Research Methodology

The methodology devised for this research can be categorized into several phases, each building upon the previous to facilitate a comprehensive exploration of semantic shifts.

Initially, for every period-specific corpus, we generate word embeddings. Employing neural network models facilitates the translation of textual data into high-dimensional vector spaces, ensuring that words are adequately represented in relation to their semantic and contextual attributes. This process yields a set of embeddings for each period, effectively capturing the diachronic nature of language.

Post embedding generation, the subsequent task is to determine which words merit representation as nodes in the graph for every period. This decision is informed by a mix of criteria: the embeddings' inherent semantics, the frequency and distribution of words in the corpora, and potential significance in tracking semantic shifts. The aim is to ensure the graph's nodes are both representative of the period and pertinent to the overarching research objectives. One foundational strategy for node inclusion in graph-based semantic modeling is determining similarity scores, like cosine similarity, between the target word and the entire vocabulary. Words that exceed a set similarity threshold can be integrated into the graph. Additionally, the principle of distributional semantics suggests that words sharing contextual proximity often possess semantic congruence. Thus, including words that frequently co-occur within a specific window size of the target word enriches the semantic landscape. Furthermore, unsupervised graph clustering algorithms applied on word embeddings can delineate clusters of semantically related words, guiding node selection. Knowledge bases, such as WordNet or ConceptNet, offer repositories of predefined semantic relationships, allowing for the inclusion of semantically linked words to the target. Meanwhile, metrics like Point Mutual Information (PMI) can gauge the strength of word associations, emphasizing words that share strong contextual intersections with the target. Techniques such as topological data analysis emphasize structural semantics, while diversity sampling ensures a panoramic view of the semantic space. An iterative refinement based on initial outcomes, coupled with domain expert insights, further optimizes the graph's semantic representation.

Edges in our graph are designed to represent the relationships between words (nodes). For each period, the edge features are constructed based on the cosine similarity or point-mutual-information between word embeddings, providing a metric of semantic closeness. Additionally, we categorize these relationships into various edge types, representing relationships such as synonymy, collocation, context words, or other semantic associations. Edge weights are then assigned, quantifying the strength of these relationships.

With the graph structured, we proceed to model the temporal graph data. For this, we adopt a Dynamic Graph Neural Network infused with temporal signals, utilizing the PyTorch Geometric library. This state-of-the-art tool allows for the intricate handling of time-evolving graph data, accommodating the diachronic nuances of our dataset.

Our inferential approach for semantic shifts spans several methodologies. Primarily, we predict future edge feature values between nodes, drawing parallels from methods employed in traffic forecasting tasks. This enables a projection into potential future linguistic trends, capturing shifts in word sense usage. Concurrently, we deploy time series anomaly detection, ensuring that any sudden or unprecedented semantic shifts are promptly flagged. Link prediction in graphs supplements this by highlighting how semantic relationships between words may evolve or transform over time. Lastly, for those periods where a threshold criteria is applied to streamline the graph, analyzing nodes that either emerge or fade becomes crucial. This offers insights into the entrance or exit of particular word meanings in the lexicon.

In addition to the above methods, it might be beneficial to integrate community detection within the graph. This could identify clusters of words that share semantic trajectories, potentially unearthing broader linguistic themes or trends.

In summary, this rigorous methodology, characterized by its depth and breadth, aspires to meticulously chart the intricate dynamics of semantic shifts across time, offering invaluable insights into the evolution of language and meaning.