Korea, Republic of

Entity Linking (EL) is the gateway into Knowledge Bases. Recent advances in EL utilize dense retrieval approaches for Candidate Generation, which addresses some of the shortcomings of the Lookup based approach of matching NER mentions against pre-computed dictionaries. In this work, we show that in the domain of Tweets, such methods suffer as users often include informal spelling, limited context, and lack of specificity, among other issues. We investigate these challenges on a large and recent Tweets benchmark for EL, empirically evaluate lookup and dense retrieval approaches, and demonstrate a hybrid solution using long contextual representation from Wikipedia is necessary to achieve considerable gains over previous work, achieving 0.93 recall.

COLING 2022

Robust Candidate Generation for Entity Linking on Short Social Media Texts

social media nlp

entity linking

knowledge graph

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

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). 

workshop paper

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### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

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 including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!

To gain access to this event page you are required to register. Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

The paper presents our system for coreference
resolution in Polish. We compare the system
with previous works for the Polish language
as well as with the multilingual approach in
the CRAC 2022 Shared Task on Multilingual
Coreference Resolution thanks to a universal,
multilingual data format and evaluation tool.
We discuss the accuracy, computational performance, and evaluation approach of the new System which is a faster, end-to-end solution.


Coreference Resolution for Polish: Improvements within the CRAC 2022 Shared Task

We describe the winning submission to the CRAC 2022 Shared Task on Multilingual Coreference Resolution. Our system first solves mention detection and then coreference linking on the retrieved spans with an antecedent-maximization approach, and both tasks are fine-tuned jointly with shared Transformer weights. We report results of fine-tuning a wide range of pretrained models. The center of this contribution are fine-tuned multilingual models. We found one large multilingual model with sufficiently large encoder to increase performance on all datasets across the board, with the benefit not limited only to the underrepresented languages or groups of typologically relative languages.

Effectivity of Multilingual Models for Coreference Resolution

T4 Knowledge Graph Embeddings for NLP: From Theory to Practice Part 2

T4 Knowledge Graph Embeddings for NLP: From Theory to Practice Part 1

On social media, additional context is often present in the form of annotations and meta-data such as the post’s author, mentions, Hashtags, and hyperlinks. We refer to these annotations as Non-Textual Units (NTUs). We posit that NTUs provide social context beyond their textual semantics and leveraging these units can enrich social media text representations. In this work we construct an NTU-centric social heterogeneous network to co-embed NTUs. We then principally integrate these NTU embeddings into a large pretrained language model by fine-tuning with these additional units. This adds context to noisy short-text social media. Experiments show that utilizing NTU-augmented text representations significantly outperforms existing text-only baselines by 2-5% relative points on many downstream tasks highlighting the importance of context to social media NLP. We also highlight that including NTU context into the initial layers of language model alongside text is better than using it after the text embedding is generated. Our work leads to the generation of holistic general purpose social media content embedding.

NTULM: Enriching Social Media Text Representations with Non-Textual Units

In this paper, we utilize recent advancements in social media natural language processing to obtain state-of-the-art syntactic dependency parsing results for social media English. We observe performance gains of 3.4 UAS and 4.0 LAS against the previous state-of-the-art as well as less disparity between African-American and Mainstream American English dialects. We demonstrate the computational social scientific utility of this parser for the task of socially embedded entity attribute analysis: for a specified entity, derive its semantic relationships from parses’ rich syntax, and accumulate and compare them across social variables. We conduct a case study on politicized views of U.S. official Anthony Fauci during the COVID-19 pandemic.

Cross-Dialect Social Media Dependency Parsing for Social Scientific Entity Attribute Analysis

This talk will discuss symbolic representations of sentences in context, focusing on abstract meaning representations (AMR), examining their capability for capturing certain aspects of meaning. A focus will be how AMR’s can be expanded to encompass figurative language, the recovery of implicit arguments and relations between events. These examples will be in English, and indeed some features of AMR are English-centric. Uniform Meaning Representations, a multi-sentence annotation scheme that is revising AMRs to make them more suitable for other languages, especially low resource languages will be introduced. UMRs include more formal logical scope, number, tense, aspect and modality as well as temporal relations. The talk will conclude with a discussion of ways in which these meaning representations can be enriched even more, by mapping to Wikidata Qnodes or through accessing the rich logical representations present in VerbNet, and thereby provide additional benefit to challenging applications.

Deep Semantics

NLP researches in developing economies are in many aspects unique compared to those in developed countries. For developing economies with middle and low incomes such as the Philippines, most NLP work focuses on relevant and useful applications for the immediate betterment of society, and involves minority languages of underrepresented regions that are usually low-resource. On the other hand, most of the global NLP research work has been focused on the major languages of the world, especially those of developed countries. Our works contribute to the advancement of research in multi-linguality for Philippine languages, adding to the complexity of NLP application development. Domain areas of these applications include health, disaster risk reduction management and climate change, and gender.
Moreover, organizational and collaborative efforts provide a critical infrastructure in building this
network of Philippine NLP researchers.

NLP in a developing economy

Health, demographic change and wellbeing

How can we leverage advances in computational methodology and technology to enhance language learning? In an era of widespread technological application, our understanding of the human-technology interaction and its impact appears to lag behind. In this talk, I outline an approach that combines emerging technologies with current neurocognitive theories in the area of language learning, with particular reference to second language acquisition and its representation. This approach relies on interactive mechanisms (bottom-up and top-down) and integrative processes (theory and application) to enable us to understand the differences between child language and adult language learning, as well as individual differences in diverse language learning contexts. Computational and neurocognitive studies along this line also have significant future promises with the use of AI and digital applications in education, thereby informing pedagogical designs and instructional innovations.

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Robust Candidate Generation for Entity Linking on Short Social Media Texts

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Coreference Resolution for Polish: Improvements within the CRAC 2022 Shared Task

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