The logic and linguistic model for automatic extraction of collocation similarity

The article discusses the process of automatic identification of collocation similarity. The semantic analysis is one of the most advanced as well as the most difficult NLP task. The main problem of semantic processing is the determination of polysemy and synonymy of linguistic units. In addition, the task becomes complicated in case of word collocations. The paper suggests a logical and linguistic model for automatic determining semantic similarity between colocations in Ukraine and English languages. The proposed model formalizes semantic equivalence of collocations by means of semantic and grammatical characteristics of collocates. The basic idea of this approach is that morphological, syntactic and semantic characteristics of lexical units are to be taken into account for the identification of collocation similarity. Basic mathematical means of our model are logical-algebraic equations of the finite predicates algebra. Verb-noun and noun-adjective collocations in Ukrainian and English languages consist of words belonged to main parts of speech. These collocations are examined in the model. The model allows extracting semantically equivalent collocations from semi-structured and non-structured texts. Implementations of the model will allow to automatically recognize semantically equivalent collocations. Usage of the model allows increasing the effectiveness of natural language processing tasks such as information extraction, ontology generation, sentiment analysis and some others

Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture

The World Wide Web holds a wealth of information in the form of unstructured texts such as customer reviews for products, events and more. By extracting and analyzing the expressed opinions in customer reviews in a fine-grained way, valuable opportunities and insights for customers and businesses can be gained. We propose a neural network based system to address the task of Aspect-Based Sentiment Analysis to compete in Task 2 of the ESWC-2016 Challenge on Semantic Sentiment Analysis. Our proposed architecture divides the task in two subtasks: aspect term extraction and aspect-specific sentiment extraction. This approach is flexible in that it allows to address each subtask independently. As a first step, a recurrent neural network is used to extract aspects from a text by framing the problem as a sequence labeling task. In a second step, a recurrent network processes each extracted aspect with respect to its context and predicts a sentiment label. The system uses pretrained semantic word embedding features which we experimentally enhance with semantic knowledge extracted from WordNet. Further features extracted from SenticNet prove to be beneficial for the extraction of sentiment labels. As the best performing system in its category, our proposed system proves to be an effective approach for the Aspect-Based Sentiment Analysis