A Multilingual Phonological Resource Toolkit for Ubiquitous Speech Technology

This paper outlines the generation process of a specifi computational linguistic representation termed the Multilingual Time Map, conceptually a multi-tape finit state transducer encoding linguistic data at different levels of granularity. The fi st component acquires phonological data from syllable labeled speech data, the second component define feature profiles the third component generates feature hierarchies and augments the acquired data with the define feature profiles and the fourth component displays the Multilingual Time Map as a graph

Dual Route Model of Idiom Processing in the Bilingual Context

The dual route model predicts that idiomatic phrases show a processing advantage over matched novel phrases. This model postulates that familiar phrases are processed by a faster direct route, and novel phrases are processed by an indirect route. This thesis investigated the role of familiar form and concept in direct route activation. Study 1 provided norming evidence for experimental stimuli selection. Study 2 examined whether direct route can be activated for translated Chinese idioms in Chinese-English bilinguals. Bilinguals listened to the idiom up until the last word (e.g., draw a snake and add), then saw either the idiom ending (e.g., feet) or the matched control ending (e.g., hair); to which they made lexical decision and reaction times were recorded. Results showed evidence for dual route model and provided preliminary support for both familiar concept and lexical association as drivers of direct route activation

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Indonesian and Malay are underrepresented in the development of natural language processing (NLP) technologies and available resources are difficult to find. A clear picture of existing work can invigorate and inform how researchers conceptualise worthwhile projects. Using an education sector project to motivate the study, we conducted a wide-ranging overview of Indonesian and Malay human language technologies and corpus work. We charted 657 included studies according to Hirschberg and Manning's 2015 description of NLP, concluding that the field was dominated by exploratory corpus work, machine reading of text gathered from the Internet, and sentiment analysis. In this paper, we identify most published authors and research hubs, and make a number of recommendations to encourage future collaboration and efficiency within NLP in Indonesian and Malay

Semantic adaptability for the systems interoperability

In the current global and competitive business context, it is essential that enterprises adapt their knowledge resources in order to smoothly interact and collaborate with others. However, due to the existent multiculturalism of people and enterprises, there are different representation views of business processes or products, even inside a same domain. Consequently, one of the main problems found in the interoperability between enterprise systems and applications is related to semantics. The integration and sharing of enterprises knowledge to build a common lexicon, plays an important role to the semantic adaptability of the information systems. The author proposes a framework to support the development of systems to manage dynamic semantic adaptability resolution. It allows different organisations to participate in a common knowledge base building, letting at the same time maintain their own views of the domain, without compromising the integration between them. Thus, systems are able to be aware of new knowledge, and have the capacity to learn from it and to manage its semantic interoperability in a dynamic and adaptable way. The author endorses the vision that in the near future, the semantic adaptability skills of the enterprise systems will be the booster to enterprises collaboration and the appearance of new business opportunities

The Distributional Learning of Multi-Word Expressions: A Computational Approach

There has been much recent research in corpus and computational linguistics on distributional learning algorithms—computer code that induces latent linguistic structures in corpus data based on co-occurrences of transcribed units in that data. These algorithms have varied applications, from the investigation of human cognitive processes to the corpus extraction of relevant linguistic structures for lexicographic, second language learning, or natural language processing applications, among others. They also operate at various levels of linguistic structure, from phonetics to syntax. One area of research on distributional learning algorithms in which there remains relatively little work is the learning of multi-word, memorized, formulaic sequences, based on the co-occurrences of words. Examples of such multi-word expressions (MWEs) include kick the bucket, New York City, sit down, and as a matter of fact. In this dissertation, I present a novel computational approach to the distributional learning of such sequences in corpora. Entitled MERGE (Multi-word Expressions from the Recursive Grouping of Elements), my algorithm iteratively works by (1) assigning a statistical ‘attraction’ score to each two-word sequence (bigram) in a corpus, based on the individual and co-occurrence frequencies of these two words in that corpus; and (2) merging the highest-scoring bigram into a single, lexicalized unit. These two steps then repeat until some maximum number of iterations or minimum score threshold is reached (since, broadly speaking, the winning score progressively decreases with increasing iterations). Because one (or both) of the ‘words’ making up a winning bigram may be an output merged item from a previous iteration, the algorithm is able to learn MWEs that are in principle of any length (e.g., apple pie versus I’ll believe it when I see it). Moreover, these MWEs may contain one or more discontinuities of different sizes, up to some maximum size threshold (measured in words) specified by the user (e.g., as _ as in as tall as and as big as). Typically, the extraction of MWEs has been handled by algorithms that identify only continuous sequences, and in which the user must specify the length(s) of the sequences to be extracted beforehand; thus, MERGE offers a bottom-up, distributional-based approach that addresses these issues.In the present dissertation, in addition to describing the algorithm, I report three rating experiments and one corpus-based early child language study that validate the efficacy of MERGE in identifying MWEs. In one experiment, participants rate sequences extracted from a corpus by the algorithm for how well they instantiate true MWEs. As expected, the results reveal that the high-scoring output items that MERGE identifies early in its iterative process are rated as ‘good’ MWEs by participants (based on certain subjective criteria), with the quality of these ratings decreasing for output from later iterations (i.e., output items that were scored lower by the algorithm). In the other two experiments, participants rate high-ranking output both from MERGE and from an existing algorithm from the literature that also learns MWEs of various lengths—the Adjusted Frequency List (Brook O’Donnell 2011). Comparison of participant ratings reveals that the items that MERGE acquires are rated more highly than those acquired by the Adjusted Frequency List, suggesting that MERGE is a performance frontrunner among distributional learning algorithms of MWEs. More broadly, together the experiments suggest that MERGE acquires representations that are compatible with adult knowledge of formulaic language, and thus it may be useful for any number of research applications that rely on such formulaic language as a unit of analysis.Finally, in a study using two corpora of caregiver-child interactions, I run MERGE on caregiver utterances and then show that, of the MWEs induced by the algorithm, those that go on to be later acquired by the children receive higher scores by the algorithm than those that do not go on to be learned. These results suggest that, when applied to acquisition data, the algorithm is useful for identifying the structures of statistical co-occurrences in the caregiver input that are relevant to children in their acquisition of early multi-word knowledge.Overall, MERGE is shown to be a powerful computational approach to the distributional learning and extraction of MWEs, both when modeling adult knowledge of formulaic language, and when accounting for the early multi-word structures acquired by children

Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021

The eighth edition of the Italian Conference on Computational Linguistics (CLiC-it 2021) was held at Università degli Studi di Milano-Bicocca from 26th to 28th January 2022. After the edition of 2020, which was held in fully virtual mode due to the health emergency related to Covid-19, CLiC-it 2021 represented the first moment for the Italian research community of Computational Linguistics to meet in person after more than one year of full/partial lockdown

First International Workshop on Lexical Resources

International audienceLexical resources are one of the main sources of linguistic information for research and applications in Natural Language Processing and related fields. In recent years advances have been achieved in both symbolic aspects of lexical resource development (lexical formalisms, rule-based tools) and statistical techniques for the acquisition and enrichment of lexical resources, both monolingual and multilingual. The latter have allowed for faster development of large-scale morphological, syntactic and/or semantic resources, for widely-used as well as resource-scarce languages. Moreover, the notion of dynamic lexicon is used increasingly for taking into account the fact that the lexicon undergoes a permanent evolution.This workshop aims at sketching a large picture of the state of the art in the domain of lexical resource modeling and development. It is also dedicated to research on the application of lexical resources for improving corpus-based studies and language processing tools, both in NLP and in other language-related fields, such as linguistics, translation studies, and didactics