12 research outputs found
Refining Implicit Argument Annotation for UCCA
Predicate-argument structure analysis is a central component in meaning
representations of text. The fact that some arguments are not explicitly
mentioned in a sentence gives rise to ambiguity in language understanding, and
renders it difficult for machines to interpret text correctly. However, only
few resources represent implicit roles for NLU, and existing studies in NLP
only make coarse distinctions between categories of arguments omitted from
linguistic form. This paper proposes a typology for fine-grained implicit
argument annotation on top of Universal Conceptual Cognitive Annotation's
foundational layer. The proposed implicit argument categorisation is driven by
theories of implicit role interpretation and consists of six types: Deictic,
Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set. We
exemplify our design by revisiting part of the UCCA EWT corpus, providing a new
dataset annotated with the refinement layer, and making a comparative analysis
with other schemes.Comment: DMR 202
How much of UCCA can be predicted from AMR?
International audienceIn this paper, we consider two of the currently popular semantic frameworks: Abstract Meaning Representation (AMR)a more abstract framework, and Universal Conceptual Cognitive Annotation (UCCA)-an anchored framework. We use a corpus-based approach to build two graph rewriting systems, a deterministic and a non-deterministic one, from the former to the latter framework. We present their evaluation and a number of ambiguities that we discovered while building our rules. Finally, we provide a discussion and some future work directions in relation to comparing semantic frameworks of different flavors
Graph-based broad-coverage semantic parsing
Many broad-coverage meaning representations can be characterized as directed graphs,
where nodes represent semantic concepts and directed edges represent semantic relations among the concepts. The task of semantic parsing is to generate such a meaning
representation from a sentence. It is quite natural to adopt a graph-based approach for
parsing, where nodes are identified conditioning on the individual words, and edges
are labeled conditioning on the pairs of nodes. However, there are two issues with
applying this simple and interpretable graph-based approach for semantic parsing:
first, the anchoring of nodes to words can be implicit and non-injective in several
formalisms (Oepen et al., 2019, 2020). This means we do not know which nodes
should be generated from which individual word and how many of them. Consequently, it makes a probabilistic formulation of the training objective problematical;
second, graph-based parsers typically predict edge labels independent from each other.
Such an independence assumption, while being sensible from an algorithmic point of
view, could limit the expressiveness of statistical modeling. Consequently, it might fail
to capture the true distribution of semantic graphs.
In this thesis, instead of a pipeline approach to obtain the anchoring, we propose to
model the implicit anchoring as a latent variable in a probabilistic model. We induce
such a latent variable jointly with the graph-based parser in an end-to-end differentiable training. In particular, we test our method on Abstract Meaning Representation
(AMR) parsing (Banarescu et al., 2013). AMR represents sentence meaning with a
directed acyclic graph, where the anchoring of nodes to words is implicit and could be
many-to-one. Initially, we propose a rule-based system that circumvents the many-to-one anchoring by combing nodes in some pre-specified subgraphs in AMR and treats
the alignment as a latent variable. Next, we remove the need for such a rule-based system by treating both graph segmentation and alignment as latent variables. Still, our
graph-based parsers are parameterized by neural modules that require gradient-based
optimization. Consequently, training graph-based parsers with our discrete latent variables can be challenging. By combing deep variational inference and differentiable
sampling, our models can be trained end-to-end. To overcome the limitation of graph-based parsing and capture interdependency in the output, we further adopt iterative
refinement. Starting with an output whose parts are independently predicted, we iteratively refine it conditioning on the previous prediction. We test this method on
semantic role labeling (Gildea and Jurafsky, 2000). Semantic role labeling is the task
of predicting the predicate-argument structure. In particular, semantic roles between
the predicate and its arguments need to be labeled, and those semantic roles are interdependent. Overall, our refinement strategy results in an effective model, outperforming
strong factorized baseline models
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Functional Distributional Semantics: Learning Linguistically Informed Representations from a Precisely Annotated Corpus
The aim of distributional semantics is to design computational techniques that can automatically learn the meanings of words from a body of text. The twin challenges are: how do we represent meaning, and how do we learn these representations? The current state of the art is to represent meanings as vectors – but vectors do not correspond to any traditional notion of meaning. In particular, there is no way to talk about truth, a crucial concept in logic and formal semantics.
In this thesis, I develop a framework for distributional semantics which answers this challenge. The meaning of a word is not represented as a vector, but as a function, mapping entities (objects in the world) to probabilities of truth (the probability that the word is true of the entity). Such a function can be interpreted both in the machine learning sense of a classifier, and in the formal semantic sense of a truth-conditional function. This simultaneously allows both the use of machine learning techniques to exploit large datasets, and also the use of formal semantic techniques to manipulate the learnt representations. I define a probabilistic graphical model, which incorporates a probabilistic generalisation of model theory (allowing a strong connection with formal semantics), and which generates semantic dependency graphs (allowing it to be trained on a corpus). This graphical model provides a natural way to model logical inference, semantic composition, and context-dependent meanings, where Bayesian inference plays a crucial role. I demonstrate the feasibility of this approach by training a model on WikiWoods, a parsed version of the English Wikipedia, and evaluating it on three tasks. The results indicate that the model can learn information not captured by vector space models.Schiff Fund Studentshi
Vector Semantics
This open access book introduces Vector semantics, which links the formal theory of word vectors to the cognitive theory of linguistics. The computational linguists and deep learning researchers who developed word vectors have relied primarily on the ever-increasing availability of large corpora and of computers with highly parallel GPU and TPU compute engines, and their focus is with endowing computers with natural language capabilities for practical applications such as machine translation or question answering. Cognitive linguists investigate natural language from the perspective of human cognition, the relation between language and thought, and questions about conceptual universals, relying primarily on in-depth investigation of language in use. In spite of the fact that these two schools both have ‘linguistics’ in their name, so far there has been very limited communication between them, as their historical origins, data collection methods, and conceptual apparatuses are quite different. Vector semantics bridges the gap by presenting a formal theory, cast in terms of linear polytopes, that generalizes both word vectors and conceptual structures, by treating each dictionary definition as an equation, and the entire lexicon as a set of equations mutually constraining all meanings
Vector Semantics
This open access book introduces Vector semantics, which links the formal theory of word vectors to the cognitive theory of linguistics. The computational linguists and deep learning researchers who developed word vectors have relied primarily on the ever-increasing availability of large corpora and of computers with highly parallel GPU and TPU compute engines, and their focus is with endowing computers with natural language capabilities for practical applications such as machine translation or question answering. Cognitive linguists investigate natural language from the perspective of human cognition, the relation between language and thought, and questions about conceptual universals, relying primarily on in-depth investigation of language in use. In spite of the fact that these two schools both have ‘linguistics’ in their name, so far there has been very limited communication between them, as their historical origins, data collection methods, and conceptual apparatuses are quite different. Vector semantics bridges the gap by presenting a formal theory, cast in terms of linear polytopes, that generalizes both word vectors and conceptual structures, by treating each dictionary definition as an equation, and the entire lexicon as a set of equations mutually constraining all meanings
With her shoulder to the wheel: the public life of Erika Theron (1907-1990)
This thesis is a biographical study of Erika Theron (1907-1990), an Afrikaner woman who played a significant role in many aspects of public life in South Africa in a critical time in the country‘s history. The study seeks to give recognition to her achievements, which have received scant attention in a historiography with a masculine bias. At the same time it examines her changing role from collaborator to critic of the apartheid system.
Certain defining features of Theron‘s life have been highlighted. First, Theron grew up in a staunchly Afrikaner nationalist, service-oriented family which encouraged loyalty to her own people and civic responsibility. Second, she was unusual among Afrikaner women of her generation, in that she was highly educated, independent and ready to assume leadership roles. She became a pioneer in a number of fields, attaining high professional rank and holding important public offices – frequently as the first woman to do so in the country.
The thesis focuses on five areas of Theron‘s public life. After returning from post-graduate studies abroad, she worked with Hendrik Verwoerd in the campaign to uplift poor whites, particularly the rehabilitation and re-integration of the Afrikaner poor. She thereafter commenced a long career as a social work academic, which included a number of milestones for her new discipline, for the profession of social work and for the advancement of women in academia. From the 1950s she served on the town council of Stellenbosch, including terms as deputy mayor and mayor. She played an important role in historic conservation but was also instrumental in the rigorous institution of apartheid structures in the town during the early days of National Party rule. In the early 1970s she served as chairman of the Commission of Enquiry into Coloured Affairs which influenced her personal views on the country‘s race policies. She became a public critic of many aspects of the apartheid system and vocal advocate for coloured rights.HistoryD. Litt. et Phil. (History