This paper deals with the identification of Multiword Expressions (MWEs) in Manipuri, a highly agglutinative Indian Language. Manipuri is listed in the Eight Schedule of Indian Constitution. MWE plays an important role in the applications of Natural Language Processing(NLP) like Machine Translation, Part of Speech tagging, Information Retrieval, Question Answering etc. Feature selection is an important factor in the recognition of Manipuri MWEs using Conditional Random Field (CRF). The disadvantage of manual selection and choosing of the appropriate features for running CRF motivates us to think of Genetic Algorithm (GA). Using GA we are able to find the optimal features to run the CRF. We have tried with fifty generations in feature selection along with three fold cross validation as fitness function. This model demonstrated the Recall (R) of 64.08%, Precision (P) of 86.84% and F-measure (F) of 73.74%, showing an improvement over the CRF based Manipuri MWE identification without GA application.
This paper gives a detail overview about the modified features selection in CRF (Conditional Random Field) based Manipuri POS (Part of Speech) tagging. Selection of features is so important in CRF that the better are the features then the better are the outputs. This work is an attempt or an experiment to make the previous work more efficient. Multiple new features are tried to run the CRF and again tried with the Reduplicated Multiword Expression (RMWE) as another feature. The CRF run with RMWE because Manipuri is rich of RMWE and identification of RMWE becomes one of the necessities to bring…
University of Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；INRIA（法国国家信息与自动化研究所）；Loria（洛林计算机科学及其应用实验室）
The task of automatically detecting hate speech in social media is gaining more and more attention. Given the enormous volume of content posted daily, human monitoring of hate speech is unfeasible. In this work, we propose new word-level features for automatic hate speech detection (HSD): multiword expressions (MWEs). MWEs are lexical units greater than a word that have idiomatic and compositional meanings. We propose to integrate MWE features in a deep neural network-based HSD framework. Our baseline HSD system relies on Universal Sentence Encoder (USE). To incorporate MWE features, we create…
University of Cambridge（剑桥大学）；Manchester Metropolitan University（曼彻斯特城市大学）
Multiword expressions (MWEs) represent lexemes that should be treated as single lexical units due to their idiosyncratic nature. Multiple NLP applications have been shown to benefit from MWE identification, however the research on lexical complexity of MWEs is still an under-explored area. In this work, we re-annotate the Complex Word Identification Shared Task 2018 dataset of Yimam et al. (2017), which provides complexity scores for a range of lexemes, with the types of MWEs. We release the MWE-annotated dataset with this paper, and we believe this dataset represents a valuable resource for t…
DECO-MWE: building a linguistic resource of Korean multiword expressions for feature-based sentiment analysis
DICORA, Department of Linguistics and Cognitive Science, Hankuk University of Foreign Studies, Korea（DICORA，语言学与认知科学系，韩国民法大学，韩国）；Université Paris-Est, LIGM, CNRS, UPEM, ESIEE, ENPC, France（巴黎-est大学，LIGM，法国国家科学研究中心，UPEM，ESIEE，ENPC，法国）
Multiword expressions (MWEs) present groups of words in which the meaning of the whole is not derived from the meaning of its parts. The task of processing MWEs is crucial in many natural language processing (NLP) applications, including machine translation and terminology extraction. Therefore, detecting MWEs is a popular research theme. In this paper, we explore state-of-the-art neural transformers in the task of detecting MWEs.We empirically evaluate several transformer models in the dataset for SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM). We show that…
University of Wolverhampton（伍尔弗汉普顿大学）；University of Granada（格拉纳达大学）
Multiword expression (MWE) is a sequence of words which collectively present a meaning which is not derived from its individual words. The task of processing MWEs is crucial in many natural language processing (NLP) applications, including machine translation and terminology extraction. Therefore, detecting MWEs in different domains is an important research topic. In this paper, we explore state-of-the-art neural transformers in the task of detecting MWEs in flower and plant names. We evaluate different transformer models on a dataset created from Encyclopedia of Plants and Flower. We empirica…
Central Calcutta Polytechnic（加尔各答中央理工学院）；Jadavpur University（贾达普大学）
This paper presents a machine learning approach for identification of Bengali multiword expressions (MWE) which are bigram nominal compounds. Our proposed approach has two steps: (1) candidate extraction using chunk information and various heuristic rules and (2) training the machine learning algorithm called Random Forest to classify the candidates into two groups: bigram nominal compound MWE or not bigram nominal compound MWE. A variety of association measures, syntactic and linguistic clues and a set of WordNet-based similarity features have been used for our MWE identification task. The ap…
University of Tours（图尔大学）；Heinrich-Heine-Universität Düsseldorf（杜塞尔多夫海因里希·海涅大学）；University of Tübingen（图宾根大学）
Multiword expressions (MWEs) exhibit both regular and idiosyncratic properties. Their idiosyncrasy requires lexical encoding in parallel with their component words. Their (at times intricate) regularity, on the other hand, calls for means of flexible factorization to avoid redundant descriptions of shared properties. However, so far, non-redundant general-purpose lexical encoding of MWEs has not received a satisfactory solution. We offer a proof of concept that this challenge might be effectively addressed within eXtensible MetaGrammar (XMG), an object-oriented metagrammar framework. We first …
Multilingual Multiword Expression Identification Using Lateral Inhibition and Domain Adaptation
University Politehnica of Bucharest（布加勒斯特理工大学）；Research Institute for Artificial Intelligence “Mihai Drăgănescu”, Romanian Academy（罗马尼亚科学院“米哈伊·德拉格内斯库”人工智能研究所）
This paper presents a language-independent deep learning architecture adapted to the task of multiword expression (MWE) identification. We employ a neural architecture comprising of convolutional and recurrent layers with the addition of an optional CRF layer at the top. This system participated in the open track of the Parseme shared task on automatic identification of verbal MWEs due to the use of pre-trained wikipedia word embeddings. It outperformed all participating systems in both open and closed tracks with the overall macro-average MWE-based F1 score of 58.09 averaged among all languag…
Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）；National Institute of Technology, Meghalaya（梅加拉亚国立理工学院）；Jadavpur University（贾达普大学）
One of the key issues in both natural language understanding and generation is the appropriate processing of Multiword Expressions (MWEs). MWEs pose a huge problem to the precise language processing due to their idiosyncratic nature and diversity in lexical, syntactical and semantic properties. The semantics of a MWE cannot be expressed after combining the semantics of its constituents. Therefore, the formalism of semantic clustering is often viewed as an instrument for extracting MWEs especially for resource constraint languages like Bengali. The present semantic clustering approach contribut…
University of Wolverhampton（伍尔弗汉普顿大学）；University of Oxford（牛津大学）
We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture. We specifically target discontinuity, an under-explored aspect that poses a significant challenge to computational treatment of MWEs. Two neural architectures are explored: Graph Convolutional Network (GCN) and multi-head self-attention. GCN leverages dependency parse information, and self-attention attends to long-range relations. We finally propose a combined model that integrates complementary information from both through a gating mechanism. Th…
This thesis presents a study about the integration of information about Multiword Expressions (MWEs) into parsing with Combinatory Categorial Grammar (CCG). We build on previous work which has shown the benefit of adding information about MWEs to syntactic parsing by implementing a similar pipeline with CCG parsing. More specifically, we collapse MWEs to one token in training and test data in CCGbank, a corpus which contains sentences annotated with CCG derivations. Our collapsing algorithm however can only deal with MWEs when they form a constituent in the data which is one of the limitations…
This paper describes an approach to detect idiomaticity only from the contextualized representation of a MWE over multilingual pretrained language models. Our experiments find that larger models are usually more effective in idiomaticity detection. However, using a higher layer of the model may not guarantee a better performance. In multilingual scenarios, the convergence of different languages are not consistent and rich-resource languages have big advantages over other languages.
University of Tours（图尔大学）；Aix Marseille Univ（艾克斯-马赛大学）；Université de Toulon（土伦大学）；CNRS（法国国家科学研究中心）；LIS（计算机与系统实验室）
Automatic identification of mutiword expressions (MWEs) is a pre-requisite for semantically-oriented downstream applications. This task is challenging because MWEs, especially verbal ones (VMWEs), exhibit surface variability. However, this variability is usually more restricted than in regular (non-VMWE) constructions, which leads to various variability profiles. We use this fact to determine the optimal set of features which could be used in a supervised classification setting to solve a subproblem of VMWE identification: the identification of occurrences of previously seen VMWEs. Surprisingl…
An Empirical Study on Chinese Character Decomposition in Multiword Expression-Aware Neural Machine Translation
Leiden University Medical Center（莱顿大学医学中心）；Leiden University（莱顿大学）；ADAPT Research Centre（ADAPT研究中心）；Dublin City University（都柏林城市大学）；Insight Centre for Data Analytics（洞察数据分析中心）
This work presents a fine-grained, text-chunking algorithm designed for the task of multiword expressions (MWEs) segmentation. As a lexical class, MWEs include a wide variety of idioms, whose automatic identification are a necessity for the handling of colloquial language. This algorithm's core novelty is its use of non-word tokens, i.e., boundaries, in a bottom-up strategy. Leveraging boundaries refines token-level information, forging high-level performance from relatively basic data. The generality of this model's feature space allows for its application across languages and domains. Experi…
University of Marne-la-Vallée（马恩河谷大学）；University of Lisbon（里斯本大学）
We report experiments about the syntactic variations of support verb constructions, a special type of multiword expressions (MWEs) containing predicative nouns. In these expressions, the noun can occur with or without the verb, with no clear-cut semantic difference. We extracted from a large French corpus a set of examples of the two situations and derived statistical results from these data. The extraction involved large-coverage language resources and finite-state techniques. The results show that, most frequently, predicative nouns occur without a support verb. This fact has consequences on…
Despite their success in a variety of NLP tasks, pre-trained language models, due to their heavy reliance on compositionality, fail in effectively capturing the meanings of multiword expressions (MWEs), especially idioms. Therefore, datasets and methods to improve the representation of MWEs are urgently needed. Existing datasets are limited to providing the degree of idiomaticity of expressions along with the literal and, where applicable, (a single) non-literal interpretation of MWEs. This work presents a novel dataset of naturally occurring sentences containing MWEs manually classified into …
Academia Sinica（中央研究院）；National Chengchi University（国立政治大学）；Yuan-Ze University（元智大学）
Financial statements contain quantitative information and manager's subjective evaluation of firm's financial status. Using information released in U.S. 10-K filings. Both qualitative and quantitative appraisals are crucial for quality financial decisions. To extract such opinioned statements from the reports, we built tagging models based on the conditional random field (CRF) techniques, considering a variety of combinations of linguistic factors including morphology, orthography, predicate-argument structure, syntax, and simple semantics. Our results show that the CRF models are reasonably e…
