pwc-archive/papers-with-abstracts huggingface dataset:
Churn Identification in Microblogs using Convolutional Neural Networks with Structured Logical Knowledge
Authors: Mourad Gridach, Hatem Haddad, Hala Mulki
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Language Modeling, Language Modelling, Machine Translation, Sentiment Analysis, Speech Recognition

For brands, gaining new customer is more expensive than keeping an existing one. Therefore, the ability to keep customers in a brand is becoming more challenging these days. Churn happens when a customer leaves a brand to another competitor. Most of the previous work considers the problem of churn prediction using the Call Detail Records (CDRs). In this paper, we use micro-posts to classify customers into churny or non-churny. We explore the power of convolutional neural networks (CNNs) since they achieved state-of-the-art in various computer vision and NLP applications. However, the robustness of end-to-end models has some limitations such as the availability of a large amount of labeled data and uninterpretability of these models. We investigate the use of CNNs augmented with structured logic rules to overcome or reduce this issue. We developed our system called Churn{\_}teacher by using an iterative distillation method that transfers the knowledge, extracted using just the combination of three logic rules, directly into the weight of the DNNs. Furthermore, we used weight normalization to speed up training our convolutional neural networks. Experimental results showed that with just these three rules, we were able to get state-of-the-art on publicly available Twitter dataset about three Telecom brands.
Constructing an Alias List for Named Entities during an Event
Authors: Anietie Andy, Mark Dredze, Mugizi Rwebangira, Chris Callison-Burch
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Community Question Answering, Question Answering

In certain fields, real-time knowledge from events can help in making informed decisions. In order to extract pertinent real-time knowledge related to an event, it is important to identify the named entities and their corresponding aliases related to the event. The problem of identifying aliases of named entities that spike has remained unexplored. In this paper, we introduce an algorithm, EntitySpike, that identifies entities that spike in popularity in tweets from a given time period, and constructs an alias list for these spiked entities. EntitySpike uses a temporal heuristic to identify named entities with similar context that occur in the same time period (within minutes) during an event. Each entity is encoded as a vector using this temporal heuristic. We show how these entity-vectors can be used to create a named entity alias list. We evaluated our algorithm on a dataset of temporally ordered tweets from a single event, the 2013 Grammy Awards show. We carried out various experiments on tweets that were published in the same time period and show that our algorithm identifies most entity name aliases and outperforms a competitive baseline.
Incorporating Metadata into Content-Based User Embeddings
Authors: Linzi Xing, Michael J. Paul
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Attribute, Data Augmentation, Recommendation Systems

Low-dimensional vector representations of social media users can benefit applications like recommendation systems and user attribute inference. Recent work has shown that user embeddings can be improved by combining different types of information, such as text and network data. We propose a data augmentation method that allows novel feature types to be used within off-the-shelf embedding models. Experimenting with the task of friend recommendation on a dataset of 5,019 Twitter users, we show that our approach can lead to substantial performance gains with the simple addition of network and geographic features.
Simple Queries as Distant Labels for Predicting Gender on Twitter
Authors: Chris Emmery, Grzegorz Chrupa{\l}a, Walter Daelemans
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Gender Classification, General Classification

The majority of research on extracting missing user attributes from social media profiles use costly hand-annotated labels for supervised learning. Distantly supervised methods exist, although these generally rely on knowledge gathered using external sources. This paper demonstrates the effectiveness of gathering distant labels for self-reported gender on Twitter using simple queries. We confirm the reliability of this query heuristic by comparing with manual annotation. Moreover, using these labels for distant supervision, we demonstrate competitive model performance on the same data as models trained on manual annotations. As such, we offer a cheap, extensible, and fast alternative that can be employed beyond the task of gender classification.
A Dataset and Classifier for Recognizing Social Media English
Authors: Su Lin Blodgett, Johnny Wei, Brendan O{'}Connor
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Language Identification, Language Modeling, Language Modelling

While language identification works well on standard texts, it performs much worse on social media language, in particular dialectal language{---}even for English. First, to support work on English language identification, we contribute a new dataset of tweets annotated for English versus non-English, with attention to ambiguity, code-switching, and automatic generation issues. It is randomly sampled from all public messages, avoiding biases towards pre-existing language classifiers. Second, we find that a demographic language model{---}which identifies messages with language similar to that used by several U.S. ethnic populations on Twitter{---}can be used to improve English language identification performance when combined with a traditional supervised language identifier. It increases recall with almost no loss of precision, including, surprisingly, for English messages written by non-U.S. authors. Our dataset and identifier ensemble are available online.
Evaluating hypotheses in geolocation on a very large sample of Twitter
Authors: Bahar Salehi, Anders S{\o}gaard
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Fraud Detection

Recent work in geolocation has made several hypotheses about what linguistic markers are relevant to detect where people write from. In this paper, we examine six hypotheses against a corpus consisting of all geo-tagged tweets from the US, or whose geo-tags could be inferred, in a 19{\%} sample of Twitter history. Our experiments lend support to all six hypotheses, including that spelling variants and hashtags are strong predictors of location. We also study what kinds of common nouns are predictive of location after controlling for named entities such as dolphins or sharks
The Effect of Error Rate in Artificially Generated Data for Automatic Preposition and Determiner Correction
Authors: Fraser Bowen, Jon Dehdari, Josef van Genabith
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Grammatical Error Correction, Machine Translation

In this research we investigate the impact of mismatches in the density and type of error between training and test data on a neural system correcting preposition and determiner errors. We use synthetically produced training data to control error density and type, and {``}real{''} error data for testing. Our results show it is possible to combine error types, although prepositions and determiners behave differently in terms of how much error should be artificially introduced into the training data in order to get the best results.
Noisy Uyghur Text Normalization
Authors: Osman Tursun, Ruket Cakici
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Decoder, Text Normalization

Uyghur is the second largest and most actively used social media language in China. However, a non-negligible part of Uyghur text appearing in social media is unsystematically written with the Latin alphabet, and it continues to increase in size. Uyghur text in this format is incomprehensible and ambiguous even to native Uyghur speakers. In addition, Uyghur texts in this form lack the potential for any kind of advancement for the NLP tasks related to the Uyghur language. Restoring and preventing noisy Uyghur text written with unsystematic Latin alphabets will be essential to the protection of Uyghur language and improving the accuracy of Uyghur NLP tasks. To this purpose, in this work we propose and compare the noisy channel model and the neural encoder-decoder model as normalizing methods.
A Text Normalisation System for Non-Standard English Words
Authors: Emma Flint, Elliot Ford, Olivia Thomas, Andrew Caines, Paula Buttery
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Automatic Speech Recognition (ASR), Speech Recognition, text-to-speech, Text to Speech

This paper investigates the problem of text normalisation; specifically, the normalisation of non-standard words (NSWs) in English. Non-standard words can be defined as those word tokens which do not have a dictionary entry, and cannot be pronounced using the usual letter-to-phoneme conversion rules; e.g. lbs, 99.3{\%}, {\#}EMNLP2017. NSWs pose a challenge to the proper functioning of text-to-speech technology, and the solution is to spell them out in such a way that they can be pronounced appropriately. We describe our four-stage normalisation system made up of components for detection, classification, division and expansion of NSWs. Performance is favourabe compared to previous work in the field (Sproat et al. 2001, Normalization of non-standard words), as well as state-of-the-art text-to-speech software. Further, we update Sproat et al.{'}s NSW taxonomy, and create a more customisable system where users are able to input their own abbreviations and specify into which variety of English (currently available: British or American) they wish to normalise.
Huntsville, hospitals, and hockey teams: Names can reveal your location
Authors: Bahar Salehi, Dirk Hovy, Eduard Hovy, Anders S{\o}gaard
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Knowledge Base Population, Recommendation Systems, Sentiment Analysis

Geolocation is the task of identifying a social media user{'}s primary location, and in natural language processing, there is a growing literature on to what extent automated analysis of social media posts can help. However, not all content features are equally revealing of a user{'}s location. In this paper, we evaluate nine name entity (NE) types. Using various metrics, we find that GEO-LOC, FACILITY and SPORT-TEAM are more informative for geolocation than other NE types. Using these types, we improve geolocation accuracy and reduce distance error over various famous text-based methods.
Improving Document Clustering by Removing Unnatural Language
Authors: Myungha Jang, Jinho D. Choi, James Allan
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Clustering, Document Layout Analysis, Optical Character Recognition (OCR)

Technical documents contain a fair amount of unnatural language, such as tables, formulas, and pseudo-code. Unnatural language can bean important factor of confusing existing NLP tools. This paper presents an effective method of distinguishing unnatural language from natural language, and evaluates the impact of un-natural language detection on NLP tasks such as document clustering. We view this problem as an information extraction task and build a multiclass classification model identifying unnatural language components into four categories. First, we create a new annotated corpus by collecting slides and papers in various for-mats, PPT, PDF, and HTML, where unnatural language components are annotated into four categories. We then explore features available from plain text to build a statistical model that can handle any format as long as it is converted into plain text. Our experiments show that re-moving unnatural language components gives an absolute improvement in document cluster-ing by up to 15{\%}. Our corpus and tool are publicly available
Results of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition
Authors: Leon Derczynski, Eric Nichols, Marieke van Erp, Nut Limsopatham
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Clustering, Named Entity Recognition (NER)

This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet {``}so.. kktny in 30 mins?!{''} {--} even human experts find the entity {`}kktny{'} hard to detect and resolve. The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities. The task as described in this paper evaluated the ability of participating entries to detect and classify novel and emerging named entities in noisy text.
Distributed Representation, LDA Topic Modelling and Deep Learning for Emerging Named Entity Recognition from Social Media
Authors: Patrick Jansson, Shuhua Liu
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Deep Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper reports our participation in the W-NUT 2017 shared task on emerging and rare entity recognition from user generated noisy text such as tweets, online reviews and forum discussions. To accomplish this challenging task, we explore an approach that combines LDA topic modelling with deep learning on word level and character level embeddings. The LDA topic modelling generates topic representation for each tweet which is used as a feature for each word in the tweet. The deep learning component consists of two-layer bidirectional LSTM and a CRF output layer. Our submitted result performed at 39.98 (F1) on entity and 37.77 on surface forms. Our new experiments after submission reached a best performance of 41.81 on entity and 40.57 on surface forms.
Multi-channel BiLSTM-CRF Model for Emerging Named Entity Recognition in Social Media
Authors: Bill Y. Lin, Frank Xu, Zhiyi Luo, Kenny Zhu
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

In this paper, we present our multi-channel neural architecture for recognizing emerging named entity in social media messages, which we applied in the Novel and Emerging Named Entity Recognition shared task at the EMNLP 2017 Workshop on Noisy User-generated Text (W-NUT). We propose a novel approach, which incorporates comprehensive word representations with multi-channel information and Conditional Random Fields (CRF) into a traditional Bidirectional Long Short-Term Memory (BiLSTM) neural network without using any additional hand-craft features such as gazetteers. In comparison with other systems participating in the shared task, our system won the 2nd place.
Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets
Authors: Pius von D{\"a}niken, Mark Cieliebak
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Sentence, Transfer Learning

We present our system for the WNUT 2017 Named Entity Recognition challenge on Twitter data. We describe two modifications of a basic neural network architecture for sequence tagging. First, we show how we exploit additional labeled data, where the Named Entity tags differ from the target task. Then, we propose a way to incorporate sentence level features. Our system uses both methods and ranked second for entity level annotations, achieving an F1-score of 40.78, and second for surface form annotations, achieving an F1-score of 39.33.
Context-Sensitive Recognition for Emerging and Rare Entities
Authors: Jake Williams, Giovanni Santia
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Information Retrieval, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper is a shared task system description for the 2017 W-NUT shared task on Rare and Emerging Named Entities. Our paper describes the development and application of a novel algorithm for named entity recognition that relies only on the contexts of word forms. A comparison against the other submitted systems is provided.
A Feature-based Ensemble Approach to Recognition of Emerging and Rare Named Entities
Authors: Utpal Kumar Sikdar, Bj{\"o}rn Gamb{\"a}ck
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Entity Extraction using GAN, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Detecting previously unseen named entities in text is a challenging task. The paper describes how three initial classifier models were built using Conditional Random Fields (CRFs), Support Vector Machines (SVMs) and a Long Short-Term Memory (LSTM) recurrent neural network. The outputs of these three classifiers were then used as features to train another CRF classifier working as an ensemble. 5-fold cross-validation based on training and development data for the emerging and rare named entity recognition shared task showed precision, recall and F1-score of 66.87{\%}, 46.75{\%} and 54.97{\%}, respectively. For surface form evaluation, the CRF ensemble-based system achieved precision, recall and F1 scores of 65.18{\%}, 45.20{\%} and 53.30{\%}. When applied to unseen test data, the model reached 47.92{\%} precision, 31.97{\%} recall and 38.55{\%} F1-score for entity level evaluation, with the corresponding surface form evaluation values of 44.91{\%}, 30.47{\%} and 36.31{\%}.
Video Highlights Detection and Summarization with Lag-Calibration based on Concept-Emotion Mapping of Crowdsourced Time-Sync Comments
Authors: Qing Ping, Chaomei Chen
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Highlight Detection

With the prevalence of video sharing, there are increasing demands for automatic video digestion such as highlight detection. Recently, platforms with crowdsourced time-sync video comments have emerged worldwide, providing a good opportunity for highlight detection. However, this task is non-trivial: (1) time-sync comments often lag behind their corresponding shot; (2) time-sync comments are semantically sparse and noisy; (3) to determine which shots are highlights is highly subjective. The present paper aims to tackle these challenges by proposing a framework that (1) uses concept-mapped lexical-chains for lag-calibration; (2) models video highlights based on comment intensity and combination of emotion and concept concentration of each shot; (3) summarize each detected highlight using improved SumBasic with emotion and concept mapping. Experiments on large real-world datasets show that our highlight detection method and summarization method both outperform other benchmarks with considerable margins.
Multimedia Summary Generation from Online Conversations: Current Approaches and Future Directions
Authors: Enamul Hoque, Giuseppe Carenini
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Community Question Answering, Question Answering

With the proliferation of Web-based social media, asynchronous conversations have become very common for supporting online communication and collaboration. Yet the increasing volume and complexity of conversational data often make it very difficult to get insights about the discussions. We consider combining textual summary with visual representation of conversational data as a promising way of supporting the user in exploring conversations. In this paper, we report our current work on developing visual interfaces that present multimedia summary combining text and visualization for online conversations and how our solutions have been tailored for a variety of domain problems. We then discuss the key challenges and opportunities for future work in this research space.
Towards Improving Abstractive Summarization via Entailment Generation
Authors: Ramakanth Pasunuru, Han Guo, Mohit Bansal
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Abstractive Text Summarization, Decoder, Machine Translation, Multi-Task Learning, Natural Language Inference

Abstractive summarization, the task of rewriting and compressing a document into a short summary, has achieved considerable success with neural sequence-to-sequence models. However, these models can still benefit from stronger natural language inference skills, since a correct summary is logically entailed by the input document, i.e., it should not contain any contradictory or unrelated information. We incorporate such knowledge into an abstractive summarization model via multi-task learning, where we share its decoder parameters with those of an entailment generation model. We achieve promising initial improvements based on multiple metrics and datasets (including a test-only setting). The domain mismatch between the entailment (captions) and summarization (news) datasets suggests that the model is learning some domain-agnostic inference skills.
Coarse-to-Fine Attention Models for Document Summarization
Authors: Jeffrey Ling, Alex Rush, er
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Document Summarization, Machine Translation, Question Answering

Sequence-to-sequence models with attention have been successful for a variety of NLP problems, but their speed does not scale well for tasks with long source sequences such as document summarization. We propose a novel coarse-to-fine attention model that hierarchically reads a document, using coarse attention to select top-level chunks of text and fine attention to read the words of the chosen chunks. While the computation for training standard attention models scales linearly with source sequence length, our method scales with the number of top-level chunks and can handle much longer sequences. Empirically, we find that while coarse-to-fine attention models lag behind state-of-the-art baselines, our method achieves the desired behavior of sparsely attending to subsets of the document for generation.
Automatic Community Creation for Abstractive Spoken Conversations Summarization
Authors: Karan Singla, Evgeny Stepanov, Ali Orkan Bayer, Giuseppe Carenini, Giuseppe Riccardi
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Abstractive Text Summarization, Word Embeddings

Summarization of spoken conversations is a challenging task, since it requires deep understanding of dialogs. Abstractive summarization techniques rely on linking the summary sentences to sets of original conversation sentences, i.e. communities. Unfortunately, such linking information is rarely available or requires trained annotators. We propose and experiment automatic community creation using cosine similarity on different levels of representation: raw text, WordNet SynSet IDs, and word embeddings. We show that the abstractive summarization systems with automatic communities significantly outperform previously published results on both English and Italian corpora.