Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation

Question Generation (QG) is fundamentally a simple syntactic transformation; however, many aspects of semantics influence what questions are good to form. We implement this observation by developing Syn-QG, a set of transparent syntactic rules leveraging universal dependencies, shallow semantic parsing, lexical resources, and custom rules which transform declarative sentences into question-answer pairs. We utilize PropBank argument descriptions and VerbNet state predicates to incorporate shallow semantic content, which helps generate questions of a descriptive nature and produce inferential and semantically richer questions than existing systems. In order to improve syntactic fluency and eliminate grammatically incorrect questions, we employ back-translation over the output of these syntactic rules. A set of crowd-sourced evaluations shows that our system can generate a larger number of highly grammatical and relevant questions than previous QG systems and that back-translation drastically improves grammaticality at a slight cost of generating irrelevant questions.Comment: Some of the results in the paper were incorrec

Why languages differ : variation in the conventionalization of constraints on inference

Sperber and Wilson (1996) and Wilson and Sperber (1993) have argued that communication involves two processes, ostension and inference, but they also assume there is a coding-decoding stage of communication and a functional distinction between lexical items and grammatical marking (what they call 'conceptual' vs. 'procedural' information). Sperber and Wilson have accepted a basically Chomskyan view of the innateness of language structure and Universal Grammar

PersoNER: Persian named-entity recognition

© 1963-2018 ACL. Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network

Joint event extraction based on hierarchical event schemas from framenet

Event extraction is useful for many practical applications, such as news summarization and information retrieval. However, the popular automatic context extraction (ACE) event extraction program only defines very limited and coarse event schemas, which may not be suitable for practical applications. FrameNet is a linguistic corpus that defines complete semantic frames and frame-to-frame relations. As frames in FrameNet share highly similar structures with event schemas in ACE and many frames actually express events, we propose to redefine the event schemas based on FrameNet. Specifically, we extract frames expressing event information from FrameNet and leverage the frame-to-frame relations to build a hierarchy of event schemas that are more fine-grained and have much wider coverage than ACE. Based on the new event schemas, we propose a joint event extraction approach that leverages the hierarchical structure of event schemas and frame-to-frame relations in FrameNet. The extensive experiments have verified the advantages of our hierarchical event schemas and the effectiveness of our event extraction model. We further apply the results of our event extraction model on news summarization. The results show that the summarization approach based on our event extraction model achieves significant better performance than several state-of-the-art summarization approaches, which also demonstrates that the hierarchical event schemas and event extraction model are promising to be used in the practical applications

Proceedings of the 2010 Annual Conference of the Gesellschaft für Semantik

Sinn & Bedeutung - the annual conference of the Gesellschaft für Semantik - aims to bring together both established researchers and new blood working on current issues in natural language semantics, pragmatics, the syntax-semantics interface, the philosophy of language or carrying out psycholinguistic studies related to meaning. Every year, the conference moves to a different location in Europe. The 2010 conference - Sinn & Bedeutung 15 - took place on September 9 - 11 at Saarland University, Saarbrücken, organized by the Department for German Studies

Definiteness effects and competition in tenses and aspects

This dissertations explores the semantics and pragmatics of tense and aspect constructions in three groups of languages: I. English; II. French, Italian, German; III. Mandarin Chinese. The basic claims of this dissertation are: (i) the English past tense is lexically ambiguous between an anaphoric and a uniqueness reading; (ii) the different properties of the present perfect construction in English versus French, Italian and German follow from the competition between the present perfect with the alternative past tense and the different set of alternatives available in these languages; (iii) the distribution of the Mandarin Chinese perfective particles reflects asymmetry in their presuppositions, such as anaphoricity and anti-resultativeness; (iv) Mandarin Chinese differs from the languages in group I and II in that it establishes anaphoric dependency in the domain of eventualities, not times; (v) the crosslinguistic distribution of perfect-like tense-aspectual constructions follows from similar semantic-pragmatic strategies, namely the competition between alternatives from a set of general categories (anaphoric, unique, neutral, and antiresulative)

The VERBMOBIL domain model version 1.0

This report describes the domain model used in the German Machine Translation project VERBMOBIL. In order make the design principles underlying the modeling explicit, we begin with a brief sketch of the VERBMOBIL demonstrator architecture from the perspective of the domain model. We then present some rather general considerations on the nature of domain modeling and its relationship to semantics. We claim that the semantic information contained in the model mainly serves two tasks. For one thing, it provides the basis for a conceptual transfer from German to English; on the other hand, it provides information needed for disambiguation. We argue that these tasks pose different requirements, and that domain modeling in general is highly task-dependent. A brief overview of domain models or ontologies used in existing NLP systems confirms this position. We finally describe the different parts of the domain model, explain our design decisions, and present examples of how the information contained in the model can be actually used in the VERBMOBIL demonstrator. In doing so, we also point out the main functionality of FLEX, the Description Logic system used for the modeling