We present larger-scale evidence overturning previous results, showing that among the many alternative phrasal lexical similarity measures based on word vectors, the Jaccard coefficient most increases the robustness of MEANT, the recently introduced, fully-automatic, state-of-the-art semantic MT evaluation metric. MEANT critically depends on phrasal lexical similarity scores in order to automat...

Semantic similarity of text plays an important role in many NLP tasks. It requires using both local information like lexical semantics and structural information like syntactic structures. Recent progress in word representation provides good resources for lexical semantics, and advances in natural language analysis tools make it possible to efficiently generate syntactic and semantic annotation...

Motivated by theories of early language development in children we investigate the contribution of affective features to early acquisition of lexical semantics. For the task of semantic similarity between words, semantic and affective spaces are modeled using network-based distributed semantic models. We propose a method for constructing semantic activations from a combination of lexical and af...

We propose a range of deep lexical acquisition methods which make use of morphological, syntactic and ontological language resources to model word similarity and bootstrap from a seed lexicon. The different methods are deployed in learning lexical items for a precision grammar, and shown to each have strengths and weaknesses over different word classes. A particular focus of this paper is the r...

Learning a new L2/L3 will undoubtedly be affected by the L1/L2 knowledge of the language learners. The present study examined the lexical interplay among L1, L2 and L3. Two different types of L3 (German and Japanese) were used to evaluate how the bilingual learners’ knowledge of L1 (Chinese) and L2 (English) would impact on the learning of L3 in terms of their different types of lexical relatio...

We propose a method for computing semantic relatedness between words or texts by using knowledge from hypertext encyclopedias such as Wikipedia. A network of concepts is built by filtering the encyclopedia’s articles, each concept corresponding to an article. Two types of weighted links between concepts are considered: one based on hyperlinks between the texts of the articles, and another one b...

We present an unsupervised method to find lexical variations in Roman Urdu informal text. Our method includes a phonetic algorithm UrduPhone, a featurebased similarity function, and a clustering algorithm Lex-C. UrduPhone encodes roman Urdu strings to their phonetic equivalent representations. This produces an initial grouping of different spelling variations of a word. The similarity function ...

Machine Translation Evaluation Metrics (MTEMs) are the central core of Machine Translation (MT) engines as they are developed based on frequent evaluation. Although MTEMs are widespread today, their validity and quality for many languages is still under question. The aim of this research study was to examine the validity and assess the quality of MTEMs from Lexical Similarity set on machine tra...