In this paper, we aim to introduce a Cognitive Linguistics perspective into a computational analysis of near-synonyms. We focus on a single set of Dutch near-synonyms, vernielen and vernietigen, roughly translated as ‘to destroy’, replicating the analysis from Geeraerts (1997) with distributional models. Our analysis, which tracks the meaning of both words in a corpus of 16th-20th century prose data, shows that both lexical items have undergone semantic change, led by differences in their prototypical semantic core.

The automatic processing of clinical documents, such as Electronic Health Records (EHRs), could benefit substantially from the enrichment of medical terminologies with terms encountered in clinical practice. To integrate such terms into existing knowledge sources, they must be linked to corresponding concepts. We present a method for the semantic categorization of clinical terms based on their surface form. We find that features based on sublanguage properties can provide valuable cues for the classification of term variants.

We conduct a corpus study to investigate the structure of multi-word expressions (MWEs) in the clinical domain. Based on an existing medical taxonomy, we develop an annotation scheme and label a sample of MWEs from a Dutch corpus with semantic and grammatical features. The analysis of the annotated data shows that the formal structure of clinical MWEs correlates with their conceptual properties. The insights gained from this study could inform the design of Natural Language Processing (NLP) systems for clinical writing, but also for other specialized genres.

Increasingly, large bilingual document collections are being made available online, especially in the legal domain. This type of Big Data is a valuable resource that specialized translators exploit to search for informative examples of how domain-specific expressions should be translated. However, general purpose search engines are not optimized to retrieve previous translations that are maximally relevant to a translator. In this paper, we report on the TermWise project, a cooperation of terminologists, corpus linguists and computer scientists, that aims to leverage big online translation data for terminological support to legal translators at the Belgian Federal Ministry of Justice. The project developed dedicated knowledge extraction algorithms and a server-based tool to provide translators with the most relevant previous translations of domain-specific expressions relative to the current translation assignment. The functionality is implemented an extra database, a Term&Phrase Memory, that is meant to be integrated with existing Computer Assisted Translation tools. In the paper, we give an overview of the system, give a demo of the user interface, we present a user-based evaluation by translators and discuss how the tool is part of the general evolution towards exploiting Big Data in translation.

Semantic similarity is a key issue in many computational tasks. This paper goes into the development and evaluation of two common ways of automatically calculating the semantic similarity between two words. On the one hand, such methods may depend on a manually constructed thesaurus like (Euro)WordNet. Their performance is often evaluated on the basis of a very restricted set of human similarity ratings. On the other hand, corpus-based methods rely on the distribution of two words in a corpus to determine their similarity. Their performance is generally quantified through a comparison with the judgements of the first type of approach. This paper introduces a new Gold Standard of more than 5,000 human intra-category similarity judgements. We show that corpus-based methods often outperform (Euro)WordNet on this data set, and that the use of the latter as a Gold Standard for the former, is thus often far from ideal.

Vector-based models of lexical semantics retrieve semantically related words automatically from large corpora by exploiting the property that words with a similar meaning tend to occur in similar contexts. Despite their increasing popularity, it is unclear which kind of semantic similarity they actually capture and for which kind of words. In this paper, we use three vector-based models to retrieve semantically related words for a set of Dutch nouns and we analyse whether three linguistic properties of the nouns influence the results. In particular, we compare results from a dependency-based model with those from a 1st and 2nd order bag-of-words model and we examine the effect of the nouns’ frequency, semantic speficity and semantic class. We find that all three models find more synonyms for high-frequency nouns and those belonging to abstract semantic classses. Semantic specificty does not have a clear influence.