pwc-archive/papers-with-abstracts huggingface dataset:
Named Entity Recognition and Hashtag Decomposition to Improve the Classification of Tweets
Authors: Billal Belainine, Alexs Fonseca, ro, Fatiha Sadat
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Classification, General Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

In social networks services like Twitter, users are overwhelmed with huge amount of social data, most of which are short, unstructured and highly noisy. Identifying accurate information from this huge amount of data is indeed a hard task. Classification of tweets into organized form will help the user to easily access these required information. Our first contribution relates to filtering parts of speech and preprocessing this kind of highly noisy and short data. Our second contribution concerns the named entity recognition (NER) in tweets. Thus, the adaptation of existing language tools for natural languages, noisy and not accurate language tweets, is necessary. Our third contribution involves segmentation of hashtags and a semantic enrichment using a combination of relations from WordNet, which helps the performance of our classification system, including disambiguation of named entities, abbreviations and acronyms. Graph theory is used to cluster the words extracted from WordNet and tweets, based on the idea of connected components. We test our automatic classification system with four categories: politics, economy, sports and the medical field. We evaluate and compare several automatic classification systems using part or all of the items described in our contributions and found that filtering by part of speech and named entity recognition dramatically increase the classification precision to 77.3 {\%}. Moreover, a classification system incorporating segmentation of hashtags and semantic enrichment by two relations from WordNet, synonymy and hyperonymy, increase classification precision up to 83.4 {\%}.
Japanese Text Normalization with Encoder-Decoder Model
Authors: Taishi Ikeda, Hiroyuki Shindo, Yuji Matsumoto
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Data Augmentation, Decoder, Machine Translation, model, Morphological Analysis, Part-Of-Speech Tagging, Text Normalization

Text normalization is the task of transforming lexical variants to their canonical forms. We model the problem of text normalization as a character-level sequence to sequence learning problem and present a neural encoder-decoder model for solving it. To train the encoder-decoder model, many sentences pairs are generally required. However, Japanese non-standard canonical pairs are scarce in the form of parallel corpora. To address this issue, we propose a method of data augmentation to increase data size by converting existing resources into synthesized non-standard forms using handcrafted rules. We conducted an experiment to demonstrate that the synthesized corpus contributes to stably train an encoder-decoder model and improve the performance of Japanese text normalization.
Results of the WNUT16 Named Entity Recognition Shared Task
Authors: Benjamin Strauss, Bethany Toma, Alan Ritter, Marie-Catherine de Marneffe, Wei Xu
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Named Entity Recognition, Named Entity Recognition (NER)

This paper presents the results of the Twitter Named Entity Recognition shared task associated with W-NUT 2016: a named entity tagging task with 10 teams participating. We outline the shared task, annotation process and dataset statistics, and provide a high-level overview of the participating systems for each shared task.
Bidirectional LSTM for Named Entity Recognition in Twitter Messages
Authors: Nut Limsopatham, Nigel Collier
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Feature Engineering, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Segmentation

In this paper, we present our approach for named entity recognition in Twitter messages that we used in our participation in the Named Entity Recognition in Twitter shared task at the COLING 2016 Workshop on Noisy User-generated text (WNUT). The main challenge that we aim to tackle in our participation is the short, noisy and colloquial nature of tweets, which makes named entity recognition in Twitter message a challenging task. In particular, we investigate an approach for dealing with this problem by enabling bidirectional long short-term memory (LSTM) to automatically learn orthographic features without requiring feature engineering. In comparison with other systems participating in the shared task, our system achieved the most effective performance on both the {`}segmentation and categorisation{'} and the {`}segmentation only{'} sub-tasks.
Learning to recognise named entities in tweets by exploiting weakly labelled data
Authors: Kurt Junshean Espinosa, Riza Theresa Batista-Navarro, Sophia Ananiadou
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named entity recognition (NER) in social media (e.g., Twitter) is a challenging task due to the noisy nature of text. As part of our participation in the W-NUT 2016 Named Entity Recognition Shared Task, we proposed an unsupervised learning approach using deep neural networks and leverage a knowledge base (i.e., DBpedia) to bootstrap sparse entity types with weakly labelled data. To further boost the performance, we employed a more sophisticated tagging scheme and applied dropout as a regularisation technique in order to reduce overfitting. Even without hand-crafting linguistic features nor leveraging any of the W-NUT-provided gazetteers, we obtained robust performance with our approach, which ranked third amongst all shared task participants according to the official evaluation on a gold standard named entity-annotated corpus of 3,856 tweets.
Feature-Rich Twitter Named Entity Recognition and Classification
Authors: Utpal Kumar Sikdar, Bj{\"o}rn Gamb{\"a}ck
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Classification, Entity Extraction using GAN, General Classification, Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Question Answering

Twitter named entity recognition is the process of identifying proper names and classifying them into some predefined labels/categories. The paper introduces a Twitter named entity system using a supervised machine learning approach, namely Conditional Random Fields. A large set of different features was developed and the system was trained using these. The Twitter named entity task can be divided into two parts: i) Named entity extraction from tweets and ii) Twitter name classification into ten different types. For Twitter named entity recognition on unseen test data, our system obtained the second highest F1 score in the shared task: 63.22{\%}. The system performance on the classification task was worse, with an F1 measure of 40.06{\%} on unseen test data, which was the fourth best of the ten systems participating in the shared task.
Learning to Search for Recognizing Named Entities in Twitter
Authors: Ioannis Partalas, C{\'e}dric Lopez, Nadia Derbas, Ruslan Kalitvianski
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

We presented in this work our participation in the 2nd Named Entity Recognition for Twitter shared task. The task has been cast as a sequence labeling one and we employed a learning to search approach in order to tackle it. We also leveraged LOD for extracting rich contextual features for the named-entities. Our submission achieved F-scores of 46.16 and 60.24 for the classification and the segmentation tasks and ranked 2nd and 3rd respectively. The post-analysis showed that LOD features improved substantially the performance of our system as they counter-balance the lack of context in tweets. The shared task gave us the opportunity to test the performance of NER systems in short and noisy textual data. The results of the participated systems shows that the task is far to be considered as a solved one and methods with stellar performance in normal texts need to be revised.
DeepNNNER: Applying BLSTM-CNNs and Extended Lexicons to Named Entity Recognition in Tweets
Authors: Fabrice Dugas, Eric Nichols
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Diversity, Feature Engineering, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Task 2, Text Normalization, Word Embeddings

In this paper, we describe the DeepNNNER entry to The 2nd Workshop on Noisy User-generated Text (WNUT) Shared Task {\#}2: Named Entity Recognition in Twitter. Our shared task submission adopts the bidirectional LSTM-CNN model of Chiu and Nichols (2016), as it has been shown to perform well on both newswire and Web texts. It uses word embeddings trained on large-scale Web text collections together with text normalization to cope with the diversity in Web texts, and lexicons for target named entity classes constructed from publicly-available sources. Extended evaluation comparing the effectiveness of various word embeddings, text normalization, and lexicon settings shows that our system achieves a maximum F1-score of 47.24, performance surpassing that of the shared task{'}s second-ranked system.
ASU: An Experimental Study on Applying Deep Learning in Twitter Named Entity Recognition.
Authors: Michel Naim Gerguis, Cherif Salama, M. Watheq El-Kharashi
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Entity Extraction using GAN, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Opinion Mining, POS, Word Embeddings

This paper describes the ASU system submitted in the COLING W-NUT 2016 Twitter Named Entity Recognition (NER) task. We present an experimental study on applying deep learning to extracting named entities (NEs) from tweets. We built two Long Short-Term Memory (LSTM) models for the task. The first model was built to extract named entities without types while the second model was built to extract and then classify them into 10 fine-grained entity classes. In this effort, we show detailed experimentation results on the effectiveness of word embeddings, brown clusters, part-of-speech (POS) tags, shape features, gazetteers, and local context for the tweet input vector representation to the LSTM model. Also, we present a set of experiments, to better design the network parameters for the Twitter NER task. Our system was ranked the fifth out of ten participants with a final f1-score for the typed classes of 39{\%} and 55{\%} for the non typed ones.
UQAM-NTL: Named entity recognition in Twitter messages
Authors: Ngoc Tan Le, Fatma Mallek, Fatiha Sadat
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: BIG-bench Machine Learning, Language Modeling, Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

This paper describes our system used in the 2nd Workshop on Noisy User-generated Text (WNUT) shared task for Named Entity Recognition (NER) in Twitter, in conjunction with Coling 2016. Our system is based on supervised machine learning by applying Conditional Random Fields (CRF) to train two classifiers for two evaluations. The first evaluation aims at predicting the 10 fine-grained types of named entities; while the second evaluation aims at predicting no type of named entities. The experimental results show that our method has significantly improved Twitter NER performance.
Semi-supervised Named Entity Recognition in noisy-text
Authors: Shubhanshu Mishra, Jana Diesner
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Many of the existing Named Entity Recognition (NER) solutions are built based on news corpus data with proper syntax. These solutions might not lead to highly accurate results when being applied to noisy, user generated data, e.g., tweets, which can feature sloppy spelling, concept drift, and limited contextualization of terms and concepts due to length constraints. The models described in this paper are based on linear chain conditional random fields (CRFs), use the BIEOU encoding scheme, and leverage random feature dropout for up-sampling the training data. The considered features include word clusters and pre-trained distributed word representations, updated gazetteer features, and global context predictions. The latter feature allows for ingesting the meaning of new or rare tokens into the system via unsupervised learning and for alleviating the need to learn lexicon based features, which usually tend to be high dimensional. In this paper, we report on the solution [ST] we submitted to the WNUT 2016 NER shared task. We also present an improvement over our original submission [SI], which we built by using semi-supervised learning on labelled training data and pre-trained resourced constructed from unlabelled tweet data. Our ST solution achieved an F1 score of 1.2{\%} higher than the baseline (35.1{\%} F1) for the task of extracting 10 entity types. The SI resulted in an increase of 8.2{\%} in F1 score over the base-line (7.08{\%} over ST). Finally, the SI model{'}s evaluation on the test data achieved a F1 score of 47.3{\%} ({\textasciitilde}1.15{\%} increase over the 2nd best submitted solution). Our experimental setup and results are available as a standalone twitter NER tool at \url{https://github.com/napsternxg/TwitterNER}.
Twitter Geolocation Prediction Shared Task of the 2016 Workshop on Noisy User-generated Text
Authors: Bo Han, Afshin Rahimi, Leon Derczynski, Timothy Baldwin
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Sentiment Analysis

This paper presents the shared task for English Twitter geolocation prediction in WNUT 2016. We discuss details of task settings, data preparations and participant systems. The derived dataset and performance figures from each system provide baselines for future research in this realm.
CSIRO Data61 at the WNUT Geo Shared Task
Authors: Gaya Jayasinghe, Brian Jin, James Mchugh, Bella Robinson, Stephen Wan
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Information Retrieval, Retrieval

In this paper, we describe CSIRO Data61{'}s participation in the Geolocation shared task at the Workshop for Noisy User-generated Text. Our approach was to use ensemble methods to capitalise on four component methods: heuristics based on metadata, a label propagation method, timezone text classifiers, and an information retrieval approach. The ensembles we explored focused on examining the role of language technologies in geolocation prediction and also in examining the use of hard voting and cascading ensemble methods. Based on the accuracy of city-level predictions, our systems were the best performing submissions at this year{'}s shared task. Furthermore, when estimating the latitude and longitude of a user, our median error distance was accurate to within 30 kilometers.
Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features
Authors: Lianhua Chi, Kwan Hui Lim, Nebula Alam, Christopher J. Butler
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: General Classification

Knowing the location of a social media user and their posts is important for various purposes, such as the recommendation of location-based items/services, and locality detection of crisis/disasters. This paper describes our submission to the shared task {``}Geolocation Prediction in Twitter{''} of the 2nd Workshop on Noisy User-generated Text. In this shared task, we propose an algorithm to predict the location of Twitter users and tweets using a multinomial Naive Bayes classifier trained on Location Indicative Words and various textual features (such as city/country names, {\#}hashtags and @mentions). We compared our approach against various baselines based on Location Indicative Words, city/country names, {\#}hashtags and @mentions as individual feature sets, and experimental results show that our approach outperforms these baselines in terms of classification accuracy, mean and median error distance.
A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter
Authors: Yasuhide Miura, Motoki Taniguchi, Tomoki Taniguchi, Tomoko Ohkuma
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Denoising, Prediction

This paper describes a model that we submitted to W-NUT 2016 Shared task {\#}1: Geolocation Prediction in Twitter. Our model classifies a tweet or a user to a city using a simple neural networks structure with fully-connected layers and average pooling processes. From the findings of previous geolocation prediction approaches, we integrated various user metadata along with message texts and trained the model with them. In the test run of the task, the model achieved the accuracy of 40.91{\%} and the median distance error of 69.50 km in message-level prediction and the accuracy of 47.55{\%} and the median distance error of 16.13 km in user-level prediction. These results are moderate performances in terms of accuracy and best performances in terms of distance. The results show a promising extension of neural networks based models for geolocation prediction where recent advances in neural networks can be added to enhance our current simple model.
Flexible and Reliable Text Analytics in the Digital Humanities -- Some Methodological Considerations
Authors: Jonas Kuhn
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Cultural Vocal Bursts Intensity Prediction

The availability of Language Technology Resources and Tools generates a considerable methodological potential in the Digital Humanities: aspects of research questions from the Humanities and Social Sciences can be addressed on text collections in ways that were unavailable to traditional approaches. I start this talk by sketching some sample scenarios of Digital Humanities projects which involve various Humanities and Social Science disciplines, noting that the potential for a meaningful contribution to higher-level questions is highest when the employed language technological models are carefully tailored both (a) to characteristics of the given target corpus, and (b) to relevant analytical subtasks feeding the discipline-specific research questions. Keeping up a multidisciplinary perspective, I then point out a recurrent dilemma in Digital Humanities projects that follow the conventional set-up of collaboration: to build high-quality computational models for the data, fixed analytical targets should be specified as early as possible {--} but to be able to respond to Humanities questions as they evolve over the course of analysis, the analytical machinery should be kept maximally flexible. To reach both, I argue for a novel collaborative culture that rests on a more interleaved, continuous dialogue. (Re-)Specification of analytical targets should be an ongoing process in which the Humanities Scholars and Social Scientists play a role that is as important as the Computational Scientists{'} role. A promising approach lies in the identification of re-occurring types of analytical subtasks, beyond linguistic standard tasks, which can form building blocks for text analysis across disciplines, and for which corpus-based characterizations (viz. annotations) can be collected, compared and revised. On such grounds, computational modeling is more directly tied to the evolving research questions, and hence the seemingly opposing needs of reliable target specifications vs. {``}malleable{''} frameworks of analysis can be reconciled. Experimental work following this approach is under way in the Center for Reflected Text Analytics (CRETA) in Stuttgart.
Finding Rising and Falling Words
Authors: Erik Tjong Kim Sang
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

We examine two different methods for finding rising words (among which neologisms) and falling words (among which archaisms) in decades of magazine texts (millions of words) and in years of tweets (billions of words): one based on correlation coefficients of relative frequencies and time, and one based on comparing initial and final word frequencies of time intervals. We find that smoothing frequency scores improves the precision scores of both methods and that the correlation coefficients perform better on magazine text but worse on tweets. Since the two ranking methods find different words they can be used in side-by-side to study the behavior of words over time.
A Dataset for Multimodal Question Answering in the Cultural Heritage Domain
Authors: Shurong Sheng, Luc van Gool, Marie-Francine Moens
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Question Answering, Speech Recognition, Visual Question Answering (VQA)

Multimodal question answering in the cultural heritage domain allows visitors to ask questions in a more natural way and thus provides better user experiences with cultural objects while visiting a museum, landmark or any other historical site. In this paper, we introduce the construction of a golden standard dataset that will aid research of multimodal question answering in the cultural heritage domain. The dataset, which will be soon released to the public, contains multimodal content including images of typical artworks from the fascinating old-Egyptian Amarna period, related image-containing documents of the artworks and over 800 multimodal queries integrating visual and textual questions. The multimodal questions and related documents are all in English. The multimodal questions are linked to relevant paragraphs in the related documents that contain the answer to the multimodal query.
Extracting Social Networks from Literary Text with Word Embedding Tools
Authors: Gerhard Wohlgenannt, Ekaterina Chernyak, Dmitry Ilvovsky
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Language Modeling, Language Modelling, Word Embeddings

In this paper a social network is extracted from a literary text. The social network shows, how frequent the characters interact and how similar their social behavior is. Two types of similarity measures are used: the first applies co-occurrence statistics, while the second exploits cosine similarity on different types of word embedding vectors. The results are evaluated by a paid micro-task crowdsourcing survey. The experiments suggest that specific types of word embeddings like word2vec are well-suited for the task at hand and the specific circumstances of literary fiction text.
Exploration of register-dependent lexical semantics using word embeddings
Authors: Andrey Kutuzov, Elizaveta Kuzmenko, Anna Marakasova
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: General Classification, regression, Word Embeddings

We present an approach to detect differences in lexical semantics across English language registers, using word embedding models from distributional semantics paradigm. Models trained on register-specific subcorpora of the BNC corpus are employed to compare lists of nearest associates for particular words and draw conclusions about their semantic shifts depending on register in which they are used. The models are evaluated on the task of register classification with the help of the deep inverse regression approach. Additionally, we present a demo web service featuring most of the described models and allowing to explore word meanings in different English registers and to detect register affiliation for arbitrary texts. The code for the service can be easily adapted to any set of underlying models.