野蠻

Damping Sentiment Analysis in Online Communication: Discussions, Monologs and Dialogsxts assigned significantly different sentiment strength to the average of previous texts – to see whether their classification can be improved. The results suggest that a damping procedure to reduce sudden large changes in sentiment can improve classification accuracy but that the optimal procedure will depend on the type of texts processed.

菊花

Predicting Subjectivity Orientation of Online Forum Threads improve information search in online forums. In this paper, we study methods to analyze subjectivity of online forum threads. We build binary classifiers on textual features extracted from thread content to classify threads as subjective or non-subjective. We demonstrate the effectiveness of our methods on two popular online forums.

N斯巴達(dá)人

No Free Lunch in Factored Phrase-Based Machine Translatione, guiding the search is difficult due to small differences between systems, which are further blurred by randomness in tuning. We describe a heuristic for estimating the complexity of factored models. Finally, we discuss the possibilities of a “semi-automatic” exploration of the space in several directions and evaluate the obtained systems.

Nucleate

Figate