pwc-archive/papers-with-abstracts huggingface dataset:
Grouping business news stories based on salience of named entities
Authors: Lloren{\c{c}} Escoter, Lidia Pivovarova, Mian Du, Anisia Katinskaia, Roman Yangarber
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Articles, Word Embeddings

In news aggregation systems focused on broad news domains, certain stories may appear in multiple articles. Depending on the relative importance of the story, the number of versions can reach dozens or hundreds within a day. The text in these versions may be nearly identical or quite different. Linking multiple versions of a story into a single group brings several important benefits to the end-user{--}reducing the cognitive load on the reader, as well as signaling the relative importance of the story. We present a grouping algorithm, and explore several vector-based representations of input documents: from a baseline using keywords, to a method using salience{--}a measure of importance of named entities in the text. We demonstrate that features beyond keywords yield substantial improvements, verified on a manually-annotated corpus of business news stories.
``PageRank'' for Argument Relevance
Authors: Henning Wachsmuth, Benno Stein, Yamen Ajjour
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Argument Mining

Future search engines are expected to deliver pro and con arguments in response to queries on controversial topics. While argument mining is now in the focus of research, the question of how to retrieve the relevant arguments remains open. This paper proposes a radical model to assess relevance objectively at web scale: the relevance of an argument{'}s conclusion is decided by what other arguments reuse it as a premise. We build an argument graph for this model that we analyze with a recursive weighting scheme, adapting key ideas of PageRank. In experiments on a large ground-truth argument graph, the resulting relevance scores correlate with human average judgments. We outline what natural language challenges must be faced at web scale in order to stepwise bring argument relevance to web search engines.
Predicting Counselor Behaviors in Motivational Interviewing Encounters
Authors: Ver{\'o}nica P{\'e}rez-Rosas, Rada Mihalcea, Kenneth Resnicow, Satinder Singh, Lawrence An, Kathy J. Goggin, Delwyn Catley
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4

As the number of people receiving psycho-therapeutic treatment increases, the automatic evaluation of counseling practice arises as an important challenge in the clinical domain. In this paper, we address the automatic evaluation of counseling performance by analyzing counselors{'} language during their interaction with clients. In particular, we present a model towards the automation of Motivational Interviewing (MI) coding, which is the current gold standard to evaluate MI counseling. First, we build a dataset of hand labeled MI encounters; second, we use text-based methods to extract and analyze linguistic patterns associated with counselor behaviors; and third, we develop an automatic system to predict these behaviors. We introduce a new set of features based on semantic information and syntactic patterns, and show that they lead to accuracy figures of up to 90{\%}, which represent a significant improvement with respect to features used in the past.
Authorship Attribution Using Text Distortion
Authors: Efstathios Stamatatos
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Authorship Attribution, Authorship Verification, Text Categorization

Authorship attribution is associated with important applications in forensics and humanities research. A crucial point in this field is to quantify the personal style of writing, ideally in a way that is not affected by changes in topic or genre. In this paper, we present a novel method that enhances authorship attribution effectiveness by introducing a text distortion step before extracting stylometric measures. The proposed method attempts to mask topic-specific information that is not related to the personal style of authors. Based on experiments on two main tasks in authorship attribution, closed-set attribution and authorship verification, we demonstrate that the proposed approach can enhance existing methods especially under cross-topic conditions, where the training and test corpora do not match in topic.
Structured Learning for Temporal Relation Extraction from Clinical Records
Authors: Artuur Leeuwenberg, Marie-Francine Moens
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Prediction, Relation, Relation Extraction, Temporal Information Extraction, Temporal Relation Extraction

We propose a scalable structured learning model that jointly predicts temporal relations between events and temporal expressions (TLINKS), and the relation between these events and the document creation time (DCTR). We employ a structured perceptron, together with integer linear programming constraints for document-level inference during training and prediction to exploit relational properties of temporality, together with global learning of the relations at the document level. Moreover, this study gives insights in the results of integrating constraints for temporal relation extraction when using structured learning and prediction. Our best system outperforms the state-of-the art on both the CONTAINS TLINK task, and the DCTR task.
Entity Extraction in Biomedical Corpora: An Approach to Evaluate Word Embedding Features with PSO based Feature Selection
Authors: Shweta Yadav, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Boundary Detection, Entity Extraction using GAN, feature selection, Word Sense Disambiguation

Text mining has drawn significant attention in recent past due to the rapid growth in biomedical and clinical records. Entity extraction is one of the fundamental components for biomedical text mining. In this paper, we propose a novel approach of feature selection for entity extraction that exploits the concept of deep learning and Particle Swarm Optimization (PSO). The system utilizes word embedding features along with several other features extracted by studying the properties of the datasets. We obtain an interesting observation that compact word embedding features as determined by PSO are more effective compared to the entire word embedding feature set for entity extraction. The proposed system is evaluated on three benchmark biomedical datasets such as GENIA, GENETAG, and AiMed. The effectiveness of the proposed approach is evident with significant performance gains over the baseline models as well as the other existing systems. We observe improvements of 7.86{\%}, 5.27{\%} and 7.25{\%} F-measure points over the baseline models for GENIA, GENETAG, and AiMed dataset respectively.
Analyzing Semantic Change in Japanese Loanwords
Authors: Hiroya Takamura, Ryo Nagata, Yoshifumi Kawasaki
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Word Embeddings

We analyze semantic changes in loanwords from English that are used in Japanese (Japanese loanwords). Specifically, we create word embeddings of English and Japanese and map the Japanese embeddings into the English space so that we can calculate the similarity of each Japanese word and each English word. We then attempt to find loanwords that are semantically different from their original, see if known meaning changes are correctly captured, and show the possibility of using our methodology in language education.
Using support vector machines and state-of-the-art algorithms for phonetic alignment to identify cognates in multi-lingual wordlists
Authors: Gerhard J{\"a}ger, Johann-Mattis List, Pavel Sofroniev
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4

Most current approaches in phylogenetic linguistics require as input multilingual word lists partitioned into sets of etymologically related words (cognates). Cognate identification is so far done manually by experts, which is time consuming and as of yet only available for a small number of well-studied language families. Automatizing this step will greatly expand the empirical scope of phylogenetic methods in linguistics, as raw wordlists (in phonetic transcription) are much easier to obtain than wordlists in which cognate words have been fully identified and annotated, even for under-studied languages. A couple of different methods have been proposed in the past, but they are either disappointing regarding their performance or not applicable to larger datasets. Here we present a new approach that uses support vector machines to unify different state-of-the-art methods for phonetic alignment and cognate detection within a single framework. Training and evaluating these method on a typologically broad collection of gold-standard data shows it to be superior to the existing state of the art.
A Multi-task Approach to Predict Likability of Books
Authors: Suraj Maharjan, John Arevalo, Manuel Montes, Fabio A. Gonz{\'a}lez, Thamar Solorio
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Binary Classification, Feature Engineering, General Classification, Sentiment Analysis

We investigate the value of feature engineering and neural network models for predicting successful writing. Similar to previous work, we treat this as a binary classification task and explore new strategies to automatically learn representations from book contents. We evaluate our feature set on two different corpora created from Project Gutenberg books. The first presents a novel approach for generating the gold standard labels for the task and the other is based on prior research. Using a combination of hand-crafted and recurrent neural network learned representations in a dual learning setting, we obtain the best performance of 73.50{\%} weighted F1-score.
Aye or naw, whit dae ye hink? Scottish independence and linguistic identity on social media
Authors: Philippa Shoemark, Debnil Sur, Luke Shrimpton, Iain Murray, Sharon Goldwater
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Attribute

Political surveys have indicated a relationship between a sense of Scottish identity and voting decisions in the 2014 Scottish Independence Referendum. Identity is often reflected in language use, suggesting the intuitive hypothesis that individuals who support Scottish independence are more likely to use distinctively Scottish words than those who oppose it. In the first large-scale study of sociolinguistic variation on social media in the UK, we identify distinctively Scottish terms in a data-driven way, and find that these terms are indeed used at a higher rate by users of pro-independence hashtags than by users of anti-independence hashtags. However, we also find that in general people are less likely to use distinctively Scottish words in tweets with referendum-related hashtags than in their general Twitter activity. We attribute this difference to style shifting relative to audience, aligning with previous work showing that Twitter users tend to use fewer local variants when addressing a broader audience.
Incremental Discontinuous Phrase Structure Parsing with the GAP Transition
Authors: Maximin Coavoux, Beno{\^\i}t Crabb{\'e}
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4

This article introduces a novel transition system for discontinuous lexicalized constituent parsing called SR-GAP. It is an extension of the shift-reduce algorithm with an additional gap transition. Evaluation on two German treebanks shows that SR-GAP outperforms the previous best transition-based discontinuous parser (Maier, 2015) by a large margin (it is notably twice as accurate on the prediction of discontinuous constituents), and is competitive with the state of the art (Fern{\'a}ndez-Gonz{\'a}lez and Martins, 2015). As a side contribution, we adapt span features (Hall et al., 2014) to discontinuous parsing.
Multilingual Back-and-Forth Conversion between Content and Function Head for Easy Dependency Parsing
Authors: Ryosuke Kohita, Hiroshi Noji, Yuji Matsumoto
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Dependency Parsing

Universal Dependencies (UD) is becoming a standard annotation scheme cross-linguistically, but it is argued that this scheme centering on content words is harder to parse than the conventional one centering on function words. To improve the parsability of UD, we propose a back-and-forth conversion algorithm, in which we preprocess the training treebank to increase parsability, and reconvert the parser outputs to follow the UD scheme as a postprocess. We show that this technique consistently improves LAS across languages even with a state-of-the-art parser, in particular on core dependency arcs such as nominal modifier. We also provide an in-depth analysis to understand why our method increases parsability.
URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors
Authors: Patrick Littell, David R. Mortensen, Ke Lin, Katherine Kairis, Carlisle Turner, Lori Levin
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Language Identification, Language Modeling, Language Modelling, Multilingual NLP

We introduce the URIEL knowledge base for massively multilingual NLP and the lang2vec utility, which provides information-rich vector identifications of languages drawn from typological, geographical, and phylogenetic databases and normalized to have straightforward and consistent formats, naming, and semantics. The goal of URIEL and lang2vec is to enable multilingual NLP, especially on less-resourced languages and make possible types of experiments (especially but not exclusively related to NLP tasks) that are otherwise difficult or impossible due to the sparsity and incommensurability of the data sources. lang2vec vectors have been shown to reduce perplexity in multilingual language modeling, when compared to one-hot language identification vectors.
An experimental analysis of Noise-Contrastive Estimation: the noise distribution matters
Authors: Matthieu Labeau, Alex Allauzen, re
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Language Modeling, Language Modelling, Machine Translation, Speech Recognition

Noise Contrastive Estimation (NCE) is a learning procedure that is regularly used to train neural language models, since it avoids the computational bottleneck caused by the output softmax. In this paper, we attempt to explain some of the weaknesses of this objective function, and to draw directions for further developments. Experiments on a small task show the issues raised by an unigram noise distribution, and that a context dependent noise distribution, such as the bigram distribution, can solve these issues and provide stable and data-efficient learning.
Robust Training under Linguistic Adversity
Authors: Yitong Li, Trevor Cohn, Timothy Baldwin
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Sentiment Analysis, Speech Recognition, Word Embeddings

Deep neural networks have achieved remarkable results across many language processing tasks, however they have been shown to be susceptible to overfitting and highly sensitive to noise, including adversarial attacks. In this work, we propose a linguistically-motivated approach for training robust models based on exposing the model to corrupted text examples at training time. We consider several flavours of linguistically plausible corruption, include lexical semantic and syntactic methods. Empirically, we evaluate our method with a convolutional neural model across a range of sentiment analysis datasets. Compared with a baseline and the dropout method, our method achieves better overall performance.
Using Twitter Language to Predict the Real Estate Market
Authors: Mohammadzaman Zamani, H. Andrew Schwartz
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Marketing

We explore whether social media can provide a window into community real estate -foreclosure rates and price changes- beyond that of traditional economic and demographic variables. We find language use in Twitter not only predicts real estate outcomes as well as traditional variables across counties, but that including Twitter language in traditional models leads to a significant improvement (e.g. from Pearson r = :50 to r = :59 for price changes). We overcome the challenge of the relative sparsity and noise in Twitter language variables by showing that training on the residual error of the traditional models leads to more accurate overall assessments. Finally, we discover that it is Twitter language related to business (e.g. {`}company{'}, {`}marketing{'}) and technology (e.g. {`}technology{'}, {`}internet{'}), among others, that yield predictive power over economics.
Lexical Simplification with Neural Ranking
Authors: Gustavo Paetzold, Lucia Specia
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Complex Word Identification, Information Retrieval, Lexical Simplification, regression, Word Embeddings

We present a new Lexical Simplification approach that exploits Neural Networks to learn substitutions from the Newsela corpus - a large set of professionally produced simplifications. We extract candidate substitutions by combining the Newsela corpus with a retrofitted context-aware word embeddings model and rank them using a new neural regression model that learns rankings from annotated data. This strategy leads to the highest Accuracy, Precision and F1 scores to date in standard datasets for the task.
The limits of automatic summarisation according to ROUGE
Authors: Natalie Schluter
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4

This paper discusses some central caveats of summarisation, incurred in the use of the ROUGE metric for evaluation, with respect to optimal solutions. The task is NP-hard, of which we give the first proof. Still, as we show empirically for three central benchmark datasets for the task, greedy algorithms empirically seem to perform optimally according to the metric. Additionally, overall quality assurance is problematic: there is no natural upper bound on the quality of summarisation systems, and even humans are excluded from performing optimal summarisation.
Crowd-Sourced Iterative Annotation for Narrative Summarization Corpora
Authors: Jessica Ouyang, Serina Chang, Kathy Mckeown
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Abstractive Text Summarization, Sentence Compression, Text Generation, Text Summarization

We present an iterative annotation process for producing aligned, parallel corpora of abstractive and extractive summaries for narrative. Our approach uses a combination of trained annotators and crowd-sourcing, allowing us to elicit human-generated summaries and alignments quickly and at low cost. We use crowd-sourcing to annotate aligned phrases with the text-to-text generation techniques needed to transform each phrase into the other. We apply this process to a corpus of 476 personal narratives, which we make available on the Web.
Detecting negation scope is easy, except when it isn't
Authors: Federico Fancellu, Adam Lopez, Bonnie Webber, Hangfeng He
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Negation

Several corpora have been annotated with negation scope{---}the set of words whose meaning is negated by a cue like the word {``}not{''}{---}leading to the development of classifiers that detect negation scope with high accuracy. We show that for nearly all of these corpora, this high accuracy can be attributed to a single fact: they frequently annotate negation scope as a single span of text delimited by punctuation. For negation scopes not of this form, detection accuracy is low and under-sampling the easy training examples does not substantially improve accuracy. We demonstrate that this is partly an artifact of annotation guidelines, and we argue that future negation scope annotation efforts should focus on these more difficult cases.
MT/IE: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models
Authors: Sheng Zhang, Kevin Duh, Benjamin Van Durme
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Machine Translation, Open Information Extraction, Question Answering, Semantic Parsing, Translation

Cross-lingual information extraction is the task of distilling facts from foreign language (e.g. Chinese text) into representations in another language that is preferred by the user (e.g. English tuples). Conventional pipeline solutions decompose the task as machine translation followed by information extraction (or vice versa). We propose a joint solution with a neural sequence model, and show that it outperforms the pipeline in a cross-lingual open information extraction setting by 1-4 BLEU and 0.5-0.8 F1.
Learning to Negate Adjectives with Bilinear Models
Authors: Laura Rimell, Am Mabona, la, Luana Bulat, Douwe Kiela
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Word Embeddings

We learn a mapping that negates adjectives by predicting an adjective{'}s antonym in an arbitrary word embedding model. We show that both linear models and neural networks improve on this task when they have access to a vector representing the semantic domain of the input word, e.g. a centroid of temperature words when predicting the antonym of {`}cold{'}. We introduce a continuous class-conditional bilinear neural network which is able to negate adjectives with high precision.
Instances and concepts in distributional space
Authors: Gemma Boleda, Abhijeet Gupta, Sebastian Pad{\'o}
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4

Instances ({``}Mozart{''}) are ontologically distinct from concepts or classes ({``}composer{''}). Natural language encompasses both, but instances have received comparatively little attention in distributional semantics. Our results show that instances and concepts differ in their distributional properties. We also establish that instantiation detection ({``}Mozart {--} composer{''}) is generally easier than hypernymy detection ({``}chemist {--} scientist{''}), and that results on the influence of input representation do not transfer from hyponymy to instantiation.
Is this a Child, a Girl or a Car? Exploring the Contribution of Distributional Similarity to Learning Referential Word Meanings
Authors: Sina Zarrie{\ss}, David Schlangen
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Semantic Similarity, Semantic Textual Similarity

There has recently been a lot of work trying to use images of referents of words for improving vector space meaning representations derived from text. We investigate the opposite direction, as it were, trying to improve visual word predictors that identify objects in images, by exploiting distributional similarity information during training. We show that for certain words (such as entry-level nouns or hypernyms), we can indeed learn better referential word meanings by taking into account their semantic similarity to other words. For other words, there is no or even a detrimental effect, compared to a learning setup that presents even semantically related objects as negative instances.
The Semantic Proto-Role Linking Model
Authors: Aaron Steven White, Kyle Rawlins, Benjamin Van Durme
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: model, Semantic Role Labeling

We propose the semantic proto-role linking model, which jointly induces both predicate-specific semantic roles and predicate-general semantic proto-roles based on semantic proto-role property likelihood judgments. We use this model to empirically evaluate Dowty{'}s thematic proto-role linking theory.
The Language of Place: Semantic Value from Geospatial Context
Authors: Anne Cocos, Chris Callison-Burch
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Word Embeddings

There is a relationship between what we say and where we say it. Word embeddings are usually trained assuming that semantically-similar words occur within the same textual contexts. We investigate the extent to which semantically-similar words occur within the same geospatial contexts. We enrich a corpus of geolocated Twitter posts with physical data derived from Google Places and OpenStreetMap, and train word embeddings using the resulting geospatial contexts. Intrinsic evaluation of the resulting vectors shows that geographic context alone does provide useful information about semantic relatedness.