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Word Embeddings for Information Extraction from Tweets
2016
Forum for Information Retrieval Evaluation
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Our method uses vector space word embeddings to extract information from microblogs (tweets) related to disaster scenarios, and can be replicated across various domains. ...
This paper describes our approach on "Information Extraction from Microblogs Posted during Disasters" as an attempt in the shared task of the Microblog Track at Forum for Information Retrieval Evaluation ...
To extract the location information, we used the geo-location attribute from the tweets and the Stanford NER tagger 6 to extract location names from the tweet text. ...
dblp:conf/fire/DasguptaKDNB16
fatcat:ooojppcgk5f55l4zzwcj65wcly
AMRITA_CEN@FIRE 2016: Code-Mix Entity Extraction for Hindi-English and Tamil-English Tweets
2016
Forum for Information Retrieval Evaluation
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Extraction of such information serves as the basis for the most preliminary task in Natural Language Processing called Entity extraction. ...
The work is submitted as a part of Shared task on Code Mix Entity Extraction for Indian Languages(CMEE-IL) at Forum for Information Retrieval Evaluation (FIRE) 2016. ...
ACKNOWLEDGMENT We would like to thank organizers of Forum for Information Retrieval Evaluation 2016 for organizing the task. We would also like to thank the organizers of the CMEE-IL task. ...
dblp:conf/fire/GVMP16
fatcat:gccds65b3jc5hfswpbhp4pj7ru
Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media
[chapter]
2017
Lecture Notes in Computer Science
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Automatically identifying the category of information (e.g., reports on affected individuals, donations and volunteers) contained in these posts is vital for their efficient handling and consumption by ...
In this paper, we introduce Sem-CNN; a wide and deep Convolutional Neural Network (CNN) model designed for identifying the category of information contained in crisis-related social media content. ...
embeddings and semantic embeddings initialised from extracted concepts. ...
doi:10.1007/978-3-319-68288-4_9
fatcat:ntetx4pzizgolpp6sskimipjve
CEN@Amrita FIRE 2016: Context based Character Embeddings for Entity Extraction in Code-Mixed Text
2016
Forum for Information Retrieval Evaluation
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The tweets in code mix are written in English mixed with Hindi or Tamil. In this work, Entity Extraction system is implemented for both Hindi-English and Tamil-English code-mix tweets. ...
These words were further split into characters. Embedding vectors of these characters are appended with the I-O-B tags and used for training the system. ...
ACKNOWLEDGMENT We would like to give thanks to the task organizer -Forum for Information Retrieval Evaluation. We also thank organizers of CMEE-IL task. ...
dblp:conf/fire/VSGVMP16
fatcat:gdjynp5l5bf3hbl7jrtm2bsua4
Detecting Arabic Offensive Language in Microblogs Using Domain-Specific Word Embeddings and Deep Learning
2022
Tehnički glasnik
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This paper proposes a deep learning approach that utilizes the bidirectional long short-term memory (BiLSTM) model and domain-specific word embeddings extracted from an Arabic offensive dataset. ...
The results showed the highest performance accuracy of 0.93% with the BiLSTM model trained using a combination of domain-specific and agnostic-domain word embeddings. ...
word embeddings extracted from an Arabic offensive corpus. ...
doaj:a64f4b001e0246babbfb91c74079767f
fatcat:yszynytvuneb7pez4yts7p4g4y
Keyphrase Extraction from Disaster-related Tweets
2019
The World Wide Web Conference on - WWW '19
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2019 pre-print arXiv:1910.07897v1 fatcat:bnmwge5qonfd7ffh5dgwubyire
While keyphrase extraction has received considerable attention in recent years, relatively few studies exist on extracting keyphrases from social media platforms such as Twitter, and even fewer for extracting ...
Previously, joint training of two different layers of a stacked Recurrent Neural Network for keyword discovery and keyphrase extraction had been shown to be effective in extracting keyphrases from general ...
We also thank NSF for support from the grants IIS-1526542, IIS-1423337, IIS-1652674, and CMMI-1541155. ...
doi:10.1145/3308558.3313696
dblp:conf/www/ChowdhuryCC19
fatcat:fgir6ach3bcbhkz7lyvuj7hvxa
On Identifying Hashtags in Disaster Twitter Data
[article]
2020
arXiv
pre-print
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Tweet hashtags have the potential to improve the search for information during disaster events. ...
To facilitate progress on automatic identification (or extraction) of disaster hashtags for Twitter data, we construct a unique dataset of disaster-related tweets annotated with hashtags useful for filtering ...
We also thank NSF for support from the grants IIS-1526542, IIS-1423337, IIS-1652674, and CMMI-1541155. ...
arXiv:2001.01323v1
fatcat:h5erzrtwpbdxno52yijjuy6gkq
SIEVE: Helping developers sift wheat from chaff via cross-platform analysis
2019
Empirical Software Engineering
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Our approach is based on transfer representation learning and word embeddings, leveraging information extracted from a source platform which contains rich domain-related content. ...
We first build a word embeddings model as a representation learned from the source platform, and use the model to improve the performance of knowledge extraction tasks in the target platform. ...
For each sentence or tweet in the dataset, we tokenize it into words. 2. For each word, we look up its weight from the word embeddings model. ...
doi:10.1007/s10664-019-09775-w
fatcat:mss5xk2givcjle4av2qmrok5fu
SIEVE: Helping Developers Sift Wheat from Chaff via Cross-Platform Analysis
[article]
2018
arXiv
pre-print
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Our approach is based on transfer representation learning and word embeddings, leveraging information extracted from a source platform which contains rich domain-related content. ...
We first build a word embeddings model as a representation learned from the source platform, and use the model to improve the performance of knowledge extraction tasks in the target platform. ...
For each sentence or tweet in the dataset, we tokenize it into words. 2. For each word, we look up its weight from the word embeddings model. ...
arXiv:1810.13144v1
fatcat:nvkrpmtq4zd4zhwn57nccctr74
ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification
2017
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
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We investigated several traditional Natural Language Processing (NLP) features, domain specific features and word embedding features together with supervised machine learning methods to address this task ...
Software for Internet of Things (ZF1213) and NSFC (61402175). ...
Acknowledgements This research is supported by grants from Science and Technology Commission of Shanghai Municipality (14DZ2260800 and 15ZR1410700), Shanghai Collaborative Innovation Center of Trustworthy ...
doi:10.18653/v1/s17-2137
dblp:conf/semeval/ZhouLW17
fatcat:6dgemujijrhxlkiwilsvvph3kq
On Identifying Hashtags in Disaster Twitter Data
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Tweet hashtags have the potential to improve the search for information during disaster events. ...
To facilitate progress on automatic identification (or extraction) of disaster hashtags for Twitter data, we construct a unique dataset of disaster-related tweets annotated with hashtags useful for filtering ...
We also thank NSF for support from the grants IIS-1526542, IIS-1423337, IIS-1652674, and CMMI-1541155. ...
doi:10.1609/aaai.v34i01.5387
fatcat:y7ntuyaw5naepd2y6c5dh4ly5a
Predicting Information Diffusion on Twitter a Deep Learning Neural Network Model Using Custom Weighted Word Features
[chapter]
2020
Lecture Notes in Computer Science
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Our framework first extracts the words, create a matrix of these words using the sequences in the tweet text. ...
The results of the proposed CWWE are compared to a pre-trained glove word embedding. For experimentation, we created a corpus of size 230,000 tweets posted by more than 45,000 users in 6 months. ...
Once every tweet loops through the above 3 steps we extract words from all the tweets and create a matrix with all the words from the corpus as columns. ...
doi:10.1007/978-3-030-44999-5_38
fatcat:ftko2naewfhxfdelj36qnz4jjq
Keyphrase Extraction Using Deep Recurrent Neural Networks on Twitter
2016
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
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from tweets. ...
Different from previous studies, which are usually focused on automatically extracting keyphrases from documents or articles, in this study, we considered the problem of automatically extracting keyphrases ...
Acknowledgement The authors wish to thank the anonymous reviewers for their helpful comments. ...
doi:10.18653/v1/d16-1080
dblp:conf/emnlp/ZhangWGH16
fatcat:na2oqckrtndg5ka3aj5jqif4na
Tracing State-Level Obesity Prevalence from Sentence Embeddings of Tweets: A Feasibility Study
[article]
2019
arXiv
pre-print
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In response, we introduce a deep learning approach that uses hashtags as a form of supervision and learns tweet embeddings for extracting informative textual features. ...
Previous public health studies based on Twitter data have largely relied on keyword-matching or topic models for clustering relevant tweets. ...
by extracting features from word and tweet embeddings Model dim MAE Pearson Corr. ...
arXiv:1911.11324v2
fatcat:o4ubgnizd5av5h6kg3phakezyy
Surface and Deep Features Ensemble for Sentiment Analysis of Arabic Tweets
2019
IEEE Access
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Previous research on the SA of tweets mainly focused on manually extracting features from the text. ...
In this paper, we propose to learn sentiment-specific word embeddings from Arabic tweets and use them in the Arabic Twitter sentiment classification. ...
We use these three models to extract sentimentspecific word embeddings from Arabic tweets, as described in Section III. ...
doi:10.1109/access.2019.2924314
fatcat:ceyngnnfkvcbdh74urcmnw4qvm
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