Multiword expressions at length and in depth

The annual workshop on multiword expressions takes place since 2001 in conjunction with major computational linguistics conferences and attracts the attention of an ever-growing community working on a variety of languages, linguistic phenomena and related computational processing issues. MWE 2017 took place in Valencia, Spain, and represented a vibrant panorama of the current research landscape on the computational treatment of multiword expressions, featuring many high-quality submissions. Furthermore, MWE 2017 included the first shared task on multilingual identification of verbal multiword expressions. The shared task, with extended communal work, has developed important multilingual resources and mobilised several research groups in computational linguistics worldwide. This book contains extended versions of selected papers from the workshop. Authors worked hard to include detailed explanations, broader and deeper analyses, and new exciting results, which were thoroughly reviewed by an internationally renowned committee. We hope that this distinctly joint effort will provide a meaningful and useful snapshot of the multilingual state of the art in multiword expressions modelling and processing, and will be a point point of reference for future work

PersoNER: Persian named-entity recognition

© 1963-2018 ACL. Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network

Extended papers from the MWE 2017 workshop

The annual workshop on multiword expressions takes place since 2001 in conjunction with major computational linguistics conferences and attracts the attention of an ever-growing community working on a variety of languages, linguistic phenomena and related computational processing issues. MWE 2017 took place in Valencia, Spain, and represented a vibrant panorama of the current research landscape on the computational treatment of multiword expressions, featuring many high-quality submissions. Furthermore, MWE 2017 included the first shared task on multilingual identification of verbal multiword expressions. The shared task, with extended communal work, has developed important multilingual resources and mobilised several research groups in computational linguistics worldwide. This book contains extended versions of selected papers from the workshop. Authors worked hard to include detailed explanations, broader and deeper analyses, and new exciting results, which were thoroughly reviewed by an internationally renowned committee. We hope that this distinctly joint effort will provide a meaningful and useful snapshot of the multilingual state of the art in multiword expressions modelling and processing, and will be a point point of reference for future work

The automatic processing of multiword expressions in Irish

It is well-documented that Multiword Expressions (MWEs) pose a unique challenge to a variety of NLP tasks such as machine translation, parsing, information retrieval, and more. For low-resource languages such as Irish, these challenges can be exacerbated by the scarcity of data, and a lack of research in this topic. In order to improve handling of MWEs in various NLP tasks for Irish, this thesis will address both the lack of resources specifically targeting MWEs in Irish, and examine how these resources can be applied to said NLP tasks. We report on the creation and analysis of a number of lexical resources as part of this PhD research. Ilfhocail, a lexicon of Irish MWEs, is created through extract- ing MWEs from other lexical resources such as dictionaries. A corpus annotated with verbal MWEs in Irish is created for the inclusion of Irish in the PARSEME Shared Task 1.2. Additionally, MWEs were tagged in a bilingual EN-GA corpus for inclusion in experiments in machine translation. For the purposes of annotation, a categorisation scheme for nine categories of MWEs in Irish is created, based on combining linguistic analysis on these types of constructions and cross-lingual frameworks for defining MWEs. A case study in applying MWEs to NLP tasks is undertaken, with the exploration of incorporating MWE information while training Neural Machine Translation systems. Finally, the topic of automatic identification of Irish MWEs is explored, documenting the training of a system capable of automatically identifying Irish MWEs from a variety of categories, and the challenges associated with developing such a system. This research contributes towards a greater understanding of Irish MWEs and their applications in NLP, and provides a foundation for future work in exploring other methods for the automatic discovery and identification of Irish MWEs, and further developing the MWE resources described above

Semantic chunking

Long sentences pose a challenge for natural language processing (NLP) applications. They are associated with a complex information structure leading to increased requirements for processing resources. Although the issue is present in many areas of research, there is little uniformity in the solutions used by research communities dedicated to individual NLP applications. Different aspects of the problem are addressed by different tasks, such as sentence simplification or shallow chunking. The main contribution of this thesis is the introduction of the task of semantic chunking as a general approach to reducing the cost of processing long sentences. The goal of semantic chunking is to find semantically contained fragments of a sentence representation that can be processed independently and recombined without loss of information. We anchor its principles in established concepts of semantic theory, in particular event and situation semantics. Most of the experiments in this thesis focus on semantic chunking defined on complex semantic representations in Dependency Minimal Recursion Semantics (DMRS), but we also demonstrate that the task can be performed on sentence strings. We present three chunking models: a) rule-based proof-of-concept DMRS chunking system; b) a semi-supervised sequence labelling neural model for surface semantic chunking; c) a system capable of finding semantic chunk boundaries based on the inherent structure of DMRS graphs, generalisable in the form of descriptive templates. We show how semantic chunking can be applied within a divide-and-conquer processing paradigm, using as an example the task of realization from DMRS. The application of semantic chunking yields noticeable efficiency gains without decreasing the quality of results

D-TERMINE : data-driven term extraction methodologies investigated

Automatic term extraction is a task in the field of natural language processing that aims to automatically identify terminology in collections of specialised, domain-specific texts. Terminology is defined as domain-specific vocabulary and consists of both single-word terms (e.g., corpus in the field of linguistics, referring to a large collection of texts) and multi-word terms (e.g., automatic term extraction). Terminology is a crucial part of specialised communication since terms can concisely express very specific and essential information. Therefore, quickly and automatically identifying terms is useful in a wide range of contexts. Automatic term extraction can be used by language professionals to find which terms are used in a domain and how, based on a relevant corpus. It is also useful for other tasks in natural language processing, including machine translation. One of the main difficulties with term extraction, both manual and automatic, is the vague boundary between general language and terminology. When different people identify terms in the same text, it will invariably produce different results. Consequently, creating manually annotated datasets for term extraction is a costly, time- and effort- consuming task. This can hinder research on automatic term extraction, which requires gold standard data for evaluation, preferably even in multiple languages and domains, since terms are language- and domain-dependent. Moreover, supervised machine learning methodologies rely on annotated training data to automatically deduce the characteristics of terms, so this knowledge can be used to detect terms in other corpora as well. Consequently, the first part of this PhD project was dedicated to the construction and validation of a new dataset for automatic term extraction, called ACTER – Annotated Corpora for Term Extraction Research. Terms and Named Entities were manually identified with four different labels in twelve specialised corpora. The dataset contains corpora in three languages and four domains, leading to a total of more than 100k annotations, made over almost 600k tokens. It was made publicly available during a shared task we organised, in which five international teams competed to automatically extract terms from the same test data. This illustrated how ACTER can contribute towards advancing the state-of-the-art. It also revealed that there is still a lot of room for improvement, with moderate scores even for the best teams. Therefore, the second part of this dissertation was devoted to researching how supervised machine learning techniques might contribute. The traditional, hybrid approach to automatic term extraction relies on a combination of linguistic and statistical clues to detect terms. An initial list of unique candidate terms is extracted based on linguistic information (e.g., part-of-speech patterns) and this list is filtered based on statistical metrics that use frequencies to measure whether a candidate term might be relevant. The result is a ranked list of candidate terms. HAMLET – Hybrid, Adaptable Machine Learning Approach to Extract Terminology – was developed based on this traditional approach and applies machine learning to efficiently combine more information than could be used with a rule-based approach. This makes HAMLET less susceptible to typical issues like low recall on rare terms. While domain and language have a large impact on results, robust performance was reached even without domain- specific training data, and HAMLET compared favourably to a state-of-the-art rule-based system. Building on these findings, the third and final part of the project was dedicated to investigating methodologies that are even further removed from the traditional approach. Instead of starting from an initial list of unique candidate terms, potential terms were labelled immediately in the running text, in their original context. Two sequential labelling approaches were developed, evaluated and compared: a feature- based conditional random fields classifier, and a recurrent neural network with word embeddings. The latter outperformed the feature-based approach and was compared to HAMLET as well, obtaining comparable and even better results. In conclusion, this research resulted in an extensive, reusable dataset and three distinct new methodologies for automatic term extraction. The elaborate evaluations went beyond reporting scores and revealed the strengths and weaknesses of the different approaches. This identified challenges for future research, since some terms, especially ambiguous ones, remain problematic for all systems. However, overall, results were promising and the approaches were complementary, revealing great potential for new methodologies that combine multiple strategies

many faces, many places (Term21)

UIDB/03213/2020 UIDP/03213/2020Proceedings of the LREC 2022 Workshop Language Resources and Evaluation Conferencepublishersversionpublishe

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

many faces, many places (Term21)

UIDB/03213/2020 UIDP/03213/2020publishersversionpublishe