ymoslem/acl-abstracts huggingface dataset:
A Hybrid System to apply Natural Language Inference over Dependency Trees (2017)

This paper presents the development of a natural language inference engine that benefits from two current standard approaches; i.e., shallow and deep approaches. This system combines two non-deterministic algorithms: the approximate matching from the shallow approach and a theorem prover from the deep approach for handling multi-step inference tasks. The theorem prover is customized to accept dependency trees and apply inference rules to these trees. The inference rules are automatically generated as syllogistic rules from our test data (FraCaS test suite). The theorem prover exploits a non-deterministic matching algorithm within a standard backward chaining inference engine. We employ continuation programming as a way of seamlessly handling the combination of these two non-deterministic algorithms. Testing the matching algorithm on {\textquotedblleft}Generalized quantifiers{\textquotedblright} and {\textquotedblleft}adjectives{\textquotedblright} topics in FraCaS (MacCartney and Manning 2007), we achieved an accuracy of 92.8{\%} of the single-premise cases. For the multi-steps of inference, we checked the validity of our syllogistic rules and then extracted four generic instances that can be applied to more than one problem.
Ensembles of Classifiers for Cleaning Web Parallel Corpora and Translation Memories (2017)

The last years witnessed an increasing interest in the automatic methods for spotting false translation units in translation memories. This problem presents a great interest to industry as there are many translation memories that contain errors. A closely related line of research deals with identifying sentences that do not align in the parallel corpora mined from the web. The task of spotting false translations is modeled as a binary classification problem. It is known that in certain conditions the ensembles of classifiers improve over the performance of the individual members. In this paper we benchmark the most popular ensemble of classifiers: Majority Voting, Bagging, Stacking and Ada Boost at the task of spotting false translation units for translation memories and parallel web corpora. We want to know if for this specific problem any ensemble technique improves the performance of the individual classifiers and if there is a difference between the data in translation memories and parallel web corpora with respect to this task.
Exploiting and Evaluating a Supervised, Multilanguage Keyphrase Extraction pipeline for under-resourced languages (2017)

This paper evaluates different techniques for building a supervised, multilanguage keyphrase extraction pipeline for languages which lack a gold standard. Starting from an unsupervised English keyphrase extraction pipeline, we implement pipelines for Arabic, Italian, Portuguese, and Romanian, and we build test collections for languages which lack one. Then, we add a Machine Learning module trained on a well-known English language corpus and we evaluate the performance not only over English but on the other languages as well. Finally, we repeat the same evaluation after training the pipeline over an Arabic language corpus to check whether using a language-specific corpus brings a further improvement in performance. On the five languages we analyzed, results show an improvement in performance when using a machine learning algorithm, even if such algorithm is not trained and tested on the same language.
Multi-Lingual Phrase-Based Statistical Machine Translation for Arabic-English (2017)

In this paper, we implement a multilingual Statistical Machine Translation (SMT) system for Arabic-English Translation. Arabic Text can be categorized into standard and dialectal Arabic. These two forms of Arabic differ significantly. Different mono-lingual and multi-lingual hybrid SMT approaches are compared. Mono-lingual systems do always results in better translation accuracy in one Arabic form and poor accuracy in the other. Multi-lingual SMT models that are trained with pooled parallel MSA/dialectal data result in better accuracy. However, since the available parallel MSA data are much larger compared to dialectal data, multilingual models are biased to MSA. We propose in the work, a multi-lingual combination of different mono-lingual systems using an Arabic form classifier. The outcome of the classier directs the system to use the appropriate mono-lingual models (standard, dialectal, or mixture). Testing the different SMT systems shows that the proposed classifier-based SMT system outperforms mono-lingual and data pooled multi-lingual systems.
Same same, but different: Compositionality of paraphrase granularity levels (2017)

Paraphrases exist on different granularity levels, the most frequently used one being the sentential level. However, we argue that working on the sentential level is not optimal for both machines and humans, and that it would be easier and more efficient to work on sub-sentential levels. To prove this, we quantify and analyze the difference between paraphrases on both sentence and sub-sentence level in order to show the significance of the problem. First results on a preliminary dataset seem to confirm our hypotheses.
Inter-Annotator Agreement in Sentiment Analysis: Machine Learning Perspective (2017)

Manual text annotation is an essential part of Big Text analytics. Although annotators work with limited parts of data sets, their results are extrapolated by automated text classification and affect the final classification results. Reliability of annotations and adequacy of assigned labels are especially important in the case of sentiment annotations. In the current study we examine inter-annotator agreement in multi-class, multi-label sentiment annotation of messages. We used several annotation agreement measures, as well as statistical analysis and Machine Learning to assess the resulting annotations.
Fast and Accurate Decision Trees for Natural Language Processing Tasks (2017)

Decision trees have been previously employed in many machine-learning tasks such as part-of-speech tagging, lemmatization, morphological-attribute resolution, letter-to-sound conversion and statistical-parametric speech synthesis. In this paper we introduce an optimized tree-computation algorithm, which is based on the original ID3 algorithm. We also introduce a tree-pruning method that uses a development set to delete nodes from over-fitted models. The later mentioned algorithm also uses a results caching method for speed-up. Our algorithm is almost 200 times faster than a naive implementation and yields accurate results on our test datasets.
An Evolutionary Algorithm for Automatic Summarization (2017)

This paper proposes a novel method to select sentences for automatic summarization based on an evolutionary algorithm. The algorithm explores candidate summaries space following an objective function computed over ngrams probability distributions of the candidate summary and the source documents. This method does not consider a summary as a stack of independent sentences but as a whole text, and makes use of advances in unsupervised summarization evaluation. We compare this sentence extraction method to one of the best existing methods which is based on integer linear programming, and show its efficiency on three different acknowledged corpora.
Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics (2017)

We propose to use question answering (QA) data from Web forums to train chat-bots from scratch, i.e., without dialog data. First, we extract pairs of question and answer sentences from the typically much longer texts of questions and answers in a forum. We then use these shorter texts to train seq2seq models in a more efficient way. We further improve the parameter optimization using a new model selection strategy based on QA measures. Finally, we propose to use extrinsic evaluation with respect to a QA task as an automatic evaluation method for chatbot systems. The evaluation shows that the model achieves a MAP of 63.5{\%} on the extrinsic task. Moreover, our manual evaluation demonstrates that the model can answer correctly 49.5{\%} of the questions when they are similar in style to how questions are asked in the forum, and 47.3{\%} of the questions, when they are more conversational in style.
Mining Association Rules from Clinical Narratives (2017)

Shallow text analysis (Text Mining) uses mainly Information Extraction techniques. The low resource languages do not allow application of such traditional techniques with sufficient accuracy and recall on big data. In contrast, Data Mining approaches provide an opportunity to make deep analysis and to discover new knowledge. Frequent pattern mining approaches are used mainly for structured information in databases and are a quite challenging task in text mining. Unfortunately, most frequent pattern mining approaches do not use contextual information for extracted patterns: general patterns are extracted regardless of the context. We propose a method that processes raw informal texts (from health discussion forums) and formal texts (outpatient records) in Bulgarian language. In addition we use some context information and small terminological lexicons to generalize extracted frequent patterns. This allows to map informal expression of medical terminology to the formal one and to generate automatically resources.
Sentence-Level Multilingual Multi-modal Embedding for Natural Language Processing (2017)

We propose a novel discriminative ranking model that learns embeddings from multilingual and multi-modal data, meaning that our model can take advantage of images and descriptions in multiple languages to improve embedding quality. To that end, we introduce an objective function that uses pairwise ranking adapted to the case of three or more input sources. We compare our model against different baselines, and evaluate the robustness of our embeddings on image{--}sentence ranking (ISR), semantic textual similarity (STS), and neural machine translation (NMT). We find that the additional multilingual signals lead to improvements on all three tasks, and we highlight that our model can be used to consistently improve the adequacy of translations generated with NMT models when re-ranking n-best lists.
Role-based model for Named Entity Recognition (2017)

Named Entity Recognition (NER) poses new challenges in real-world documents in which there are entities with different roles according to their purpose or meaning. Retrieving all the possible entities in scenarios in which only a subset of them based on their role is needed, produces noise on the overall precision. This work proposes a NER model that relies on role classification models that support recognizing entities with a specific role. The proposed model has been implemented in two use cases using Spanish drug Summary of Product Characteristics: identification of therapeutic indications and identification of adverse reactions. The results show how precision is increased using a NER model that is oriented towards a specific role and discards entities out of scope.
Towards the Improvement of Automatic Emotion Pre-annotation with Polarity and Subjective Information (2017)

Emotion detection has a high potential positive impact on the benefit of business, society, politics or education. Given this, the main objective of our research is to contribute to the resolution of one of the most important challenges in textual emotion detection: emotional corpora annotation. This will be tackled by proposing a semi-automatic methodology. It consists in two main phases: (1) an automatic process to pre-annotate the unlabelled sentences with a reduced number of emotional categories; and (2) a manual process of refinement where human annotators will determine which is the dominant emotion between the pre-defined set. Our objective in this paper is to show the pre-annotation process, as well as to evaluate the usability of subjective and polarity information in this process. The evaluation performed confirms clearly the benefits of employing the polarity and subjective information on emotion detection and thus endorses the relevance of our approach.
Underspecification in Natural Language Understanding for Dialog Automation (2017)

With the increasing number of communication platforms that offer variety of ways of connecting two interlocutors, there is a resurgence of chat-based dialog systems. These systems, typically known as \textit{chatbots} have been successfully applied in a range of consumer and enterprise applications. A key technology in such chat-bots is robust natural language understanding (NLU) which can significantly influence and impact the efficacy of the conversation and ultimately the user-experience. While NLU is far from perfect, this paper illustrates the role of \textit{underspecification} and its impact on successful dialog completion.
Identification and Classification of the Most Important Moments in Students' Collaborative Chats (2017)

In this paper, we present an application for the automatic identification of the important moments that might occur during students' collaborative chats. The moments are detected based on the input received from the user, who may choose to perform an analysis on the topics that interest him/her. Moreover, the application offers various types of suggestive and intuitive graphics that aid the user in identification of such moments. There are two main aspects that are considered when identifying important moments: the concepts' frequency and distribution throughout the conversation and the chat tempo, which is analyzed for identifying intensively debated concepts. By the tempo of the chat we understand the rate at which the ideas are input by the chat participants, expressed by the utterances' timestamps.
Annotation of Entities and Relations in Spanish Radiology Reports (2017)

Radiology reports express the results of a radiology study and contain information about anatomical entities, findings, measures and impressions of the medical doctor. The use of information extraction techniques can help physicians to access this information in order to understand data and to infer further knowledge. Supervised machine learning methods are very popular to address information extraction, but are usually domain and language dependent. To train new classification models, annotated data is required. Moreover, annotated data is also required as an evaluation resource of information extraction algorithms. However, one major drawback of processing clinical data is the low availability of annotated datasets. For this reason we performed a manual annotation of radiology reports written in Spanish. This paper presents the corpus, the annotation schema, the annotation guidelines and further insight of the data.
Towards Replicability in Parsing (2017)

We investigate parsing replicability across 7 languages (and 8 treebanks), showing that choices concerning the use of grammatical functions in parsing or evaluation, the influence of the rare word threshold, as well as choices in test sentences and evaluation script options have considerable and often unexpected effects on parsing accuracies. All of those choices need to be carefully documented if we want to ensure replicability.
Automatic Identification of AltLexes using Monolingual Parallel Corpora (2017)

The automatic identification of discourse relations is still a challenging task in natural language processing. Discourse connectives, such as since or but, are the most informative cues to identify explicit relations; however discourse parsers typically use a closed inventory of such connectives. As a result, discourse relations signalled by markers outside these inventories (i.e. AltLexes) are not detected as effectively. In this paper, we propose a novel method to leverage parallel corpora in text simplification and lexical resources to automatically identify alternative lexicalizations that signal discourse relation. When applied to the Simple Wikipedia and Newsela corpora along with WordNet and the PPDB, the method allowed the automatic discovery of 91 AltLexes.
On the stylistic evolution from communism to democracy: Solomon Marcus study case (2017)

In this article we propose a stylistic analysis of Solomon Marcus' non-scientific published texts, gathered in six volumes, aiming to uncover some of his quantitative and qualitative fingerprints. Moreover, we compare and cluster two distinct periods of time in his writing style: 22 years of communist regime (1967-1989) and 27 years of democracy (1990-2016). The distributional analysis of Marcus' text reveals that the passing from the communist regime period to democracy is sharply marked by two complementary changes in Marcus' writing: in the pre-democracy period, the communist norms of writing style demanded on the one hand long phrases, long words and clich{\'e}s, and on the other hand, a short list of preferred {\textquotedblleft}official{\textquotedblright} topics; in democracy tendency was towards shorten phrases and words while approaching a broader area of topics.
Building timelines of soccer matches from Twitter (2017)

This demo paper presents a system that builds a timeline with salient actions of a soccer game, based on the tweets posted by users. It combines information provided by external knowledge bases to enrich the content of tweets and applies graph theory to model relations between actions (e.g. goals, penalties) and participants of a game (e.g. players, teams). In the demo, a web application displays in nearly real-time the actions detected from tweets posted by users for a given match of Euro 2016. Our tools are freely available at \url{https://bitbucket.org/eamosse/event_tracking}.
You`ll Never Tweet Alone: Building Sports Match Timelines from Microblog Posts (2017)

In this paper, we propose an approach to build a timeline with actions in a sports game based on tweets. We combine information provided by external knowledge bases to enrich the content of the tweets, and apply graph theory to model relations between actions and participants in a game. We demonstrate the validity of our approach using tweets collected during the EURO 2016 Championship and evaluate the output against live summaries produced by sports channels.
Graph-based Event Extraction from Twitter (2017)

Detecting which tweets describe a specific event and clustering them is one of the main challenging tasks related to Social Media currently addressed in the NLP community. Existing approaches have mainly focused on detecting spikes in clusters around specific keywords or Named Entities (NE). However, one of the main drawbacks of such approaches is the difficulty in understanding when the same keywords describe different events. In this paper, we propose a novel approach that exploits NE mentions in tweets and their entity context to create a temporal event graph. Then, using simple graph theory techniques and a PageRank-like algorithm, we process the event graphs to detect clusters of tweets describing the same events. Experiments on two gold standard datasets show that our approach achieves state-of-the-art results both in terms of evaluation performances and the quality of the detected events.
Opinion Mining in Social Networks versus Electoral Polls (2017)

The recent failures of traditional poll models, like the predictions in United Kingdom with the Brexit, or in United States presidential elections with the victory of Donald Trump, have been noteworthy. With the decline of traditional poll models and the growth of the social networks, automatic tools are gaining popularity to make predictions in this context. In this paper we present our approximation and compare it with a real case: the 2017 French presidential election.
Corpus Creation and Initial SMT Experiments between Spanish and Shipibo-konibo (2017)

In this paper, we present the first attempts to develop a machine translation (MT) system between Spanish and Shipibo-konibo (es-shp). There are very few digital texts written in Shipibo-konibo and even less bilingual texts that can be aligned, hence we had to create a parallel corpus using both bilingual and monolingual texts. We will describe how this corpus was made, as well as the process we followed to improve the quality of the sentences used to build a statistical MT model or SMT. The results obtained surpassed the baseline proposed (dictionary based) and made a promising result for further development considering the size of corpus used. Finally, it is expected that this MT system can be reinforced with the use of additional linguistic rules and automatic language processing functions that are being implemented.
Russian-Tatar Socio-Political Thesaurus: Methodology, Challenges, the Status of the Project (2017)

This paper discusses the general methodology and important practical aspects of implementing a new bilingual lexical resource {--} the Russian-Tatar Socio-Political Thesaurus that is being developed on the basis of the Russian RuThes thesaurus format as a hierarchy of concepts viewed as units of thought. Each concept is linked with a set of language expressions (words and collocations) referring to it in texts (text entries). Currently the Russian-Tatar Socio-Political Thesaurus includes 6,000 concepts, while new concepts and text entries are being constantly added to it. The paper outlines main challenges of translating concept names and their text entries into Tatar, and describes ways of reflecting the specificity of the Tatar lexical-semantic system.
On a Chat Bot Finding Answers with Optimal Rhetoric Representation (2017)

We demo a chat bot with the focus on complex, multi-sentence questions that enforce what we call rhetoric agreement of answers with questions. Chat bot finds answers which are not only relevant by topic but also match the question by style, argumentation patterns, communication means, experience level and other attributes. The system achieves rhetoric agreement by learning pairs of discourse trees (DTs) for question (Q) and answer (A). We build a library of best answer DTs for most types of complex questions. To better recognize a valid rhetoric agreement between Q and A, DTs are extended with the labels for communicative actions. An algorithm for finding the best DT for an A, given a Q, is evaluated.
Detecting Online Hate Speech Using Context Aware Models (2017)

In the wake of a polarizing election, the cyber world is laden with hate speech. Context accompanying a hate speech text is useful for identifying hate speech, which however has been largely overlooked in existing datasets and hate speech detection models. In this paper, we provide an annotated corpus of hate speech with context information well kept. Then we propose two types of hate speech detection models that incorporate context information, a logistic regression model with context features and a neural network model with learning components for context. Our evaluation shows that both models outperform a strong baseline by around 3{\%} to 4{\%} in F1 score and combining these two models further improve the performance by another 7{\%} in F1 score.
A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates (2017)

In the context of investigative journalism, we address the problem of automatically identifying which claims in a given document are most worthy and should be prioritized for fact-checking. Despite its importance, this is a relatively understudied problem. Thus, we create a new corpus of political debates, containing statements that have been fact-checked by nine reputable sources, and we train machine learning models to predict which claims should be prioritized for fact-checking, i.e., we model the problem as a ranking task. Unlike previous work, which has looked primarily at sentences in isolation, in this paper we focus on a rich input representation modeling the context: relationship between the target statement and the larger context of the debate, interaction between the opponents, and reaction by the moderator and by the public. Our experiments show state-of-the-art results, outperforming a strong rivaling system by a margin, while also confirming the importance of the contextual information.