Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 8

Search results for: N-gram

8 Lexical Based Method for Opinion Detection on Tripadvisor Collection

Authors: Faiza Belbachir, Thibault Schienhinski

Abstract:

The massive development of online social networks allows users to post and share their opinions on various topics. With this huge volume of opinion, it is interesting to extract and interpret these information for different domains, e.g., product and service benchmarking, politic, system of recommendation. This is why opinion detection is one of the most important research tasks. It consists on differentiating between opinion data and factual data. The difficulty of this task is to determine an approach which returns opinionated document. Generally, there are two approaches used for opinion detection i.e. Lexical based approaches and Machine Learning based approaches. In Lexical based approaches, a dictionary of sentimental words is used, words are associated with weights. The opinion score of document is derived by the occurrence of words from this dictionary. In Machine learning approaches, usually a classifier is trained using a set of annotated document containing sentiment, and features such as n-grams of words, part-of-speech tags, and logical forms. Majority of these works are based on documents text to determine opinion score but dont take into account if these texts are really correct. Thus, it is interesting to exploit other information to improve opinion detection. In our work, we will develop a new way to consider the opinion score. We introduce the notion of trust score. We determine opinionated documents but also if these opinions are really trustable information in relation with topics. For that we use lexical SentiWordNet to calculate opinion and trust scores, we compute different features about users like (numbers of their comments, numbers of their useful comments, Average useful review). After that, we combine opinion score and trust score to obtain a final score. We applied our method to detect trust opinions in TRIPADVISOR collection. Our experimental results report that the combination between opinion score and trust score improves opinion detection.

Keywords: Tripadvisor, Opinion detection, SentiWordNet, trust score.

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7 A Sentence-to-Sentence Relation Network for Recognizing Textual Entailment

Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

Abstract:

Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: Deep neural models, natural language inference, recognizing textual entailment, sentence-to-sentence relation.

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6 Concepts Extraction from Discharge Notes using Association Rule Mining

Authors: Basak Oguz Yolcular

Abstract:

A large amount of valuable information is available in plain text clinical reports. New techniques and technologies are applied to extract information from these reports. In this study, we developed a domain based software system to transform 600 Otorhinolaryngology discharge notes to a structured form for extracting clinical data from the discharge notes. In order to decrease the system process time discharge notes were transformed into a data table after preprocessing. Several word lists were constituted to identify common section in the discharge notes, including patient history, age, problems, and diagnosis etc. N-gram method was used for discovering terms co-Occurrences within each section. Using this method a dataset of concept candidates has been generated for the validation step, and then Predictive Apriori algorithm for Association Rule Mining (ARM) was applied to validate candidate concepts.

Keywords: association rule mining, otorhinolaryngology, predictive apriori, text mining

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5 Fast Database Indexing for Large Protein Sequence Collections Using Parallel N-Gram Transformation Algorithm

Authors: Jehad A. H. Hammad, Nur'Aini binti Abdul Rashid

Abstract:

With the rapid development in the field of life sciences and the flooding of genomic information, the need for faster and scalable searching methods has become urgent. One of the approaches that were investigated is indexing. The indexing methods have been categorized into three categories which are the lengthbased index algorithms, transformation-based algorithms and mixed techniques-based algorithms. In this research, we focused on the transformation based methods. We embedded the N-gram method into the transformation-based method to build an inverted index table. We then applied the parallel methods to speed up the index building time and to reduce the overall retrieval time when querying the genomic database. Our experiments show that the use of N-Gram transformation algorithm is an economical solution; it saves time and space too. The result shows that the size of the index is smaller than the size of the dataset when the size of N-Gram is 5 and 6. The parallel N-Gram transformation algorithm-s results indicate that the uses of parallel programming with large dataset are promising which can be improved further.

Keywords: Biological sequence, Database index, N-gram indexing, Parallel computing, Sequence retrieval.

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4 Advanced Information Extraction with n-gram based LSI

Authors: Ahmet Güven, Ö. Özgür Bozkurt, Oya Kalıpsız

Abstract:

Number of documents being created increases at an increasing pace while most of them being in already known topics and little of them introducing new concepts. This fact has started a new era in information retrieval discipline where the requirements have their own specialties. That is digging into topics and concepts and finding out subtopics or relations between topics. Up to now IR researches were interested in retrieving documents about a general topic or clustering documents under generic subjects. However these conventional approaches can-t go deep into content of documents which makes it difficult for people to reach to right documents they were searching. So we need new ways of mining document sets where the critic point is to know much about the contents of the documents. As a solution we are proposing to enhance LSI, one of the proven IR techniques by supporting its vector space with n-gram forms of words. Positive results we have obtained are shown in two different application area of IR domain; querying a document database, clustering documents in the document database.

Keywords: Document clustering, Information Extraction, Information Retrieval, LSI, n-gram.

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3 Words Reordering based on Statistical Language Model

Authors: Theologos Athanaselis, Stelios Bakamidis, Ioannis Dologlou

Abstract:

There are multiple reasons to expect that detecting the word order errors in a text will be a difficult problem, and detection rates reported in the literature are in fact low. Although grammatical rules constructed by computer linguists improve the performance of grammar checker in word order diagnosis, the repairing task is still very difficult. This paper presents an approach for repairing word order errors in English text by reordering words in a sentence and choosing the version that maximizes the number of trigram hits according to a language model. The novelty of this method concerns the use of an efficient confusion matrix technique for reordering the words. The comparative advantage of this method is that works with a large set of words, and avoids the laborious and costly process of collecting word order errors for creating error patterns.

Keywords: Permutations filtering, Statistical languagemodel N-grams, Word order errors

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2 Automatic Recognition of Emotionally Coloured Speech

Authors: Theologos Athanaselis, Stelios Bakamidis, Ioannis Dologlou

Abstract:

Emotion in speech is an issue that has been attracting the interest of the speech community for many years, both in the context of speech synthesis as well as in automatic speech recognition (ASR). In spite of the remarkable recent progress in Large Vocabulary Recognition (LVR), it is still far behind the ultimate goal of recognising free conversational speech uttered by any speaker in any environment. Current experimental tests prove that using state of the art large vocabulary recognition systems the error rate increases substantially when applied to spontaneous/emotional speech. This paper shows that recognition rate for emotionally coloured speech can be improved by using a language model based on increased representation of emotional utterances.

Keywords: Statistical language model, N-grams, emotionallycoloured speech

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1 N-Grams: A Tool for Repairing Word Order Errors in Ill-formed Texts

Authors: Theologos Athanaselis, Stelios Bakamidis, Ioannis Dologlou, Konstantinos Mamouras

Abstract:

This paper presents an approach for repairing word order errors in English text by reordering words in a sentence and choosing the version that maximizes the number of trigram hits according to a language model. A possible way for reordering the words is to use all the permutations. The problem is that for a sentence with length N words the number of all permutations is N!. The novelty of this method concerns the use of an efficient confusion matrix technique for reordering the words. The confusion matrix technique has been designed in order to reduce the search space among permuted sentences. The limitation of search space is succeeded using the statistical inference of N-grams. The results of this technique are very interesting and prove that the number of permuted sentences can be reduced by 98,16%. For experimental purposes a test set of TOEFL sentences was used and the results show that more than 95% can be repaired using the proposed method.

Keywords: Permutations filtering, Statistical language model N-grams, Word order errors, TOEFL

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