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检索条件"任意字段=2016 Workshop on Interactions between Data Mining and Natural Language Processing, DMNLP 2016"

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NLP4REC: The WSDM 2020 workshop on natural language processing for Recommendations 13

NLP4REC: The WSDM 2020 Workshop on Natural Language Processi...

引用

13th Annual ACM International Conference on Web Search and data mining (WSDM)

作者： Ren, Pengjie Ren, Zhaochun Sun, Fei He, Xiangnan Yin, Dawei de Rijke, Maarten Univ Amsterdam Amsterdam Netherlands Shandong Univ Jinan Peoples R China Alibaba Grp Hangzhou Peoples R China Univ Sci & Technol China Hefei Peoples R China JDcom Beijing Peoples R China

ISBN: (纸本)9781450368223

natural language processing is becoming more and more important in recommender systems. This half day workshop explores challenges and potential research directions in Recommender Systems (RSs) combining natural language processing (NLP). The focus will be on stimulating discussions around how to combine natural language processing technologies with recommendation. We welcome theoretical, experimental, and methodological studies that leverage NLP technologies to advance recommender systems, as well as emphasize the applicability in practical applications. The workshop aims to bring together a diverse set of researchers and practitioners interested in investigating the interaction between NLP and RSs to develop more intelligent RSs.

关键词： Recommender systems

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Gene-Disease-Food Relation Extraction from Biomedical database 7th

Gene-Disease-Food Relation Extraction from Biomedical Databa...

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7th International workshop on Soft Computing Applications (SOFA)

作者： Karaa, Wahiba Ben Abdessalem Mannai, Monia Dey, Nilanjan Ashour, Amira S. Olariu, Iustin Taif Univ Coll Comp & IT At Taif Saudi Arabia ENSI RIADI GDL Lab Tunis Tunisia High Inst Management Tunis Tunisia Techno India Coll Technol Dept Informat Technol Kolkata India Tanta Univ Dept Elect & Elect Commun Engn Fac Engn Tanta Egypt Vasile Goldis Western Univ Arad Fac Dent Arad Romania

ISBN: (纸本)9783319625218

Through the past years, an incredible increase in the biomedical data amount presented on the web is enlarged due to the increased data volume in the medical and biological domains. Hence, the search for documents and information on the internet became increasingly complicated. In the current work, a new approach for information extraction using the natural language processing (NLP) tools and ontology was proposed. It described a system to extract relations between the concepts from biomedical texts using morphological analysis and information extraction techniques. In the first step, the system segmented the input text into sentences. Each sentence is then segmented into words that were tagged with part-of-speech labels and concept classes (food, drug, and gene). A set of relation extraction rules (regular expression patterns) are applied on the annotated sentences. If a pattern matches, the concepts and relations are extracted. The system has been tested on a set of 700 MEDLINE abstracts. For performance evaluation, the precision, recall and F-score were calculated. The proposed approach created by information retrieval from MEDLINE to gather a set of abstracts related to a given domain. Then, these texts were annotated using an automaton and ontology via recognizing interesting concepts for morphological analysis. After the annotation step, some rules were summarizing in an automaton that help gene-disease-food relationships discovery. This work proposed an approach for identifying relations between medical concepts using NLP tools. An evaluation experiment reported good effectiveness results.

关键词： Biomedical information Semantic reasoning techniques Information extraction MEDLINE natural language processing (NLP) Text mining

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Topic sentiment joint model with word embeddings

Topic sentiment joint model with word embeddings

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2016 workshop on interactions between data mining and natural language processing, dmnlp 2016

作者： Fu, Xianghua Wu, Haiying Cui, Laizhong College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong518060 China

Topic sentiment joint model is an extended model which aims to deal with the problem of detecting sentiments and topics simultaneously from online reviews. Most of existing topic sentiment joint modeling algorithms infer resulting distributions from the co-occurrence of words. But when the training corpus is short and small, the resulting distributions might be not very satisfying. In this paper, we propose a novel topic sentiment joint model with word embeddings (TSWE), which introduces word embeddings trained on external large corpus. Furthermore, we implement TSWE with Gibbs sampling algorithms. The experiment results on Chinese and English data sets show that TSWE achieves significant performance in the task of detecting sentiments and topics simultaneously. Copyright ©by the paper's authors.

关键词： natural language processing systems

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CEUR workshop Proceedings

CEUR Workshop Proceedings

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2016 workshop on interactions between data mining and natural language processing, dmnlp 2016

The proceedings contain 6 papers. The topics discussed include: using machine learning methods and linguistic features in single-document extractive summarization;prediction of happy endings in German novels;a study of synthetic oversampling for Twitter imbalanced sentiment analysis;shallow text clustering does not mean weak topics: how topic identification can leverage bigram features;topic models with sparse and group-sparsity inducing priors;and topic sentiment joint model with word embeddings.

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Text mining of Related Events from natural Science Literature 2nd

Text Mining of Related Events from Natural Science Literatur...

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2nd International workshop on Semantics, Analytics, Visualization - Enhancing Scholarly data (SAVE-SD)

作者： Marsi, Erwin Ozturk, Pinar Norwegian Univ Sci & Technol Dept Comp & Informat Sci Trondheim Norway

ISBN: (纸本)9783319536378;9783319536361

We present an approach to text mining in areas where the entities of interest can not be defined in advance. Our system is aimed at finding related events in natural science literature, in particular, changing/increasing/decreasing variables in Marine science publications. It enables semantic search for events by abstracting from morphological, lexical-semantic and syntactic variations. In addition, generalisation of variables through syntactic pruning helps finding similar variables. Relations between events are induced from co-occurrence frequencies. Extracted information is stored in a property graph database and accessed using the Cypher query language. A user interface presents events as a graph to visualise their type, frequency and relation strength, in combination with their textual sources.

关键词： Text mining natural language processing Information extraction Visualisation Knowledge discovery

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Sentiment extraction from financial public disclosure documents 1

Sentiment extraction from financial public disclosure docume...

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1st workshop on mining data for Financial ApplicationS, MIDAS 2016

作者： Türkmen, Ali Caner Boǧaziçi University Department of Computer Engineering BebekIstanbul34342 Turkey

We address the problem of extracting sentiment in financial public disclosure documents, and explore their effects on daily price movements. We take a collection of public disclosure forms submitted by four companies in the Turkish stock market. Using simple classification algorithms, we point to a significant correlation between the content of disclosure texts and the next day's price direction. We discuss the relationship between learned term weights and sentiment by comparing to a translation of a well-known financial sentiment lexicon. © 2016, CEUR-WS. All rights reserved.

关键词： natural language processing systems

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language independent Big-data system for the prediction of user location on Twitter 4

Language independent Big-Data system for the prediction of u...

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4th IEEE International Conference on Big data (Big data)

作者： Alonso-Lorenzo, Jaime Costa-Montenegro, Enrique Fernandez-Gavilanes, Milagros Univ Vigo Telemat Engn Dept Vigo Spain

ISBN: (纸本)9781467390057

Social media interactions have become increasingly important in today's world. A survey conducted in 2014 among adult Americans found that a majority of those surveyed use at least one social media site. Twitter, in particular, serves 310 million active users on a monthly basis, and thousands of tweets are published every second. The public nature of this data makes it a prime candidate for data mining. Twitter users publish 140-character long messages and have the ability to geo-tag these tweets using a variety of methods: GPS coordinates, IP geolocation and user-declared location. However, few users disclose their location, only between 1% and 3% of users provide location data, according to our empirical findings. In this article, we aim to aggregate information from different sources to provide an estimation on the location of any Twitter user. We use an hybrid approach, using techniques in the fields of natural language processing and network theory. Tests have been conducted on two datasets, inferring the location of each individual user and then comparing it against the actual known location of users with geolocation information. The estimation error is the distance in kilometers between the estimation and the actual location. Furthermore, there is a comparison of the relative average error per country, to account for difference in country sizes. Our results improve those presented in different researches in the literature. Our research has as feature to be independent of the language used by the user, while most of works in the literature use just one language or a reduced set of languages. The article also showcases the evolution of our estimation approach and the impact that the modifications had on the results.

关键词： Big-data Social networks Twitter User location natural language processing Network theory

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Application of a hybrid relation extraction framework for intelligent natural language processing

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International Symposium on Intelligent Systems Technologies and Applications, ISTA 2016

作者： Goel, Lavika Khandelwal, Rashi Retamino, Eloy Nair, Suraj Knoll, Alois Birla Institute of Technology and Science Pilani India TUM CREATE Singapore Germany

ISBN: (纸本)9783319479514

When an intelligent system needs to carry out a task, it needs to understand the instructions given by the user. But natural language instructions are unstructured and cannot be resolved by a machine without processing. Hence natural language processing (NLP) needs to be done by extracting relations between the words in the input sentences. As a result of this, the input gets structured in the form of relations which are then stored in the system’s knowledge base. In this domain, majorly two kinds of extraction techniques have been discovered and exploited - rule based and machine learning based. These approaches have been separately used for text classification, data mining, etc. However progress still needs to be made in the field of information extraction from human instructions. The work done here, takes both the approaches, combines them to form a hybrid algorithm and applies this to the domain of human robot interactions. The approach first uses rules and patterns to extract candidate relations. It then uses a machine learning classifier called Support Vector Machine (SVM) to learn and identify the correct relations. The algorithm is then validated against a standard text corpus taken from the RoCKIn transcriptions and the accuracy achieved is shown to be around 91%. © Springer International Publishing AG 2016.

关键词： Intelligent systems

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Sense-making for intelligence analysis on social media data 4

Sense-making for intelligence analysis on social media data

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Conference on Next-Generation Analyst IV

作者： Pritzkau, Albert Fraunhofer Inst Commun Informat Proc & Ergon FKIE Dept Informat Technol Command & Control ITF Fraunhoferstr 20 D-53343 Wachtberg Germany

ISBN: (纸本)9781510600928

Social networks, in particular online social networks as a subset, enable the analysis of social relationships which are represented by interaction, collaboration, or other sorts of influence between people. Any set of people and their internal social relationships can be modelled as a general social graph. These relationships are formed by exchanging emails, making phone calls, or carrying out a range of other activities that build up the network. This paper presents an overview of current approaches to utilizing social media as a ubiquitous sensor network in the context of national and global security. Exploitation of social media is usually an interdisciplinary endeavour, in which the relevant technologies and methods are identified and linked in order ultimately demonstrate selected applications. Effective and efficient intelligence is usually accomplished in a combined human and computer effort. Indeed, the intelligence process heavily depends on combining a human's flexibility, creativity, and cognitive ability with the bandwidth and processing power of today's computers. To improve the usability and accuracy of the intelligence analysis we will have to rely on data-processing tools at the level of natural language. Especially the collection and transformation of unstructured data into actionable, structured data requires scalable computational algorithms ranging from Artificial Intelligence, via Machine Learning, to natural language processing (NLP). To support intelligence analysis on social media data, social media analytics is concerned with developing and evaluating computational tools and frameworks to collect, monitor, analyze, summarize, and visualize social media data. Analytics methods are employed to extract of significant patterns that might not be obvious. As a result, different data representations rendering distinct aspects of content and interactions serve as a means to adapt the focus of the intelligence analysis to specific information reques

关键词： intelligence analysis social media mining feature engineering

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Some thoughts on using annotated suffix trees for natural language processing 2

Some thoughts on using annotated suffix trees for Natural La...

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2nd workshop on interactions between data mining and natural language processing, dmnlp 2015

作者： Chernyak, Ekaterina National Research University Higher School of Economics Moscow Russia

The paper defines an annotated suffix tree (AST) - a data structure used to calculate and store the frequencies of all the fragments of the given string or a collection of strings. The AST is associated with a string to text scoring, which takes all fuzzy matches into account. We show how the AST and the AST scoring can be used for natural language processing tasks. Copyright © by the paper's authors. Copying only for private and academic purposes.

关键词： Forestry

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