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Problem Statement

The evolution of computational linguistics has seen significant strides in embedding techniques, promising nuanced representations of words in vector spaces. However, the aggregation of these embeddings, particularly in the realm of contextual embeddings, has been a point of contention.

Firstly, the very act of aggregation, whether it be through averaging mechanisms or clustering methodologies, carries an inherent risk of information loss. Each instance of a word in textual data is rooted in a specific context, influenced by myriad factors ranging from syntactic structures to broader discursive themes. Aggregating embeddings attempts to distill this richness into a singular or limited set of representations, often sidelining the less frequent or less predictable nuances.

Furthermore, aggregation is underpinned by a slew of assumptions. It presumes a certain homogeneity within the aggregated data points, often prioritizing dominant senses at the expense of overshadowing emergent or peripheral meanings. The granularity and intricacy of language, where words can adopt varied shades of meanings based on contexts, temporalities, and audiences, challenges the very foundational premise of aggregation.

The assertion in 1 is particularly illuminating in this debate. Kilgariff delineates the difference between 'lumping' and 'splitting' when defining word senses. The act of lumping seeks to group together instances based on perceived similarities, often influenced by factors like frequency and predictability. In contrast, splitting allows for the recognition of distinctions, emphasizing the heterogeneity of word usages. Aggregation, by its nature, leans towards the lumping paradigm. While it offers the advantage of simplification, it does so at the cost of potentially overlooking the intricate mosaic of semantic landscapes. A telling example from Kilgarriff's research elucidates this concern. In 1, the sense of the word 'handbag' is explored, highlighting an unconventional interpretation: handbag-as-weapon. This specific sense, though valid and meaningful in certain contexts, remains absent from many conventional dictionaries. Such an omission can largely be attributed to the infrequent deployment of 'handbag' in this particular sense. This example underscores the pitfalls of aggregation and the perils of biasing towards frequency---it demonstrates how meaningful, albeit less common, interpretations can be easily overlooked. The term run exemplifies the complexities inherent in semantic predictability. Predominantly associated with locomotion, run encompasses an array of interpretations, from operating machinery to temporal lateness. However, in the realm of journalism, run acquires a specialized nuance, denoting the publication of a story. This specific interpretation, although contextually significant, might not be immediately discernible based on the term's more ubiquitous senses. Models predisposed to dominant patterns could thus misapprehend or overlook this journalistic context, underscoring the challenges of predictability. Essentially, a sense's predictability, or lack thereof, based on prevalent uses does not diminish its validity, highlighting the intricacies of semantic modeling.

These challenges can be addressed in practical endeavors by establishing task-dependent foundations for the aggregation process. Depending on the specific task, the aggregation can be tailored to prioritize certain senses, underpinning the rationale for such grouping or clustering. However, this approach also presents drawbacks. Its inherent subjectivity means that outcomes are not solely dependent on the raw data (corpus) but are also influenced by editorial philosophies and the target audience. These external determinants can introduce biases, potentially distorting the perceived semantic landscape.

Motivation

In response to the challenges faced by traditional word embedding aggregation techniques, a graph-based approach offers a promising alternative. Graphs, by their nature, are inherently flexible and adaptable. They can be molded to suit specific needs, accommodating a range of data types and structures. Graphs also offer a more nuanced representation of data, allowing for the inclusion of multiple relationships and connections. This is particularly relevant in the realm of semantic modeling, where words can be associated with a range of meanings and contexts. Graphs, therefore, facilitate a deeper, more comprehensive exploration of semantic terrains without resorting to the lumping that aggregation methods often impose.

In this methodology, individual words are represented as nodes in a graph, while their corresponding embeddings serve as node features. The relationships or edges between these nodes are established based on features of semantic closeness. Graphs also allow for the use of multiple types of edges, representing varied semantic associations. Utilizing a temporal graph forecasting model, the methodology aspires to predict the dynamism of a target word's semantic relationships across time, with other words in the corpus. This allows for an effective capture and forecasting of word meaning transitions and relational evolutions. To elucidate, let's consider the lexeme web. Historically situated within a biological or fabric-oriented semantic realm, its associations were predominantly with entities such as spider or weave. In the graph representation, the node web during this epoch would exhibit strong edge weights with these terms. Yet, with the digital revolution and the advent of the internet, web began its semantic transition. If we were to examine its node in a more recent temporal slice of the graph, we'd anticipate strengthened connections with nodes representing browser, online, and internet. The temporal graph forecasting model, by leveraging historical data and current embeddings, could predict this evolving topography of edges for the web node, flagging its semantic shift. The graph-centric methodology for detecting semantic shifts, underpinned by embeddings as node features, embodies a promising departure from traditional techniques. However, its effective deployment demands a conscientious navigation of its inherent challenges, ensuring its robustness in varied linguistic landscapes.

Project Objectives

The core ambition of this project is to construct a graph-based model that intricately captures the relationships between words, offering a solution that surpasses the constraints of traditional word embedding aggregation techniques. By representing words and their associated meanings in this interconnected format, the model aims to provide a deeper perspective on semantics.

Introducing a temporal dimension becomes essential to capture the fluidity of language, reflecting its evolving nature. This dynamic representation will enable insights into how word meanings and associations shift across time, painting a comprehensive picture of linguistic transitions. Alongside this, effective node selection is paramount, as the vocabulary size of a corpus can be vast. By including words that mirror the topological characteristics or are proximal to the target word in the graph, the model aims to ensure that the graph is both comprehensive and precise in its semantic representation. Integrating techniques such as similarity metrics, distributional semantics, and unsupervised graph clustering, complemented by insights from domain experts can also enhance this representation.

Forecasting is another crucial aspect. Utilizing temporal graph forecasting models, the objective is to predict potential shifts in word relationships and meanings across different chronological spans. This predictive approach is further enriched by tapping into external knowledge bases, such as WordNet and ConceptNet, which promise a deeper, layered understanding of semantic interconnections.

Continuous evaluation forms the backbone of this endeavor. The outcomes derived from the initial model iterations will be subjected to rigorous assessment, leading to iterative refinements to enhance accuracy and maintain semantic fidelity. The graph-centric approach will be juxtaposed with traditional aggregation methods, allowing for a critical examination of the strengths, weaknesses, and unique insights each methodology brings to the fore. Finally, in recognizing the expansive nature of language, considerations of scalability and efficiency are paramount. This project is dedicated to ensuring that the developed model is adaptable to large vocabularies and complex relationships while optimizing computational demands.

In conclusion, this project embarks on a journey to delve deep into the intricate tapestry of language, aiming to unravel broader semantic themes, linguistic trends, and the cultural implications of observed semantic shifts.


  1. Adam Kilgarriff. "i don't believe in word senses". Computers and the Humanities, 31:, 04 1999.