ymoslem/acl-abstracts huggingface dataset:
Online Editor for WordNets (2018)

The paper presents an online editor for lexical-semantic databases with relational structure similar to the structure of WordNet {--} Hydra for Web. It supports functionalities for editing of relational data (including query, creation, change, and linking of relational objects), simultaneous access of multiple user profiles, parallel data visualization and editing of the data on top of single- and parallel mode visualization of the language data.
The Effect of Unobserved Word-Context Co-occurrences on a VectorMixture Approach for Compositional Distributional Semantics (2018)

Swivel (Submatrix-WIse Vector Embedding Learner) is a distributional semantic model based on counting point-wise mutual information values, capable of capturing word-context co-occurrences in the PMI matrix that were not noted in the training corpus. This model outperforms mainstream word embedding training algorithms such as Continuous Bag-of-Words, GloVe and Skip-Gram in word similarity and word analogy tasks. But the properness of these intrinsic tasks could be questioned, and it is unclear if the ability to count unobservable word-context co-occurrences could also be helpful for downstream tasks. In this work we propose a comparison of Word2Vec and Swivel for two downstream tasks based on natural language sentence matching: the paraphrase detection task and the textual entailment task. As a result, we reveal that Swivel outperforms Word2Vec in both cases, but the difference is minuscule. We can conclude, that the ability to learn embeddings for rarely co-occurring words is not so crucial for downstream tasks.
Introducing Computational Linguistics and NLP to High School Students (2018)

The paper addresses a possible way of introducing core concepts of Computational Linguistics through problems given at the linguistic contests organized for high school students in Bulgaria and abroad. Following a brief presentation of the foundation and the underlying objective of these contests, we outline some of the types of problems as reflecting the different levels of language processing and the diversity of approaches and tasks to be solved. By presenting the variety of problems given so far through the years, we would like to attract the attention of the academic community to this captivating method through which high school students might be acquainted with the challenges and the main goals of Computational Linguistics (CL) and Natural Language Processing (NLP).
Linguistic Problems on Number Names (2018)

This paper presents a contrastive investigation of linguistic problems based on number names in different languages and intended for secondary-school students. We examine the eight problems of this type that have been assigned at the International Linguistics Olympiad throughout the years and compare the phenomena in the number systems featured there with those of the working languages of the Olympiad and other languages known to be familiar to the participants. On the basis of a statistical analysis of the results achieved by the contestants we draw conclusions regarding the ways in which the difficulty of a problem depends on its structure and the kinds of linguistic phenomena featured in it.
Parallel Web Display of Transcribed Spoken Bulgarian with its Normalised Version and an Indexed List of Lemmas (2018)

We present and discuss problems in creating a lemmatised index to transcriptions of Bulgarian speech, including the prerequisites for such an index, and why we consider an index preferable to a search engine for this particular kind of text.
Integrating Crowdsourcing in Language Learning (2018)

This article aims to illustrate the use of crowdsourcing in an educational context. The practical part illustrates and provides the results of an online test conducted among 12th grade high school students from Bulgaria in order to gain new knowledge, find out common characteristics among the tenses and revise for their upcoming exams. They along with some interesting and inspiring teaching ideas could be used in an educational environment to provide easier, quicker and more interactive acquisition of a language. The experiment has been conducted by means of Google forms and sets the beginning of the establishment of an annotated corpus of right and wrong uses of the Bulgarian and English tenses too.
Bulgarian--English Parallel Corpus for the Purposes of Creating Statistical Translation Model of the Verb Forms. General Conception, Structure, Resources and Annotation (2018)

This paper describes the process of creating a Bulgarian-English parallel corpus for the purposes of constructing a statistical translation model for verb forms in both languages. We briefly introduce the scientific problem behind the corpus, its main purpose, general conception, linguistic resources and annotation conception. In more details we describe the collection of language data for the purposes of creating the corpus, the preparatory processing of the gathered data, the annotation rules based on the characteristics of the gathered data and the chosen software. We discuss the current work on the training model and the future work on this linguistic resource and the aims of the scientific project.
Fingerprints in SMS messages: Automatic Recognition of a Short Message Sender Using Gradient Boosting (2018)

This paper considers the following question: Is it possible to tell who is the short message sender just by analyzing a typing style of the sender, and not the meaning of the content itself? If possible, how reliable would the judgment be? Are we leaving some kind of {\textquotedblleft}fingerprint{\textquotedblright} when we text, and can we tell something about others based just on their typing style? For this purpose, a corpus of {\ensuremath{\sim}} 5,500 SMS messages was gathered from one person`s cell phone and two gradient boost classifiers were built: first one is trying to distinguish whether the message was sent by this exact person (cell phone owner) or by someone else; second one was trained to distinguish between messages sent by some public service (e.g. parking service, bank reports etc.) and messages sent by humans. The performance of the classifiers was evaluated in the 5-fold cross-validation setting, resulting in 73.6{\%} and 99.3{\%} overall accuracy for the first and the second classifier, respectively.
Entropy Reduction correlates with temporal lobe activity (2017)

Using the Entropy Reduction incremental complexity metric, we relate high gamma power signals from the brains of epileptic patients to incremental stages of syntactic analysis in English and French. We find that signals recorded intracranially from the anterior Inferior Temporal Sulcus (aITS) and the posterior Inferior Temporal Gyrus (pITG) correlate with word-by-word Entropy Reduction values derived from phrase structure grammars for those languages. In the anterior region, this correlation persists even in combination with surprisal co-predictors from PCFG and ngram models. The result confirms the idea that the brain`s temporal lobe houses a parsing function, one whose incremental processing difficulty profile reflects changes in grammatical uncertainty.
Learning an Input Filter for Argument Structure Acquisition (2017)

How do children learn a verb`s argument structure when their input contains nonbasic clauses that obscure verb transitivity? Here we present a new model that infers verb transitivity by learning to filter out non-basic clauses that were likely parsed in error. In simulations with child-directed speech, we show that this model accurately categorizes the majority of 50 frequent transitive, intransitive and alternating verbs, and jointly learns appropriate parameters for filtering parsing errors. Our model is thus able to filter out problematic data for verb learning without knowing in advance which data need to be filtered.
Grounding sound change in ideal observer models of perception (2017)

An important predictor of historical sound change, functional load, fails to capture insights from speech perception. Building on ideal observer models of word recognition, we devise a new definition of functional load that incorporates both a priori predictability and perceptual information. We explore this new measure with a simple model and find that it outperforms traditional measures.
\textquotedblleftOh, I`ve Heard That Before\textquotedblright: Modelling Own-Dialect Bias After Perceptual Learning by Weighting Training Data (2017)

Human listeners are able to quickly and robustly adapt to new accents and do so by using information about speaker`s identities. This paper will present experimental evidence that, even considering information about speaker`s identities, listeners retain a strong bias towards the acoustics of their own dialect after dialect learning. Participants' behaviour was accurately mimicked by a classifier which was trained on more cases from the base dialect and fewer from the target dialect. This suggests that imbalanced training data may result in automatic speech recognition errors consistent with those of speakers from populations over-represented in the training data.
Inherent Biases of Recurrent Neural Networks for Phonological Assimilation and Dissimilation (2017)

A recurrent neural network model of phonological pattern learning is proposed. The model is a relatively simple neural network with one recurrent layer, and displays biases in learning that mimic observed biases in human learning. Single-feature patterns are learned faster than two-feature patterns, and vowel or consonant-only patterns are learned faster than patterns involving vowels and consonants, mimicking the results of laboratory learning experiments. In non-recurrent models, capturing these biases requires the use of alpha features or some other representation of repeated features, but with a recurrent neural network, these elaborations are not necessary.
Predicting Japanese scrambling in the wild (2017)

Japanese speakers have a choice between canonical SOV and scrambled OSV word order to express the same meaning. Although previous experiments examine the influence of one or two factors for scrambling in a controlled setting, it is not yet known what kinds of multiple effects contribute to scrambling. This study uses naturally distributed data to test the multiple effects on scrambling simultaneously. A regression analysis replicates the NP length effect and suggests the influence of noun types, but it provides no evidence for syntactic priming, given-new ordering, and the animacy effect. These findings only show evidence for sentence-internal factors, but we find no evidence that discourse level factors play a role.
Readers vs. Writers vs. Texts: Coping with Different Perspectives of Text Understanding in Emotion Annotation (2017)

We here examine how different perspectives of understanding written discourse, like the reader`s, the writer`s or the text`s point of view, affect the quality of emotion annotations. We conducted a series of annotation experiments on two corpora, a popular movie review corpus and a genre- and domain-balanced corpus of standard English. We found statistical evidence that the writer`s perspective yields superior annotation quality overall. However, the quality one perspective yields compared to the other(s) seems to depend on the domain the utterance originates from. Our data further suggest that the popular movie review data set suffers from an atypical bimodal distribution which may decrease model performance when used as a training resource.
Finding Good Conversations Online: The Yahoo News Annotated Comments Corpus (2017)

This work presents a dataset and annotation scheme for the new task of identifying {\textquotedblleft}good{\textquotedblright} conversations that occur online, which we call ERICs: Engaging, Respectful, and/or Informative Conversations. We develop a taxonomy to reflect features of entire threads and individual comments which we believe contribute to identifying ERICs; code a novel dataset of Yahoo News comment threads (2.4k threads and 10k comments) and 1k threads from the Internet Argument Corpus; and analyze the features characteristic of ERICs. This is one of the largest annotated corpora of online human dialogues, with the most detailed set of annotations. It will be valuable for identifying ERICs and other aspects of argumentation, dialogue, and discourse.
Crowdsourcing discourse interpretations: On the influence of context and the reliability of a connective insertion task (2017)

Traditional discourse annotation tasks are considered costly and time-consuming, and the reliability and validity of these tasks is in question. In this paper, we investigate whether crowdsourcing can be used to obtain reliable discourse relation annotations. We also examine the influence of context on the reliability of the data. The results of a crowdsourced connective insertion task showed that the method can be used to obtain reliable annotations: The majority of the inserted connectives converged with the original label. Further, the method is sensitive to the fact that multiple senses can often be inferred for a single relation. Regarding the presence of context, the results show no significant difference in distributions of insertions between conditions overall. However, a by-item comparison revealed several characteristics of segments that determine whether the presence of context makes a difference in annotations. The findings discussed in this paper can be taken as evidence that crowdsourcing can be used as a valuable method to obtain insights into the sense(s) of relations.
A Code-Switching Corpus of Turkish-German Conversations (2017)

We present a code-switching corpus of Turkish-German that is collected by recording conversations of bilinguals. The recordings are then transcribed in two layers following speech and orthography conventions, and annotated with sentence boundaries and intersentential, intrasentential, and intra-word switch points. The total amount of data is 5 hours of speech which corresponds to 3614 sentences. The corpus aims at serving as a resource for speech or text analysis, as well as a collection for linguistic inquiries.
Annotating omission in statement pairs (2017)

We focus on the identification of omission in statement pairs. We compare three annotation schemes, namely two different crowdsourcing schemes and manual expert annotation. We show that the simplest of the two crowdsourcing approaches yields a better annotation quality than the more complex one. We use a dedicated classifier to assess whether the annotators' behavior can be explained by straightforward linguistic features. The classifier benefits from a modeling that uses lexical information beyond length and overlap measures. However, for our task, we argue that expert and not crowdsourcing-based annotation is the best compromise between annotation cost and quality.
Annotating Speech, Attitude and Perception Reports (2017)

We present REPORTS, an annotation scheme for the annotation of speech, attitude and perception reports. Such a scheme makes it possible to annotate the various text elements involved in such reports (e.g. embedding entity, complement, complement head) and their relations in a uniform way, which in turn facilitates the automatic extraction of information on, for example, complementation and vocabulary distribution. We also present the Ancient Greek corpus RAG (Thucydides' History of the Peloponnesian War), to which we have applied this scheme using the annotation tool BRAT. We discuss some of the issues, both theoretical and practical, that we encountered, show how the corpus helps in answering specific questions, and conclude that REPORTS fitted in well with our needs.
Consistent Classification of Translation Revisions: A Case Study of English-Japanese Student Translations (2017)

Consistency is a crucial requirement in text annotation. It is especially important in educational applications, as lack of consistency directly affects learners' motivation and learning performance. This paper presents a quality assessment scheme for English-to-Japanese translations produced by learner translators at university. We constructed a revision typology and a decision tree manually through an application of the OntoNotes method, i.e., an iteration of assessing learners' translations and hypothesizing the conditions for consistent decision making, as well as re-organizing the typology. Intrinsic evaluation of the created scheme confirmed its potential contribution to the consistent classification of identified erroneous text spans, achieving visibly higher Cohen`s kappa values, up to 0.831, than previous work. This paper also describes an application of our scheme to an English-to-Japanese translation exercise course for undergraduate students at a university in Japan.
Representation and Interchange of Linguistic Annotation. An In-Depth, Side-by-Side Comparison of Three Designs (2017)

For decades, most self-respecting linguistic engineering initiatives have designed and implemented custom representations for various layers of, for example, morphological, syntactic, and semantic analysis. Despite occasional efforts at harmonization or even standardization, our field today is blessed with a multitude of ways of encoding and exchanging linguistic annotations of these types, both at the levels of {\textquoteleft}abstract syntax', naming choices, and of course file formats. To a large degree, it is possible to work within and across design plurality by conversion, and often there may be good reasons for divergent design reflecting differences in use. However, it is likely that some abstract commonalities across choices of representation are obscured by more superficial differences, and conversely there is no obvious procedure to tease apart what actually constitute contentful vs. mere technical divergences. In this study, we seek to conceptually align three representations for common types of morpho-syntactic analysis, pinpoint what in our view constitute contentful differences, and reflect on the underlying principles and specific requirements that led to individual choices. We expect that a more in-depth understanding of these choices across designs may led to increased harmonization, or at least to more informed design of future representations.
TDB 1.1: Extensions on Turkish Discourse Bank (2017)

This paper presents the recent developments on Turkish Discourse Bank (TDB). First, the resource is summarized and an evaluation is presented. Then, TDB 1.1, i.e. enrichments on 10{\%} of the corpus are described (namely, senses for explicit discourse connectives, and new annotations for three discourse relation types - implicit relations, entity relations and alternative lexicalizations). The method of annotation is explained and the data are evaluated.
Two Layers of Annotation for Representing Event Mentions in News Stories (2017)

In this paper, we describe our preliminary study on annotating event mention as a part of our research on high-precision news event extraction models. To this end, we propose a two-layer annotation scheme, designed to separately capture the functional and conceptual aspects of event mentions. We hypothesize that the precision of models can be improved by modeling and extracting separately the different aspects of news events, and then combining the extracted information by leveraging the complementarities of the models. In addition, we carry out a preliminary annotation using the proposed scheme and analyze the annotation quality in terms of inter-annotator agreement.
Word Similarity Datasets for Indian Languages: Annotation and Baseline Systems (2017)

With the advent of word representations, word similarity tasks are becoming increasing popular as an evaluation metric for the quality of the representations. In this paper, we present manually annotated monolingual word similarity datasets of six Indian languages - Urdu, Telugu, Marathi, Punjabi, Tamil and Gujarati. These languages are most spoken Indian languages worldwide after Hindi and Bengali. For the construction of these datasets, our approach relies on translation and re-annotation of word similarity datasets of English. We also present baseline scores for word representation models using state-of-the-art techniques for Urdu, Telugu and Marathi by evaluating them on newly created word similarity datasets.
The BECauSE Corpus 2.0: Annotating Causality and Overlapping Relations (2017)

Language of cause and effect captures an essential component of the semantics of a text. However, causal language is also intertwined with other semantic relations, such as temporal precedence and correlation. This makes it difficult to determine when causation is the primary intended meaning. This paper presents BECauSE 2.0, a new version of the BECauSE corpus with exhaustively annotated expressions of causal language, but also seven semantic relations that are frequently co-present with causation. The new corpus shows high inter-annotator agreement, and yields insights both about the linguistic expressions of causation and about the process of annotating co-present semantic relations.
Catching the Common Cause: Extraction and Annotation of Causal Relations and their Participants (2017)

In this paper, we present a simple, yet effective method for the automatic identification and extraction of causal relations from text, based on a large English-German parallel corpus. The goal of this effort is to create a lexical resource for German causal relations. The resource will consist of a lexicon that describes constructions that trigger causality as well as the participants of the causal event, and will be augmented by a corpus with annotated instances for each entry, that can be used as training data to develop a system for automatic classification of causal relations. Focusing on verbs, our method harvested a set of 100 different lexical triggers of causality, including support verb constructions. At the moment, our corpus includes over 1,000 annotated instances. The lexicon and the annotated data will be made available to the research community.
Assessing SRL Frameworks with Automatic Training Data Expansion (2017)

We present the first experiment-based study that explicitly contrasts the three major semantic role labeling frameworks. As a prerequisite, we create a dataset labeled with parallel FrameNet-, PropBank-, and VerbNet-style labels for German. We train a state-of-the-art SRL tool for German for the different annotation styles and provide a comparative analysis across frameworks. We further explore the behavior of the frameworks with automatic training data generation. VerbNet provides larger semantic expressivity than PropBank, and we find that its generalization capacity approaches PropBank in SRL training, but it benefits less from training data expansion than the sparse-data affected FrameNet.
Inducing Script Structure from Crowdsourced Event Descriptions via Semi-Supervised Clustering (2017)

We present a semi-supervised clustering approach to induce script structure from crowdsourced descriptions of event sequences by grouping event descriptions into paraphrase sets (representing event types) and inducing their temporal order. Our approach exploits semantic and positional similarity and allows for flexible event order, thus overcoming the rigidity of previous approaches. We incorporate crowdsourced alignments as prior knowledge and show that exploiting a small number of alignments results in a substantial improvement in cluster quality over state-of-the-art models and provides an appropriate basis for the induction of temporal order. We also show a coverage study to demonstrate the scalability of our approach.