A company’s GICS sector can be inferred from what the firm writes about itself, and it can be inferred from who chooses to write about the firm. We show these are the same inference over two event spaces, and fit one multinomial naïve Bayes to both. In the first a company is a bag of the words of Item 1 of its 10-K; in the second it is a bag of the 814 analysts who covered it, with no use of what they wrote. Sector prototypes are counted log-probabilities in both cases, w_s = log p_s , so the two differ only in what is being counted.

Over 426 S P 500 constituents the word space gives 83.7% top-1 recovery and the analyst space 73.7%, against a 16.2% majority-class baseline. Because the two spaces are conditionally independent given the sector their log-likelihoods simply add – but doing so captures only a quarter of the available gain (+0.75 points). A filing supplies some 2,100 wordevents against 18 analyst events at the median, and naïve Bayes counts each as independent, so the word log-likelihood varies across the eleven sectors with a median standard deviation of 766 nats against 8.3 for the analyst log-likelihood, a factor of 92: the sum is numerically dominated and analyst evidence rarely moves a decision. Standardising each log-likelihood within a company before adding removes the scale disparity and recovers +2.9 points [0.4, 5.4], reaching 86.5% top-1 and 94.8% top-2.

A dense encoder is not better here, and not combinable on the same terms. Qwen3-Embedding-4B over the same filings reaches 76.9% under the nearest-centroid classifier that dense vectors admit, below naïve Bayes over the same words; and having no event counts it cannot form a third event space, so combining it means leaving the likelihood and choosing a scale.

In this work, we tested the Triplet Extraction (TE) capabilities of a variety of Large Language Models (LLMs) of different sizes in the Zero- and Few-Shots settings. In detail, we proposed a pipeline that dynamically gathers contextual information from a Knowledge Base (KB), both in the form of context triplets and of (sentence, triplets) pairs as examples, and provides it to the LLM through a prompt. The additional context allowed the LLMs to be competitive with all the older fully trained baselines based on the Bidirectional Long Short-Term Memory (BiLSTM) Network architecture. We further conducted a detailed analysis of the quality of the gathered KB context, finding it to be strongly correlated with the final TE performance of the model. In contrast, the size of the model appeared to only logarithmically improve the TE capabilities of the LLMs. We release the code on GitHub for reproducibility.

Lexical substitution, which aims to generate substitutes for a target word given a context, is an important natural language processing task useful in many applications. Due to the paucity of annotated data, existing methods for lexical substitution tend to rely on manually curated lexical resources and contextual word embedding models. Methods based on lexical resources are likely to miss relevant substitutes whereas relying only on contextual word embedding models fails to provide adequate information on the impact of a substitute in the entire context and the overall meaning of the input. We proposed CILex, which uses contextual sentence embeddings along with methods that capture additional context information complimenting contextual word embeddings for lexical substitution. This ensured the semantic consistency of a substitute with the target word while maintaining the overall meaning of the sentence. Our experimental comparisons with previously proposed methods indicated that our solution is now the state-of-the-art on both the widely used LS07 and CoInCo datasets with P@1 scores of 55.96% and 57.25% for lexical substitution. The implementation of the proposed approach is available at https://github.com/sandaruSen/CILex under the MIT license.

Acronym disambiguation (AD) is the process of identifying the correct expansion of the acronyms in text. AD is crucial in natural language understanding of scientific and medical documents due to the high prevalence of technical acronyms and the possible expansions. Given that natural language is often ambiguous with more than one meaning for words, identifying the correct expansion for acronyms requires learning of effective representations for words, phrases, acronyms, and abbreviations based on their context. In this paper, we proposed an approach to leverage the triplet networks and triplet loss which learns better representations of text through distance comparisons of embeddings. We tested both the triplet network-based method and the modified triplet network-based method with m networks on the AD dataset from the SDU@AAAI-21 AD task, CASI dataset, and MeDAL dataset. F scores of 87.31%, 70.67%, and 75.75% were achieved by the m network-based approach for SDU, CASI, and MeDAL datasets respectively indicating that triplet network-based methods have comparable performance but with only 12% of the number of parameters in the baseline method. This effective implementation is available at https://github.com/sandaruSen/m_networks under the MIT license.

In this work we put forward to combine pretrained knowledge base graph embeddings with transformer based language models to improve performance on the sentential Relation Extraction task in natural language processing. Our proposed model is based on a simple variation of existing models to incorporate off-task pretrained graph embeddings with an on-task finetuned BERT encoder. We perform a detailed statistical evaluation of the model on standard datasets. We provide evidence that the added graph embeddings improve the performance, making such a simple approach competitive with the state-of-the-art models that perform explicit on-task training of the graph embeddings. Furthermore, we ob- serve for the underlying BERT model an interesting power-law scaling behavior between the variance of the F1 score obtained for a relation class and its support in terms of training examples.