Call for papers

In conjunction with COLING 2018 in Santa Fe, we are organizing a half-day workshop, entitled “Linguistic Resources for Natural Language Processing” (LR4NLP).

This workshop aims to bring together linguists who are interested in developing large-coverage linguistic resources and researchers with an interest in developing real-world NLP software. These two communities have been working separately for many years. NLP researchers are typically more focused on technical issues specific to automatic text processing, where high-quality performance (e.g. recall and precision) is crucial. On the other hand, linguists tend to focus on problems related to the development of exhaustive and precise resources to pursue the wide spectrum of language – linguistically motivated resources based on a specific theory naturally, but which are for the most part ‘neutral’ vis-à-vis any NLP application. That is, they might be implemented using various grammatical formalisms (HPSG, LFG, NooJ, RG, TAG, XFST, etc.) and should be useable by a wide variety of NLP applications, such as parsing sentences, generating texts, transformational analysis, paraphrasing and translation, among others.

Recent progress in both computer science and linguistics is reducing many of these differences, with large-coverage, collaborative linguistic resources increasingly being used by robust NLP software. For example, NLP researchers can now use large dictionaries of multiword units and expressions, and several linguistic experiments have shown the feasibility of using extensive dictionaries and grammars in software applications that can parse sentences, as well as produce paraphrases and translations of sentences.

By encouraging members of both communities to mutually discuss current research on related topics, we hope to move towards a better understanding of the problems involved. Furthermore, examining ideas that offer reciprocal benefits to both communities may lead to potential collaborative efforts. This workshop focuses on the following questions:

Is it possible to construct NLP applications that remove ambiguities by using linguistic data alone, i.e. with no statistical methods?
How does one develop ‘neutral’ linguistic resources (dictionaries, lexicon-grammars, morphological, phrase-structure and transformational grammars, etc.) that can be used both to parse and generate texts, in one or multiple languages?
What are the limitations of stochastic and neural net based systems, as opposed to grammar and rule-based ones?

Topics should relate to linguistically-based NLP, such as:

Assessment of grammar and rule-based vs. statistical and neural net approaches to NLP
Natural language disambiguation based on handcrafted grammars
Development of large-coverage linguistic resources
Use of linguistic resources in paraphrasing and machine translation applications
Linguistically-based NLP for real-world applications
Paraphrase and translation generation
Phraseology of specialized languages
Processing of multiword units, discontinuous expressions, phrasal verbs, etc.
Surface structure realization
Transformational analysis and generation
Linguistically-based question-answering and summarization systems