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Overview of the IGGSA 2016 Shared Task on Source and Target Extraction from Political Speeches
(2016)
We present the second iteration of IGGSA’s Shared Task on Sentiment Analysis for German. It resumes the STEPS task of IGGSA’s 2014 evaluation campaign: Source, Subjective Expression and Target Extraction from Political Speeches. As before, the task is focused on fine-grained sentiment analysis, extracting sources and targets with their associated subjective expressions from a corpus of speeches given in the Swiss parliament. The second iteration exhibits some differences, however; mainly the use of an adjudicated gold standard and the availability of training data. The shared task had 2 participants submitting 7 runs for the full task and 3 runs for each of the subtasks. We evaluate the results and compare them to the baselines provided by the previous iteration. The shared task homepage can be found at http://iggsasharedtask2016.github.io/.
A polarity-sensitive item (PSI), as traditionally defined, is an expression that is restricted to either an affirmative or negative context. PSIs like ‘lift a finger’ and ‘all the time in the world’ sub-serve discourse routines like understatement and emphasis. Lexical–semantic classes are increasingly invoked in descriptions of the properties of PSIs. Here, we use English corpus data and the tools of Frame Semantics (Fillmore, 1982, 1985) to explore Israel’s (2011) observation that the semantic role of a PSI determines how the expression fits into a contextually constructed scalar model. We focus on a class of exceptions implied by Israel’s model: cases in which a given PSI displays two countervailing patterns of polarity sensitivity, with attendant differences in scalar entailments. We offer a set of case studies of polaritysensitive expressions – including verbs of attraction and aversion like ‘can live without’, monetary units like ‘a red cent’, comparative adjectives and time-span adverbials – that demonstrate that the interpretation of a given PSI in a given polar context is based on multiple factors. These factors include the speaker’s perspective on and affective stance towards the described event, available inferences about causality and, perhaps most critically, particulars of the predication, including the verb or adjective’s frame membership, the presence or absence of an ability modal like can, the grammatical construction used and the range of contingencies evoked by the utterance.
Sentiment analysis has so far focused on the detection of explicit opinions. However, of late implicit opinions have received broader attention, the key idea being that the evaluation of an event type by a speaker depends on how the participants in the event are valued and how the event itself affects the participants. We present an annotation scheme for adding relevant information, couched in terms of so-called effect functors, to German lexical items. Our scheme synthesizes and extends previous proposals. We report on an inter-annotator agreement study. We also present results of a crowdsourcing experiment to test the utility of some known and some new functors for opinion inference where, unlike in previous work, subjects are asked to reason from event evaluation to participant evaluation.
The Shared Task on Source and Target Extraction from Political Speeches (STEPS) first ran in 2014 and is organized by the Interest Group on German Sentiment Analysis (IGGSA). This volume presents the proceedings of the workshop of the second iteration of the shared task. The workshop was held at KONVENS 2016 at Ruhr-University Bochum on September 22, 2016.
We present an approach to the new task of opinion holder and target extraction on opinion compounds. Opinion compounds (e.g. user rating or victim support) are noun compounds whose head is an opinion noun. We do not only examine features known to be effective for noun compound analysis, such as paraphrases and semantic classes of heads and modifiers, but also propose novel features tailored to this new task. Among them, we examine paraphrases that jointly consider holders and targets, a verb detour in which noun heads are replaced by related verbs, a global head constraint allowing inferencing between different compounds, and the categorization of the sentiment view that the head conveys.
We examine different features and classifiers for the categorization of opinion words into actor and speaker view. To our knowledge, this is the first comprehensive work to address sentiment views on the word level taking into consideration opinion verbs, nouns and adjectives. We consider many high-level features requiring only few labeled training data. A detailed feature analysis produces linguistic insights into the nature of sentiment views. We also examine how far global constraints between different opinion words help to increase classification performance. Finally, we show that our (prior) word-level annotation correlates with contextual sentiment views.
There is increasing interest in recognizing opinion inferences in addition to expressions of explicit sentiment. While different formalisms for representing inferential mechanisms are being developed and lexical resources are being built alongside, we here address the need for deeper investigation of the robustness of various aspects of opinion inference, performing crowdsourcing experiments with constructed stimuli as well as a corpus study of attested data.