Evaluating Semantic Vectors for Norwegian

In this article, we present two benchmark data sets for evaluating models of semantic word similarity for Norwegian. While such resources are available for English, they did not exist for Norwegian prior to this work. Furthermore, we produce large-coverage semantic vectors trained on the Norwegian Newspaper Corpus using several popular word embedding frameworks. Finally, we demonstrate the usefulness of the created resources for evaluating performance of different word embedding models on the tasks of analogical reasoning and synonym detection. The benchmark data sets and word embeddings are all made freely available

#REVAL: a semantic evaluation framework for hashtag recommendation

Automatic evaluation of hashtag recommendation models is a fundamental task in many online social network systems. In the traditional evaluation method, the recommended hashtags from an algorithm are firstly compared with the ground truth hashtags for exact correspondences. The number of exact matches is then used to calculate the hit rate, hit ratio, precision, recall, or F1-score. This way of evaluating hashtag similarities is inadequate as it ignores the semantic correlation between the recommended and ground truth hashtags. To tackle this problem, we propose a novel semantic evaluation framework for hashtag recommendation, called #REval. This framework includes an internal module referred to as BERTag, which automatically learns the hashtag embeddings. We investigate on how the #REval framework performs under different word embedding methods and different numbers of synonyms and hashtags in the recommendation using our proposed #REval-hit-ratio measure. Our experiments of the proposed framework on three large datasets show that #REval gave more meaningful hashtag synonyms for hashtag recommendation evaluation. Our analysis also highlights the sensitivity of the framework to the word embedding technique, with #REval based on BERTag more superior over #REval based on FastText and Word2Vec.Comment: 18 pages, 4 figure

Classifying Relations using Recurrent Neural Network with Ontological-Concept Embedding

Relation extraction and classification represents a fundamental and challenging aspect of Natural Language Processing (NLP) research which depends on other tasks such as entity detection and word sense disambiguation. Traditional relation extraction methods based on pattern-matching using regular expressions grammars and lexico-syntactic pattern rules suffer from several drawbacks including the labor involved in handcrafting and maintaining large number of rules that are difficult to reuse. Current research has focused on using Neural Networks to help improve the accuracy of relation extraction tasks using a specific type of Recurrent Neural Network (RNN). A promising approach for relation classification uses an RNN that incorporates an ontology-based concept embedding layer in addition to word embeddings. This dissertation presents several improvements to this approach by addressing its main limitations. First, several different types of semantic relationships between concepts are incorporated into the model; prior work has only considered is-a hierarchical relationships. Secondly, a significantly larger vocabulary of concepts is used. Thirdly, an improved method for concept matching was devised. The results of adding these improvements to two state-of-the-art baseline models demonstrated an improvement to accuracy when evaluated on benchmark data used in prior studies

Semantic similarity and analysis of the word frequency dynamics

© Published under licence by IOP Publishing Ltd. In this study a similarity in changes of frequencies dynamics for semantically related words was analyzed using word statistics extracted from more than 4.5 million books written over a period of 205 years. The approach is based on the correlation analysis of 1-grams frequency dynamics. We analyzed the frequencies correlation of synonym pairs, their corresponding antonymous groups and random words pairs. Also, we compared several metrics to find the most effective for assessing the degree of similarity in the dynamics of use of different words. Comparing differences between logarithmic rank variations in pairs of synonyms and random word pairs, significant differences are found, though they are smaller than it could be expected

Linear mappings: semantic transfer from transformer models for cognate detection and coreference resolution

Includes bibliographical references.2022 Fall.Embeddings or vector representations of language and their properties are useful for understanding how Natural Language Processing technology works. The usefulness of embeddings, however, depends on how contextualized or information-rich such embeddings are. In this work, I apply a novel affine (linear) mapping technique first established in the field of computer vision to embeddings generated from large Transformer-based language models. In particular, I study its use in two challenging linguistic tasks: cross-lingual cognate detection and cross-document coreference resolution. Cognate detection for two Low-Resource Languages (LRL), Assamese and Bengali, is framed as a binary classification problem using semantic (embedding-based), articulatory, and phonetic features. Linear maps for this task are extrinsically evaluated on the extent of transfer of semantic information between monolingual as well as multi-lingual models including those specialized for low-resourced Indian languages. For cross-document coreference resolution, whole-document contextual representations are generated for event and entity mentions from cross- document language models like CDLM and other BERT-variants and then linearly mapped to form coreferring clusters based on their cosine similarities. I evaluate my results on gold output based on established coreference metrics like BCUB and MUC. My findings reveal that linearly transforming vectors from one model's embedding space to another carries certain semantic information with high fidelity thereby revealing the existence of a canonical embedding space and its geometric properties for language models. Interestingly, even for a much more challenging task like coreference resolution, linear maps are able to transfer semantic information between "lighter" models or less contextual models and "larger" models with near-equivalent performance or even improved results in some cases