pwc-archive/papers-with-abstracts huggingface dataset:
Kawenn\'on:nis: the Wordmaker for Kanyen'k\'eha
Authors: Anna Kazantseva, Owennatekha Brian Maracle, Ronkwe{'}tiy{\'o}hstha Josiah Maracle, Aidan Pine
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Language Modeling, Language Modelling

In this paper we describe preliminary work on Kawenn{\'o}n:nis, a verb conjugator for Kanyen{'}k{\'e}ha (Ohsweken dialect). The project is the result of a collaboration between Onkwawenna Kentyohkwa Kanyen{'}k{\'e}ha immersion school and the Canadian National Research Council{'}s Indigenous Language Technology lab. The purpose of Kawenn{\'o}n:nis is to build on the educational successes of the Onkwawenna Kentyohkwa school and develop a tool that assists students in learning how to conjugate verbs in Kanyen{'}k{\'e}ha; a skill that is essential to mastering the language. Kawenn{\'o}n:nis is implemented with both web and mobile front-ends that communicate with an application programming interface that in turn communicates with a symbolic language model implemented as a finite state transducer. Eventually, it will serve as a foundation for several other applications for both Kanyen{'}k{\'e}ha and other Iroquoian languages.
Using the Nunavut Hansard Data for Experiments in Morphological Analysis and Machine Translation
Authors: Jeffrey Micher
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, Morphological Analysis, Translation

Inuktitut is a polysynthetic language spoken in Northern Canada and is one of the official languages of the Canadian territory of Nunavut. As such, the Nunavut Legislature publishes all of its proceedings in parallel English and Inuktitut. Several parallel English-Inuktitut corpora from these proceedings have been created from these data and are publically available. The corpus used for current experiments is described. Morphological processing of one of these corpora was carried out and details about the processing are provided. Then, the processed corpus was used in morphological analysis and machine translation (MT) experiments. The morphological analysis experiments aimed to improve the coverage of morphological processing of the corpus, and compare an additional experimental condition to previously published results. The machine translation experiments made use of the additional morphologically analyzed word types in a statistical machine translation system designed to translate to and from Inuktitut morphemes. Results are reported and next steps are defined.
Automatic Glossing in a Low-Resource Setting for Language Documentation
Authors: Sarah Moeller, Mans Hulden
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Decoder, Descriptive, Morphological Analysis

Morphological analysis of morphologically rich and low-resource languages is important to both descriptive linguistics and natural language processing. Field documentary efforts usually procure analyzed data in cooperation with native speakers who are capable of providing some level of linguistic information. Manually annotating such data is very expensive and the traditional process is arguably too slow in the face of language endangerment and loss. We report on a case study of learning to automatically gloss a Nakh-Daghestanian language, Lezgi, from a very small amount of seed data. We compare a conditional random field based sequence labeler and a neural encoder-decoder model and show that a nearly 0.9 F1-score on labeled accuracy of morphemes can be achieved with 3,000 words of transcribed oral text. Errors are mostly limited to morphemes with high allomorphy. These results are potentially useful for developing rapid annotation and fieldwork tools to support documentation of morphologically rich, endangered languages.
Annotation Schemes for Surface Construction Labeling
Authors: Lori Levin
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Semantic Parsing

In this talk I will describe the interaction of linguistics and language technologies in Surface Construction Labeling (SCL) from the perspective of corpus annotation tasks such as definiteness, modality, and causality. Linguistically, following Construction Grammar, SCL recognizes that meaning may be carried by morphemes, words, or arbitrary constellations of morpho-lexical elements. SCL is like Shallow Semantic Parsing in that it does not attempt a full compositional analysis of meaning, but rather identifies only the main elements of a semantic frame, where the frames may be invoked by constructions as well as lexical items. Computationally, SCL is different from tasks such as information extraction in that it deals only with meanings that are expressed in a conventional, grammaticalized way and does not address inferred meanings. I review the work of Dunietz (2018) on the labeling of causal frames including causal connectives and cause and effect arguments. I will describe how to design an annotation scheme for SCL, including isolating basic units of form and meaning and building a {``}constructicon{''}. I will conclude with remarks about the nature of universal categories and universal meaning representations in language technologies. This talk describes joint work with Jaime Carbonell, Jesse Dunietz, Nathan Schneider, and Miriam Petruck.
From Lexical Functional Grammar to Enhanced Universal Dependencies
Authors: Adam Przepi{\'o}rkowski, Agnieszka Patejuk
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

This is a summary of an invited talk.
Leaving no token behind: comprehensive (and delicious) annotation of MWEs and supersenses
Authors: Nathan Schneider
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

I will describe an unorthodox approach to lexical semantic annotation that prioritizes corpus coverage, democratizing analysis of a wide range of expression types. I argue that a lexicon-free lexical semantics{---}defined in terms of units and supersense tags{---}is an appetizing direction for NLP, as it is robust, cost-effective, easily understood, not too language-specific, and can serve as a foundation for richer semantic structure. Linguistic delicacies from the STREUSLE and DiMSUM corpora, which have been multiword- and supersense-annotated, attest to the veritable sm{\"o}rg{\aa}sbord of noncanonical constructions in English, including various flavors of prepositions, MWEs, and other curiosities. Bio: Nathan Schneider is an annotation schemer and computational modeler for natural language. As Assistant Professor of Linguistics and Computer Science at Georgetown University, he looks for synergies between practical language technologies and the scientific study of language. He specializes in broad-coverage semantic analysis: designing linguistic meaning representations, annotating them in corpora, and automating them with statistical natural language processing techniques. A central focus in this research is the nexus between grammar and lexicon as manifested in multiword expressions and adpositions/case markers. He has inhabited UC Berkeley (BA in Computer Science and Linguistics), Carnegie Mellon University (Ph.D. in Language Technologies), and the University of Edinburgh (postdoc). Now a Hoya and leader of NERT, he continues to play with data and algorithms for linguistic meaning.
Processing MWEs: Neurocognitive Bases of Verbal MWEs and Lexical Cohesiveness within MWEs
Authors: Shohini Bhattasali, Murielle Fabre, John Hale
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

Multiword expressions have posed a challenge in the past for computational linguistics since they comprise a heterogeneous family of word clusters and are difficult to detect in natural language data. In this paper, we present a fMRI study based on language comprehension to provide neuroimaging evidence for processing MWEs. We investigate whether different MWEs have distinct neural bases, e.g. if verbal MWEs involve separate brain areas from non-verbal MWEs and if MWEs with varying levels of cohesiveness activate dissociable brain regions. Our study contributes neuroimaging evidence illustrating that different MWEs elicit spatially distinct patterns of activation. We also adapt an association measure, usually used to detect MWEs, as a cognitively plausible metric for language processing.
The Interplay of Form and Meaning in Complex Medical Terms: Evidence from a Clinical Corpus
Authors: Leonie Gr{\"o}n, Ann Bertels, Kris Heylen
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Form

We conduct a corpus study to investigate the structure of multi-word expressions (MWEs) in the clinical domain. Based on an existing medical taxonomy, we develop an annotation scheme and label a sample of MWEs from a Dutch corpus with semantic and grammatical features. The analysis of the annotated data shows that the formal structure of clinical MWEs correlates with their conceptual properties. The insights gained from this study could inform the design of Natural Language Processing (NLP) systems for clinical writing, but also for other specialized genres.
Discourse and Lexicons: Lexemes, MWEs, Grammatical Constructions and Compositional Word Combinations to Signal Discourse Relations
Authors: Laurence Danlos
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

Lexicons generally record a list of lexemes or non-compositional multiword expressions. We propose to build lexicons for compositional word combinations, namely {``}secondary discourse connectives{''}. Secondary discourse connectives play the same function as {``}primary discourse connectives{''} but the latter are either lexemes or non-compositional multiword expressions. The paper defines primary and secondary connectives, and explains why it is possible to build a lexicon for the compositional ones and how it could be organized. It also puts forward the utility of such a lexicon in discourse annotation and parsing. Finally, it opens the discussion on the constructions that signal a discourse relation between two spans of text.
From Chinese Word Segmentation to Extraction of Constructions: Two Sides of the Same Algorithmic Coin
Authors: Jean-Pierre Colson
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Chinese Word Segmentation, Clustering

This paper presents the results of two experiments carried out within the framework of computational construction grammar. Starting from the constructionist point of view that there are just constructions in language, including lexical ones, we tested the validity of a clustering algorithm that was primarily designed for MWE extraction, the cpr-score (Colson, 2017), on Chinese word segmentation. Our results indicate a striking recall rate of 75 percent without any special adaptation to Chinese or to the lexicon, which confirms that there is some similarity between extracting MWEs and CWS. Our second experiment also suggests that the same methodology might be used for extracting more schematic or abstract constructions, thereby providing evidence for the statistical foundation of construction grammar.
Fixed Similes: Measuring aspects of the relation between MWE idiomatic semantics and syntactic flexibility
Authors: Stella Markantonatou, Panagiotis Kouris, Yanis Maistros
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Relation

We shed light on aspects of the relation between the semantics and the syntactic flexibility of multiword expressions by investigating fixed adjective similes (FS), a predicative multiword expression class not studied in this respect before. We find that only a subset of the syntactic structures observed in the data are related with idiomaticity. We identify and measure two aspects of idiomaticity, one of which seems to allow for predictions about FS syntactic flexibility. Our research draws on a resource developed with the semantic and detailed syntactic annotation of web-retrieved Modern Greek material, indicating frequency of use of the individual similes.
Fine-Grained Termhood Prediction for German Compound Terms Using Neural Networks
Authors: Anna H{\"a}tty, Sabine Schulte im Walde
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: General Classification, Prediction

Automatic term identification and investigating the understandability of terms in a specialized domain are often treated as two separate lines of research. We propose a combined approach for this matter, by defining fine-grained classes of termhood and framing a classification task. The classes reflect tiers of a term{'}s association to a domain. The new setup is applied to German closed compounds as term candidates in the domain of cooking. For the prediction of the classes, we compare several neural network architectures and also take salient information about the compounds{'} components into account. We show that applying a similar class distinction to the compounds{'} components and propagating this information within the network improves the compound class prediction results.
Towards a Computational Lexicon for Moroccan Darija: Words, Idioms, and Constructions
Authors: Jamal Laoudi, Claire Bonial, Lucia Donatelli, Stephen Tratz, Clare Voss
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation

In this paper, we explore the challenges of building a computational lexicon for Moroccan Darija (MD), an Arabic dialect spoken by over 32 million people worldwide but which only recently has begun appearing frequently in written form in social media. We raise the question of what belongs in such a lexicon and start by describing our work building traditional word-level lexicon entries with their English translations. We then discuss challenges in translating idiomatic MD text that led to creating multi-word expression lexicon entries whose meanings could not be fully derived from the individual words. Finally, we provide a preliminary exploration of constructions to be considered for inclusion in an MD constructicon by translating examples of English constructions and examining their MD counterparts.
Verbal Multiword Expressions in Basque Corpora
Authors: Uxoa I{\~n}urrieta, Itziar Aduriz, Ainara Estarrona, Itziar Gonzalez-Dios, Antton Gurrutxaga, Ruben Urizar, I{\~n}aki Alegria
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

This paper presents a Basque corpus where Verbal Multiword Expressions (VMWEs) were annotated following universal guidelines. Information on the annotation is given, and some ideas for discussion upon the guidelines are also proposed. The corpus is useful not only for NLP-related research, but also to draw conclusions on Basque phraseology in comparison with other languages.
Annotation of Tense and Aspect Semantics for Sentential AMR
Authors: Lucia Donatelli, Michael Regan, William Croft, Nathan Schneider
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Abstract Meaning Representation, Entity Typing, Sentence

Although English grammar encodes a number of semantic contrasts with tense and aspect marking, these semantics are currently ignored by Abstract Meaning Representation (AMR) annotations. This paper extends sentence-level AMR to include a coarse-grained treatment of tense and aspect semantics. The proposed framework augments the representation of finite predications to include a four-way temporal distinction (event time before, up to, at, or after speech time) and several aspectual distinctions (including static vs. dynamic, habitual vs. episodic, and telic vs. atelic). This will enable AMR to be used for NLP tasks and applications that require sophisticated reasoning about time and event structure.
A Syntax-Based Scheme for the Annotation and Segmentation of German Spoken Language Interactions
Authors: Swantje Westpfahl, Jan Gorisch
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Segmentation

Unlike corpora of written language where segmentation can mainly be derived from orthographic punctuation marks, the basis for segmenting spoken language corpora is not predetermined by the primary data, but rather has to be established by the corpus compilers. This impedes consistent querying and visualization of such data. Several ways of segmenting have been proposed, some of which are based on syntax. In this study, we developed and evaluated annotation and segmentation guidelines in reference to the topological field model for German. We can show that these guidelines are used consistently across annotators. We also investigated the influence of various interactional settings with a rather simple measure, the word-count per segment and unit-type. We observed that the word count and the distribution of each unit type differ in varying interactional settings and that our developed segmentation and annotation guidelines are used consistently across annotators. In conclusion, our syntax-based segmentations reflect interactional properties that are intrinsic to the social interactions that participants are involved in. This can be used for further analysis of social interaction and opens the possibility for automatic segmentation of transcripts.
An Annotated Corpus of Picture Stories Retold by Language Learners
Authors: Christine K{\"o}hn, Arne K{\"o}hn
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Attribute, Grammatical Error Correction, Reading Comprehension

Corpora with language learner writing usually consist of essays, which are difficult to annotate reliably and to process automatically due to the high degree of freedom and the nature of learner language. We develop a task which mildly constrains learner utterances to facilitate consistent annotation and reliable automatic processing but at the same time does not prime learners with textual information. In this task, learners retell a comic strip. We present the resulting task-based corpus of stories written by learners of German. We designed the corpus to be able to serve multiple purposes: The corpus was manually annotated, including target hypotheses and syntactic structures. We achieve a very high inter-annotator agreement: κ = 0.765 for the annotation of minimal target hypotheses and κ = 0.507 for the extended target hypotheses. We attribute this to the design of our task and the annotation guidelines, which are based on those for the Falko corpus (Reznicek et al., 2012).
Developing and Evaluating Annotation Procedures for Twitter Data during Hazard Events
Authors: Kevin Stowe, Martha Palmer, Jennings Anderson, Marina Kogan, Leysia Palen, Kenneth M. Anderson, Rebecca Morss, Julie Demuth, Heather Lazrus
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Decision Making

When a hazard such as a hurricane threatens, people are forced to make a wide variety of decisions, and the information they receive and produce can influence their own and others{'} actions. As social media grows more popular, an increasing number of people are using social media platforms to obtain and share information about approaching threats and discuss their interpretations of the threat and their protective decisions. This work aims to improve understanding of natural disasters through social media and provide an annotation scheme to identify themes in user{'}s social media behavior and facilitate efforts in supervised machine learning. To that end, this work has three contributions: (1) the creation of an annotation scheme to consistently identify hazard-related themes in Twitter, (2) an overview of agreement rates and difficulties in identifying annotation categories, and (3) a public release of both the dataset and guidelines developed from this scheme.
A Treebank for the Healthcare Domain
Authors: Nganthoibi Oinam, Diwakar Mishra, Pinal Patel, Narayan Choudhary, Hitesh Desai
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

This paper presents a treebank for the healthcare domain developed at ezDI. The treebank is created from a wide array of clinical health record documents across hospitals. The data has been de-identified and annotated for constituent syntactic structure. The treebank contains a total of 52053 sentences that have been sampled for subdomains as well as linguistic variations. The paper outlines the sampling process followed to ensure a better domain representation in the corpus, the annotation process and challenges, and corpus statistics. The Penn Treebank tagset and guidelines were largely followed, but there were many syntactic contexts that warranted adaptation of the guidelines. The treebank created was used to re-train the Berkeley parser and the Stanford parser. These parsers were also trained with the GENIA treebank for comparative quality assessment. Our treebank yielded great-er accuracy on both parsers. Berkeley parser performed better on our treebank with an average F1 measure of 91 across 5-folds. This was a significant jump from the out-of-the-box F1 score of 70 on Berkeley parser{'}s default grammar.
The RST Spanish-Chinese Treebank
Authors: Shuyuan Cao, Iria da Cunha, Mikel Iruskieta
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

Discourse analysis is necessary for different tasks of Natural Language Processing (NLP). As two of the most spoken languages in the world, discourse analysis between Spanish and Chinese is important for NLP research. This paper aims to present the first open Spanish-Chinese parallel corpus annotated with discourse information, whose theoretical framework is based on the Rhetorical Structure Theory (RST). We have evaluated and harmonized each annotation part to obtain a high annotated-quality corpus. The corpus is already available to the public.
The Other Side of the Coin: Unsupervised Disambiguation of Potentially Idiomatic Expressions by Contrasting Senses
Authors: Hessel Haagsma, Malvina Nissim, Johan Bos
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, Sentiment Analysis, Translation

Disambiguation of potentially idiomatic expressions involves determining the sense of a potentially idiomatic expression in a given context, e.g. determining that make hay in {`}Investment banks made hay while takeovers shone.{'} is used in a figurative sense. This enables automatic interpretation of idiomatic expressions, which is important for applications like machine translation and sentiment analysis. In this work, we present an unsupervised approach for English that makes use of literalisations of idiom senses to improve disambiguation, which is based on the lexical cohesion graph-based method by Sporleder and Li (2009). Experimental results show that, while literalisation carries novel information, its performance falls short of that of state-of-the-art unsupervised methods.
Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?
Authors: Ali Hakimi Parizi, Paul Cook
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, Prediction

In this paper, we propose the first model for multiword expression (MWE) compositionality prediction based on character-level neural network language models. Experimental results on two kinds of MWEs (noun compounds and verb-particle constructions) and two languages (English and German) suggest that character-level neural network language models capture knowledge of multiword expression compositionality, in particular for English noun compounds and the particle component of English verb-particle constructions. In contrast to many other approaches to MWE compositionality prediction, this character-level approach does not require token-level identification of MWEs in a training corpus, and can potentially predict the compositionality of out-of-vocabulary MWEs.
Constructing an Annotated Corpus of Verbal MWEs for English
Authors: Abigail Walsh, Claire Bonial, Kristina Geeraert, John P. McCrae, Nathan Schneider, Clarissa Somers
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Word Alignment

This paper describes the construction and annotation of a corpus of verbal MWEs for English, as part of the PARSEME Shared Task 1.1 on automatic identification of verbal MWEs. The criteria for corpus selection, the categories of MWEs used, and the training process are discussed, along with the particular issues that led to revisions in edition 1.1 of the annotation guidelines. Finally, an overview of the characteristics of the final annotated corpus is presented, as well as some discussion on inter-annotator agreement.