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This paper investigates the semantic prosody of three causal connectives: &quot;due to&quot;, &quot;owing to&quot; and &quot;because of&quot; in seven varieties of the English language. While research in the domain of English causality exists, we are not aware of studies that would cover the domain of causal connectives in English. Our claim is that connectives such as because of link two arguments, (at least) one of which will include a phrase that contributes to the interpretation of the relation as positive or negative, and hence define the prosody of the connective used. As our results demonstrate, the majority of the prosodies identified are negative for all three connectives; the proportions are stable across the varieties of English studied, and contrary to our expectations, we find no significant differences between the functions of the connectives and discourse preferences. Further, we investigate whether automatizing the sentiment annotation procedure via a simple language-model based classifier is possible. The initial results highlight the complexity of the task and the need for complicated systems, probably aided with other related datasets to achieve reasonable performance.


COLING 2020

A sentiment-annotated dataset of English causal connectives

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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

This paper investigates the semantic prosody of three causal connectives: "due to", "owing to" and "because of" in seven varieties of the English language. While research in the domain of English causality exists, we are not aware of studies that would cover the domain of causal connectives in English. Our claim is that connectives such as because of link two arguments, (at least) one of which will include a phrase that contributes to the interpretation of the relation as positive or negative, and hence define the prosody of the connective used. As our results demonstrate, the majority of the prosodies identified are negative for all three connectives; the proportions are stable across the varieties of English studied, and contrary to our expectations, we find no significant differences between the functions of the connectives and discourse preferences. Further, we investigate whether automatizing the sentiment annotation procedure via a simple language-model based classifier is possible. The initial results highlight the complexity of the task and the need for complicated systems, probably aided with other related datasets to achieve reasonable performance.


technical paper

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COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

Humor research is a multifaceted field that has led to a better understanding of humor's psychological effects and the development of different theories of humor. This paper's main objective is to develop a hierarchical schema for a fine-grained annotation of Conversational Humor. A prominent 19th-century play from Telugu, Kanyasulkam, is annotated to substantiate the work across cultures at multiple levels. Based on the Benign Violation Theory, the benignity or non-benignity of the interlocutor’s intentions is included within the framework. Under the categories mentioned above, in addition to different types of humor, the techniques utilized by these types are identified. Furthermore, the inter-annotator agreement is calculated to assess the accuracy and validity of the dataset. An in-depth analysis of the disagreement is performed to understand the subjectivity of humor better.

A Novel Annotation Schema for Conversational Humor: Capturing the cultural nuances in Kanyasulkam

We present the Prepositions Annotated with Supsersense Tags in Reddit International English ("PASTRIE") corpus, a new dataset containing manually annotated preposition supersenses of English data from presumed speakers of four L1s: English, French, German, and Spanish. The annotations are comprehensive, covering all preposition types and tokens in the sample. Along with the corpus, we provide analysis of distributional patterns across the included L1s and a discussion of the influence of L1s on L2 preposition choice.

PASTRIE: A Corpus of Prepositions Annotated with Supersense Tags in Reddit International English

We reevaluate an existing adpositional annotation scheme with respect to two thorny semantic domains: accompaniment and purpose. ‘Accompaniment’ broadly speaking includes two entities situated together or participating in the same event, while ‘purpose’ broadly speaking covers the desired outcome of an action, the intended use or evaluated use of an entity, and more. We argue the policy in the SNACS scheme for English should be recalibrated with respect to these clusters of interrelated meanings without adding complexity to the overall scheme. Our analysis highlights tradeoffs in lumping vs. splitting decisions as well as the flexibility afforded by the construal analysis.

Sprucing up Supersenses:  Untangling the Semantic Clusters of Accompaniment and Purpose

We report on an application of universal dependencies for the study of diachronic shifts in syntactic usage patterns. Our focus is on the evolution of Scientific English in the Late Modern English period (ca. 1700-1900). Our data set is the Royal Society Corpus (RSC), comprising the full set of publications of the Royal Society of London between 1665 and 1996. Our starting assumption is that over time, Scientific English develops specific syntactic choice preferences that increase efficiency in (expert-to-expert) communication. The specific hypothesis we pursue in this paper is that changing syntactic choice preferences lead to greater dependency locality/dependency length minimization, which is associated with positive effects for the efficiency of human as well as computational linguistic processing. As a basis for our measurements, we parsed the RSC using Stanford CoreNLP. Overall, we observe a decrease in dependency length, with long dependency structures becoming less frequent and short dependency structures becoming more frequent over time, thus marking an overall push towards greater communicative efficiency.

Exploring diachronic syntactic shifts with dependency length: the case of scientific English

Cross-lingual and multilingual methods have been widely suggested as options for dependency parsing of low-resource languages; however, these typically require the use of annotated data in related high-resource languages. In this paper, we evaluate the performance of these methods versus monolingual parsing of Tagalog, an Austronesian language which shares little typological similarity with any existing high-resource languages. We show that a monolingual model developed on minimal target language data consistently outperforms all cross-lingual and multilingual models when no closely-related sources exist for a low-resource language.

Parsing in the absence of related languages: Evaluating low-resource dependency parsers on Tagalog


This paper describes an approach to annotating noun incorporation in Universal Dependencies. It motivates the need to annotate this particular morphosyntactic phenomenon and justifies it with respect to frequency of the construction. A case study is presented in which the proposed annotation scheme is applied to a corpus of Chukchi, a highly-endangered language of Siberia that exhibits noun incorporation. We compare argument encoding in Chukchi, English and Russian and find that while in English and Russian discourse elements are primarily tracked through noun phrases and pronouns, in Chukchi they are tracked through agreement marking and incorporation, with a lesser role for noun phrases. 


Dependency analysis of noun incorporation in polysynthetic languages

The Universal Dependencies treebanks are a still-growing collection of treebanks for a wide range of languages, all annotated with a common inventory of dependency relations. Yet, the usages of the relations can be categorically different even for treebanks of the same language. We present a pilot study on identifying such inconsistencies in a language-independent way and conduct an experiment which illustrates that a proper handling of inconsistencies can improve parsing performance by several percentage points.

Identifying and Handling Cross-Treebank Inconsistencies in UD: A Pilot Study

The UD framework defines guidelines for a crosslingual syntactic analysis in the framework of dependency grammar, with the aim of providing a consistent treatment across languages that not only supports multilingual NLP applications but also facilitates typological studies. In the paper, we discuss possible UD treatments for several types of constructions encountered when branching out into the annotation of social media or spoken-language data. The examples include constructions where surface relations are deceptive, and constructions that either involve unconnected subtrees or structures with multiply-headed dependents. We argue that a unified treatment of constructions across languages will increase the consistency of the UD annotations and thus the quality of the treebanks for linguistic analysis.

I got a construction looks funny - Representing and recovering non-standard constructions in UD

In this presentation we look at how to compose word representations from token embeddings obtained from BPE sub-word tokens, so we can analyse the grammatical features of words. We explore four different methods of composing word representations and find that using an RNN performs the best consistently across eight different languages with varying typology and tokens-per-word ratio.

Composing Byte-Pair Encodings for Morphological Sequence Classification

In this presentation, we examine three types of multiword expressions (compound, fixed and flat) in the Irish UD Treebank. We provide guidelines on how these labels were applied in the Irish treebank, and discuss how some of our findings will be explored in future work.

A sentiment-annotated dataset of English causal connectives

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A Novel Annotation Schema for Conversational Humor: Capturing the cultural nuances in Kanyasulkam

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A sentiment-annotated dataset of English causal connectives

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Next from COLING 2020

A Novel Annotation Schema for Conversational Humor: Capturing the cultural nuances in Kanyasulkam

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BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

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