Distributional Semantics and Linguistic Theory (1905.01896v4)

Abstract: Distributional semantics provides multi-dimensional, graded, empirically induced word representations that successfully capture many aspects of meaning in natural languages, as shown in a large body of work in computational linguistics; yet, its impact in theoretical linguistics has so far been limited. This review provides a critical discussion of the literature on distributional semantics, with an emphasis on methods and results that are of relevance for theoretical linguistics, in three areas: semantic change, polysemy and composition, and the grammar-semantics interface (specifically, the interface of semantics with syntax and with derivational morphology). The review aims at fostering greater cross-fertilization of theoretical and computational approaches to language, as a means to advance our collective knowledge of how it works.

• The paper demonstrates the utility of vector space models for capturing nuanced semantic change and graded word meanings.
• The paper contrasts single-vector and sense-specific models to address polysemy and refine semantic representations.
• The paper explores the grammar-semantics interface by linking distributional features with syntactic patterns for improved linguistic analysis.

An Examination of Distributional Semantics and Its Contribution to Linguistic Theory

The paper "Distributional Semantics and Linguistic Theory" by Gemma Boleda presents a comprehensive review of the relationship between distributional semantics and linguistic theory. The document discusses the potential of distributional semantics to identify and model various linguistic phenomena, emphasizing its contributions to areas such as semantic change, polysemy, composition, and the grammar-semantics interface.

Distributional semantics operates on the premise of the Distributional Hypothesis, which suggests that semantically similar words appear in similar contexts. This hypothesis is materialized in the form of vector spaces where words are represented as vectors reflecting their context of use. The paper argues for the utility of these vector space models in providing empirical, multi-dimensional, and graded word representations, elements that are essential for capturing subtle semantic nuances and graded meanings.

Key Areas and Contributions

1. Semantic Change: The application of distributional semantics in diachronic linguistic studies shows promising results. By analyzing shifts in word context over time, researchers have been able to detect and trace semantic change. The gradedness inherent in distributional models aligns well with the gradual nature of semantic evolution, allowing for detailed tracking of shifts, as exemplified by the semantic journey of words like "gay" across the 20th century.
2. Polysemy and Composition: The review distinguishes between two primary approaches in handling polysemy: single vector representations and sense-specific vectors. While single vectors offer a consolidated view that encapsulates all meanings, the paper acknowledges the challenges this model faces in accounting for distinct word senses. On the other hand, sense-specific models attempt to resolve this by clustering instances based on contextual similarity, offering a more nuanced depiction of polysemous words.
3. Grammar-Semantics Interface: The relationship between distributional semantics and grammatical structures, such as argument structure and derivational morphology, is another focal point. The paper discusses how distributional models can be employed to explore the syntax-semantics interface, offering insights into verb class behavior and thematic role assignment. In morphological studies, these models help predict derivational outcomes and uncover patterns in noun compounds, though challenges remain regarding the handling of infrequent or highly lexicalized forms.

Implications and Future Directions

Boleda's analysis illustrates the potential of distributional semantics as both a methodological tool and a source of theoretical insight, especially in areas tightly interwoven with semantics, such as polysemy and diachronic change. The empirical nature of these models supports the exploration of extensive linguistic datasets and enables the development of hypotheses that can be tested on a large scale, contributing to the refinement of linguistic theories.

Nonetheless, the paper also highlights ongoing challenges, particularly the requirement for large data corpora and the presence of biases within those data sets. The exploration of function words and deeper grammatical structures remains a complex domain, as conventional distributional models are better suited to content words and more straightforward composition.

Recent advancements in neural network architectures, including their ability to incorporate contextual information, point to exciting possibilities for improving the scope and accuracy of distributional representations. These developments suggest a future where distributional semantics could effectively bridge the gap between abstract lexical representation and context-dependent language use.

Overall, Boleda's work underscores the significance of cross-disciplinary collaboration between computational and theoretical linguists to further refine these models and enhance our understanding of underlying linguistic principles. The ongoing research into distributional semantics is poised to make substantial contributions to the broader field of linguistics by offering new tools and methodologies for empirically grounded linguistic analysis.