Search

We present a formalization of dependency labeling with Integer Linear Programming. We focus on the integration of subcategorization into the decision making process, where the various subcategorization frames of a verb compete with each other. A maximum entropy model provides the weights for ILP optimization.

In this paper, we present a formalization of grammatical role labeling within the framework of Integer Linear Programming (ILP). We focus on the integration of subcategorization information into the decision making process. We present a first empirical evaluation that achieves competitive precision and recall rates.

The work presented here is an approach to Sentiment Analy sis from a rule based compositional perspective. The proposed approach is characterized by three major points: (a) rules are automatically learned from annotated corpora using Inductive Logic Programming and represented as Prolog sets of clauses (b) the focus is on the noun phrase (NP) level and (c) learning is performed on deep parsed structures. We describe the process of annotating a collection of some 3000 German NPs of medium to quite complex structure as well as the empirical evaluation of our implementation in comparison with commonly used classifiers and a handcrafted rule based system.

We have manually curated a polarity lexicon for German comprising word polarities and polarity strength values of about 8000 words: nouns verbs and adjectives. The decisions were primarily carried out using the synsets from GermaNet a WordNet like lexical database. In an evaluation on German novels it turned out that the stock of adjectives was too small. We carried out experiments to automatically learn new subjective adjectives together with their polarity orientation and polarity strength. For this purpose we applied a corpus based approach that works with pairs of coordinated adjectives extracted from a large German newspaper corpus. In the context of this work we evaluated two subtasks in detail. First how good are we at reproducing the polarity classification ? including our three level strength measure ? contained in our initial lexicon by machine learning methods. Second because adding of training material did not improve the results at the expected rate we evaluated the human intercoder agreement on polarity classifications in an experiment. The results show that judgements about the strength of polarity do vary considerably between different persons. Given these problems related to the design and automatic augmentation of polarity lexicons we have successfully experimented with a semi automatically approach where a list of reliable candidate words (here: adjectives) is generated to ease the manual annotation process.

Proceedings of the 17th Nordic Conference of Computational Linguistics NODALIDA 2009. Editors: Kristiina Jokinen and Eckhard Bick. NEALT Proceedings Series, Vol. 4 (2009), 235-238. © 2009 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/9206 .

Nous pr??sentons ici PolArt un outil multilingue pour l?analyse de sentiments qui aborde la composition des sentiments en appliquant des transducteurs en cascade. La compositionnalit?? est assure?? au moyen de polarit??s pr??alables extraites d?un lexique et des r??gles de composition appliqu??es de mani??re incr??mentielle. We introduce PolArt a multilingual tool for sentiment detection that copes with sentiment composition through the application of cascaded transducers. Compositionality is enabled by prior polarities taken from a polarity lexicon and the compositional rules applied incrementally.

Proceedings of the Second Workshop on Anaphora Resolution (WAR II). Editor: Christer Johansson. NEALT Proceedings Series, Vol. 2 (2008), 31-40. © 2008 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/7129 .

In this paper we focus on anaphora resolution for German, a highly inflected language which also allows for closed form compounds (i.e. compounds without spaces). Especially, we describe a system that only uses real preprocessing components, e.g. a dependency parser, a two-level morphological analyser etc. We trace the performance drop occurring under these conditions back to underspecification and ambiguity at the morphological level. A demanding subtask of anaphora resolution are the so-called bridging anaphora, a special variant of nominal anaphora where the heads of the coreferent noun phrases do not match. We experiment with two different resources in order to find out how to cope best with this problem.

We introduce an explorative tool for affect analysis from texts. Rather than the full range of emotions, feelings, and sentiment, our system is currently restricted to the positive or negative polarity of phrases and sentences. It analyses the input texts with the aid of a affect lexicon that specifies among others the prior polarity (positive or negative) of words. A chunker is used to determine phrases that are the basis for a compositional treatment of phraselevel polarity assignment. In our current experiments we focus on phrases that are targeted towards persons, be it the writer (I, my, me,.), the social group including the writer (we, our,.) or the reader (you, your,). We evaluate our system with standard data (customer reviews). We also give initial results from a small corpus of 35 texts taken from a panel group called 'I battle depression'.