Emanuele Lapponi

Representing and Resolving Negation for Sentiment Analysis

Proper treatment of negation is an important characteristic of methods for sentiment analysis. However, while there is a growing body of research on the automatic resolution of negation, it is not yet clear as to how negation is best represented for different applications. To begin to address this issue, we review representation alternatives and present a state-of-the-art system for negation resolution that is interoperable across these schemes. By employing different configurations of this system as a component in a test bed for lexically-based sentiment classification, we demonstrate that the choice of representation can have a significant impact on downstream processing.

On different approaches to syntactic analysis into bi-lexical dependencies: An empirical comparison of direct, PCFG-based, and HPSG-based parsers

We compare three different approaches to parsing into syntactic, bilexical dependencies for English: a ‘direct’ data-driven dependency parser, a statistical phrase structure parser, and a hybrid, ‘deep’ grammar-driven parser. The analyses from the latter two are postconverted to bi-lexical dependencies. Through this ‘reduction’ of all three approaches to syntactic dependency parsers, we determine empirically what performance can be obtained for a common set of dependency types for English; in- and out-of-domain experimentation ranges over diverse text types. In doing so, we observe what trade-offs apply along three dimensions: accuracy, efficiency, and resilience to domain variation. Our results suggest that the hand-built grammar in one of our parsers helps in both accuracy and cross-domain parsing performance. When evaluated extrinsically in two downstream tasks – negation resolution and semantic dependency parsing – these accuracy gains do sometimes but not always translate into improved end-to-end performance.

Representation and Interchange of Linguistic Annotation. An In-Depth, Side-by-Side Comparison of Three Designs

For decades, most self-respecting linguistic engineering initiatives have designed and implemented custom representations for various layers of, for example, morphological, syntactic, and semantic analysis. Despite occasional efforts at harmonization or even standardization, our field today is blessed with a multitude of ways of encoding and exchanging linguistic annotations of these types, both at the levels of ‘abstract syntax’, naming choices, and of course file formats. To a large degree, it is possible to work within and across design plurality by conversion, and often there may be good reasons for divergent design reflecting differences in use. However, it is likely that some abstract commonalities across choices of representation are obscured by more superficial differences, and conversely there is no obvious procedure to tease apart what actually constitute contentful vs. mere technical divergences. In this study, we seek to conceptually align three representations for common types of morpho-syntactic analysis, pinpoint what in our view constitute contentful differences, and reflect on the underlying principles and specific requirements that led to individual choices. We expect that a more in-depth understanding of these choices across designs may led to increased harmonization, or at least to more informed design of future representations.

Predicting Party Affiliations from European Parliament Debates

This paper documents an ongoing effort to assess whether party group affiliation of participants in European Parliament debates can be automatically predicted on the basis of the content of their speeches, using a support vector machine multi-class model. The work represents a joint effort between researchers within Political Science and Language Technology.

The Talk of Norway: a richly annotated corpus of the Norwegian parliament, 1998–2016

AbstractIn this work we present the Talk of Norway (ToN) data set, a collection of Norwegian Parliament speeches from 1998 to 2016. Every speech is richly annotated with metadata harvested from different sources, and augmented with language type, sentence, token, lemma, part-of-speech, and morphological feature annotations. We also present a pilot study on party classification in the Norwegian Parliament, carried out in the context of a cross-faculty collaboration involving researchers from both Political Science and Computer Science. Our initial experiments demonstrate how the linguistic and institutional annotations in ToN can be used to gather insights on how different aspects of the political process affect classification.