Using distributional similarity to organise biomedical terminology

We investigate an application of distributional similarity techniques to the problem of structural organisation of biomedical terminology. Our application domain is the relatively small GENIA corpus. Using terms that have been accurately marked-up by hand within the corpus, we consider the problem of automatically determining semantic proximity. Terminological units are dened for our purposes as normalised classes of individual terms. Syntactic analysis of the corpus data is carried out using the Pro3Gres parser and provides the data required to calculate distributional similarity using a variety of dierent measures. Evaluation is performed against a hand-crafted gold standard for this domain in the form of the GENIA ontology. We show that distributional similarity can be used to predict semantic type with a good degree of accuracy

Chinese-English Semantic Resource Construction

We describe an approach to large-scale construction of a semantic lexicon for Chinese verbs. We leverage off of three existing resources--a classification of English verbs called EVCA (English Verbs Classes and Alterations) [Levin, 1993], a Chinese conceptual database called HowNet [Zhendong, 1988c, Zhendong, 1988b] (http://www.how-net.com), and a large machine-readable dictionary called Optilex. The resulting lexicon is used for determining appropriate word senses in applications such as machine translation and cross-language information retrieval. (Also cross-referenced as UMIACS-TR-2000-27) (Also cross-referenced as LAMP-TR-044

Application of Weighted Voting Taggers to Languages Described with Large Tagsets

The paper presents baseline and complex part-of-speech taggers applied to the modified corpus of Frequency Dictionary of Contemporary Polish, annotated with a large tagset. First, the paper examines accuracy of 6 baseline part-of-speech taggers. The main part of the work presents simple weighted voting and complex voting taggers. Special attention is paid to lexical voting methods and issues of ties and fallbacks. TagPair and WPDV voting methods achieve the top accuracy among all considered methods. Error reduction 10.8 % with respect to the best baseline tagger for the large tagset is comparable with other author's results for small tagsets

Italian VerbNet: A Construction based Approach to Italian Verb Classification

This paper proposes a new method for Italian verb classification -and a preliminary example of resulting classes- inspired by Levin (1993) and VerbNet (Kipper-Schuler, 2005), yet partially independent from these resources; we achieved such a result by integrating Levin and VerbNet’s models of classification with other theoretic frameworks and resources. The classification is rooted in the constructionist framework (Goldberg, 1995; 2006) and is distribution-based. It is also semantically characterized by a link to FrameNet’ssemanticframesto represent the event expressed by a class. However, the new Italian classes maintain the hierarchic “tree” structure and monotonic nature of VerbNet’s classes, and, where possible, the original names (e.g.: Verbs of Killing, Verbs of Putting, etc.). We therefore propose here a taxonomy compatible with VerbNet but at the same time adapted to Italian syntax and semantics. It also addresses a number of problems intrinsic to the original classifications, such as the role of argument alternations, here regarded simply as epiphenomena, consistently with the constructionist approach

Natural language processing for similar languages, varieties, and dialects: A survey

There has been a lot of recent interest in the natural language processing (NLP) community in the computational processing of language varieties and dialects, with the aim to improve the performance of applications such as machine translation, speech recognition, and dialogue systems. Here, we attempt to survey this growing field of research, with focus on computational methods for processing similar languages, varieties, and dialects. In particular, we discuss the most important challenges when dealing with diatopic language variation, and we present some of the available datasets, the process of data collection, and the most common data collection strategies used to compile datasets for similar languages, varieties, and dialects. We further present a number of studies on computational methods developed and/or adapted for preprocessing, normalization, part-of-speech tagging, and parsing similar languages, language varieties, and dialects. Finally, we discuss relevant applications such as language and dialect identification and machine translation for closely related languages, language varieties, and dialects.Non peer reviewe

A Tour of Explicit Multilingual Semantics: Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing

The recent advent of modern pretrained language models has sparked a revolution in Natural Language Processing (NLP), especially in multilingual and cross-lingual applications. Today, such language models have become the de facto standard for providing rich input representations to neural systems, achieving unprecedented results in an increasing range of benchmarks. However, questions that often arise are: firstly, whether current language models are, indeed, able to capture explicit, symbolic meaning; secondly, if they are, to what extent; thirdly, and perhaps more importantly, whether current approaches are capable of scaling across languages. In this cutting-edge tutorial, we will review recent efforts that have aimed at shedding light on meaning in NLP, with a focus on three key open problems in lexical and sentence-level semantics: Word Sense Disambiguation, Semantic Role Labeling, and Semantic Parsing. After a brief introduction, we will spotlight how state-of-the-art models tackle these tasks in multiple languages, showing where they excel and where they fail. We hope that this tutorial will broaden the audience interested in multilingual semantics and inspire researchers to further advance the field

A review of EBMT using proportional analogies

Some years ago a number of papers reported an experimental implementation of Example Based Machine Translation (EBMT) using Proportional Analogy. This approach, a type of analogical learning, was attractive because of its simplicity; and the papers reported considerable success with the method. This paper reviews what we believe to be the totality of research reported using this method, as an introduction to our own experiments in this framework, reported in a companion paper. We report first some lack of clarity in the previously published work, and then report our findings that the purity of the proportional analogy approach imposes huge run-time complexity for the EBMT task even when heuristics as hinted at in the original literature are applied to reduce the amount of computation