@Book{RANLPStud:2017,
  editor    = {Venelin  and  Irina  and  Pepa  and  Yasen Kiprov  and  Ivelina Nikolova},
  title     = {Proceedings of the Student Research Workshop Associated with RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna},
  publisher = {INCOMA Ltd.},
  url       = {https://doi.org/10.26615/issn.1314-9156.2017_}
}

@InProceedings{clairet:2017:RANLPStud,
  author    = {Clairet, Nadia},
  title     = {Dish Classification using Knowledge based Dietary Conflict Detection},
  booktitle = {Proceedings of the Student Research Workshop Associated with RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna},
  publisher = {INCOMA Ltd.},
  pages     = {1--9},
  abstract  = {The present paper considers the problem of dietary conflict detection from dish
	titles. The proposed method explores the semantics associated with the dish
	title in order to discover a certain or possible incompatibility of a
	particular dish with a particular diet. Dish titles are parts of the elusive
	and metaphoric gastronomy language, their processing can be viewed as a
	combination of short text and domain-specific texts analysis. We build our
	algorithm on the basis of a common knowledge lexical semantic network and show
	how such network can be used for domain specific short text processing.},
  url       = {https://doi.org/10.26615/issn.1314-9156.2017_001}
}

@InProceedings{daudert:2017:RANLPStud,
  author    = {Daudert, Tobias},
  title     = {Analysing Market Sentiments: Utilising Deep Learning to Exploit Relationships within the Economy},
  booktitle = {Proceedings of the Student Research Workshop Associated with RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna},
  publisher = {INCOMA Ltd.},
  pages     = {10--16},
  abstract  = {In today's world, globalisation is not only affecting inter-culturalism but
	also linking markets across the globe. Given that all markets are affecting
	each other and are not only driven by fundamental data but also by sentiments,
	sentiment analysis regarding the markets becomes a tool to predict, anticipate,
	and milden future economic crises such as the one we faced in 2008. In this
	paper, an approach to improve sentiment analysis by exploiting relationships
	among different kinds of sentiment, together with supplementary information,
	from and across various data sources is proposed.},
  url       = {https://doi.org/10.26615/issn.1314-9156.2017_002}
}

@InProceedings{jwalapuram:2017:RANLPStud,
  author    = {Jwalapuram, Prathyusha},
  title     = {Evaluating Dialogs based on Grice's Maxims},
  booktitle = {Proceedings of the Student Research Workshop Associated with RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna},
  publisher = {INCOMA Ltd.},
  pages     = {17--24},
  abstract  = {There is no agreed upon standard for the evaluation of conversational dialog
	systems, which are well-known to be hard to evaluate due to the difficulty in
	pinning down metrics that will correspond to human judgements and the
	subjective nature of human judgment itself. We explored the possibility of
	using Grice's Maxims to evaluate effective communication in conversation. We
	collected some system generated dialogs from popular conversational chatbots
	across the spectrum and conducted a survey to see how the human judgements
	based on Gricean maxims correlate, and if such human judgments can be used as
	an effective evaluation metric for conversational dialog.},
  url       = {https://doi.org/10.26615/issn.1314-9156.2017_003}
}

@InProceedings{popov:2017:RANLPStud,
  author    = {Popov, Alexander},
  title     = {Word Sense Disambiguation with Recurrent Neural Networks},
  booktitle = {Proceedings of the Student Research Workshop Associated with RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna},
  publisher = {INCOMA Ltd.},
  pages     = {25--34},
  abstract  = {This paper presents a neural network architecture for word sense disambiguation
	(WSD). The architecture employs recurrent neural layers and more specifically
	LSTM cells, in order to capture information about word order and to easily
	incorporate distributed word representations (embeddings) as features, without
	having to use a fixed window of text. The paper demonstrates that the
	architecture is able to compete with the most successful supervised systems for
	WSD and that there is an abundance of possible improvements to take it to the
	current state of the art. In addition, it explores briefly the potential of
	combining different types of embeddings as input features; it also discusses
	possible ways for generating "artificial corpora" from knowledge bases -- for
	the purpose of producing training data and in relation to possible applications
	of embedding lemmas and word senses in the same space.},
  url       = {https://doi.org/10.26615/issn.1314-9156.2017_004}
}

@InProceedings{rohanian:2017:RANLPStud,
  author    = {Rohanian, Morteza},
  title     = {Multi-Document Summarization of Persian Text using Paragraph Vectors},
  booktitle = {Proceedings of the Student Research Workshop Associated with RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna},
  publisher = {INCOMA Ltd.},
  pages     = {35--40},
  abstract  = {A multi-document summarizer finds the key topics from multiple textual sources
	and organizes information around them. In this paper we propose a summarization
	method for Persian text using paragraph vectors that can represent textual
	units of arbitrary lengths. We use these vectors to calculate the semantic
	relatedness between documents, cluster them to a number of predetermined
	groups, weight them based on their distance to the centroids and the
	intra-cluster homogeneity and take out the key paragraphs. We compare the final
	summaries with the gold-standard summaries of 21 digital topics using the ROUGE
	evaluation metric. Experimental results show the advantages of using paragraph
	vectors over earlier attempts at developing similar methods for a low resource
	language like Persian.},
  url       = {https://doi.org/10.26615/issn.1314-9156.2017_005}
}

@InProceedings{simeonova:2017:RANLPStud,
  author    = {Simeonova, Lilia},
  title     = {Gradient Emotional Analysis},
  booktitle = {Proceedings of the Student Research Workshop Associated with RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna},
  publisher = {INCOMA Ltd.},
  pages     = {41--45},
  abstract  = {Over the past few years a lot of research has been done on sentiment analysis,
	however, the emotional analysis, being so subjective, is not a well examined
	dis-cipline. The main focus of this proposal is to categorize a given sentence
	in two dimensions - sentiment and arousal. For this purpose two techniques will
	be com-bined -- Machine Learning approach and Lexicon-based approach. The
	first di-mension will give the sentiment value -- positive versus negative.
	This will be re-solved by using Na\"{i}ve Bayes Classifier. The second and more
	interesting dimen-sion will determine the level of arousal. This will be
	achieved by evaluation of given a phrase or sentence based on lexi-con with
	affective ratings for 14 thousand English words.},
  url       = {https://doi.org/10.26615/issn.1314-9156.2017_006}
}

@InProceedings{tran:2017:RANLPStud,
  author    = {Tran, Anh Hang Nga},
  title     = {Applying Deep Neural Network to Retrieve Relevant Civil Law Articles},
  booktitle = {Proceedings of the Student Research Workshop Associated with RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna},
  publisher = {INCOMA Ltd.},
  pages     = {46--48},
  abstract  = {The paper aims to achieve the legal question answering information
	retrieval (IR) task at Competition on Legal Information
	Extraction/Entailment (COLIEE) 2017. Our proposal methodology for the
	task is to utilize deep neural network, natural language processing and
	word2vec. The system was evaluated using training and testing data from
	the competition on legal information extraction/entailment (COLIEE).
	Our system mainly focuses on giving relevant civil law articles for given
	bar exams. The corpus of legal questions is drawn from Japanese Legal
	Bar exam queries. We implemented a combined deep neural network with
	additional features NLP and word2vec to gain the corresponding civil law
	articles based on a given bar exam 'Yes/No' questions. This paper focuses
	on clustering words-with- relation in order to acquire relevant civil law
	articles. All evaluation processes were done on the COLIEE 2017 training
	and test data set. The experimental result shows a very promising result.},
  url       = {https://doi.org/10.26615/issn.1314-9156.2017_007}
}