4,635 research outputs found
Information and Experience in Metaphor: A Perspective From Computer Analysis
Novel linguistic metaphor can be seen as the assignment of attributes to a topic through a vehicle belonging to another domain. The experience evoked by the vehicle is a significant aspect of the meaning of the metaphor, especially for abstract metaphor, which involves more than mere physical similarity. In this article I indicate, through description of a specific model, some possibilities as well as limitations of computer processing directed toward both informative and experiential/affective aspects of metaphor. A background to the discussion is given by other computational treatments of metaphor analysis, as well as by some questions about metaphor originating in other disciplines. The approach on which the present metaphor analysis model is based is consistent with a theory of language comprehension that includes both the intent of the originator and the effect on the recipient of the metaphor. The model addresses the dual problem of (a) determining potentially salient properties of the vehicle concept, and (b) defining extensible symbolic representations of such properties, including affective and other connotations. The nature of the linguistic analysis underlying the model suggests how metaphoric expression of experiential components in abstract metaphor is dependent on the nominalization of actions and attributes. The inverse process of undoing such nominalizations in computer analysis of metaphor constitutes a translation of a metaphor to a more literal expression within the metaphor-nonmetaphor dichotomy
Compositional Semantic Parsing on Semi-Structured Tables
Two important aspects of semantic parsing for question answering are the
breadth of the knowledge source and the depth of logical compositionality.
While existing work trades off one aspect for another, this paper
simultaneously makes progress on both fronts through a new task: answering
complex questions on semi-structured tables using question-answer pairs as
supervision. The central challenge arises from two compounding factors: the
broader domain results in an open-ended set of relations, and the deeper
compositionality results in a combinatorial explosion in the space of logical
forms. We propose a logical-form driven parsing algorithm guided by strong
typing constraints and show that it obtains significant improvements over
natural baselines. For evaluation, we created a new dataset of 22,033 complex
questions on Wikipedia tables, which is made publicly available
Understanding Task Design Trade-offs in Crowdsourced Paraphrase Collection
Linguistically diverse datasets are critical for training and evaluating
robust machine learning systems, but data collection is a costly process that
often requires experts. Crowdsourcing the process of paraphrase generation is
an effective means of expanding natural language datasets, but there has been
limited analysis of the trade-offs that arise when designing tasks. In this
paper, we present the first systematic study of the key factors in
crowdsourcing paraphrase collection. We consider variations in instructions,
incentives, data domains, and workflows. We manually analyzed paraphrases for
correctness, grammaticality, and linguistic diversity. Our observations provide
new insight into the trade-offs between accuracy and diversity in crowd
responses that arise as a result of task design, providing guidance for future
paraphrase generation procedures.Comment: Published at ACL 201
Attempto - From Specifications in Controlled Natural Language towards Executable Specifications
Deriving formal specifications from informal requirements is difficult since
one has to take into account the disparate conceptual worlds of the application
domain and of software development. To bridge the conceptual gap we propose
controlled natural language as a textual view on formal specifications in
logic. The specification language Attempto Controlled English (ACE) is a subset
of natural language that can be accurately and efficiently processed by a
computer, but is expressive enough to allow natural usage. The Attempto system
translates specifications in ACE into discourse representation structures and
into Prolog. The resulting knowledge base can be queried in ACE for
verification, and it can be executed for simulation, prototyping and validation
of the specification.Comment: 15 pages, compressed, uuencoded Postscript, to be presented at EMISA
Workshop 'Naturlichsprachlicher Entwurf von Informationssystemen -
Grundlagen, Methoden, Werkzeuge, Anwendungen', May 28-30, 1996, Ev. Akademie
Tutzin
Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs
Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines
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