Graph Neural Networks(GNNs) have been applied successfully to various NLP tasks, particularly Relation Extraction(RE). Even though most of these approaches rely on the syntactic dependency tree of a sentence to derive a graph representation, the impact of this choice compared to other possible graph representations has not been evaluated. We examine the effect of representing text though a graph of different graph representations for GNNs that are applied to RE, considering, e.g., a fully connected graph of tokens, of semantic role structures, and combinations thereof. We further examine the impact of background knowledge injection from Knowledge Graphs(KGs) into the graph representation to achieve enhanced graph representations. Our results show that combining multiple graph representations can improve the model’s predictions. Moreover, the integration of background knowledge positively impacts scores, as enhancing the text graphs with Wikidata features or WordNet features can lead to an improvement of close to 0.1 points in F1.

Argument mining has focused so far mainly on the identification, extraction, and formalization of arguments. An important yet unaddressedtask consists in the prediction of the argumentative behavior of stakeholders in a debate. Predicting the argumentative behavior in advance can support foreseeing issues in public policy making or help recognize potential disagreements early on and help to resolve them. In this paper, we consider the novel task of predicting the argumentative behavior of individual stakeholders. We present ARGENST, a framework that relies on a recommender-based architecture to predict the stance and the argumentative main point on a specific controversial topic for a given stakeholder, which is described in terms of a profile including properties related to demographic attributes, religious and political orientation, socio-economic background, etc. We evaluate our approach on the well-known debate.org dataset in terms of accuracy for predicting stance as well as in terms of similarity of the generated arguments to the ground truth arguments using BERTScore. As part of a case study, we show how juries of members representing different stakeholder groups and perspectives can be assembled to simulate the public opinion on a given topic.

Temporal adverbial phrases such as recently and some time ago have a special function in communication and temporal cognition. These adverbials are deictic, in that their meaning is tied to their time of utterance; and they are vague, in that the time periods to which they apply are under-specified in comparison to expressions such as yesterday, which precisely indicates the day before the day of utterance. Despite their vagueness, conversational participants have a mental image of when events described using these adverbials take place. We present a study that aims to quantify this mental model in terms of fuzzy or graded membership. To achieve this, we investigated the four English temporal adverbials recently, just, some time ago and long time ago as applied to types of events with different durations and frequencies, by conducting surveys to measure how speakers judge the different adverbials to apply in different time ranges. Our results suggest that it is possible to represent the meanings of deictic vague temporal adverbials geometrically in terms of graded membership within a temporal conceptual space.

Explanations are pervasive in our lives. Mostly, they occur in dialogical form where an explainer discusses a concept or phenomenon of interest with an explainee. Leaving the explainee with a clear understanding is not straightforward due to the knowledge gap between the two participants. Previous research looked at the interaction of explanation moves, dialogue acts, and topics in successful dialogues with expert explainers. However, daily-life explanations often fail, raising the question of what makes a dialogue successful. In this work, we study explanation dialogues in terms of the interactions between the explainer and explainee and how they correlate with the quality of explanations in terms of a successful understanding on the explainee’s side. In particular, we first construct a corpus of 399 dialogues from the Reddit forum Explain Like I am Five and annotate it for interaction flows and explanation quality. We then analyze the interaction flows, comparing them to those appearing in expert dialogues. Finally, we encode the interaction flows using two language models that can handle long inputs, and we provide empirical evidence for the effectiveness boost gained through the encoding in predicting the success of explanation dialogues.

In recent years, large language models have achieved state-of-the-art performance across various NLP tasks. However, investigations have shown that these models tend to rely on shortcut features, leading to inaccurate predictions and causing the models to be unreliable at generalization to out-of-distribution (OOD) samples. For instance, in the context of relation extraction (RE), we would expect a model to identify the same relation independently of the entities involved in it. For example, consider the sentence “Leonardo da Vinci painted the Mona Lisa” expressing the created(Leonardo_da_Vinci, Mona_Lisa) relation. If we substiute “Leonardo da Vinci” with “Barack Obama”, then the sentence still expresses the created relation. A robust model is supposed to detect the same relation in both cases. In this work, we describe several semantically-motivated strategies to generate adversarial examples by replacing entity mentions and investigate how state-of-the-art RE models perform under pressure. Our analyses show that the performance of these models significantly deteriorates on the modified datasets (avg. of -48.5% in F1), which indicates that these models rely to a great extent on shortcuts, such as surface forms (or patterns therein) of entities, without making full use of the information present in the sentences.

Arguments often do not make explicit how a conclusion follows from its premises. To compensate for this lack, we enrich arguments with structured background knowledge to support knowledge-intense argumentation tasks. We present a new unsupervised method for constructing Contextualized Commonsense Knowledge Graphs (CCKGs) that selects contextually relevant knowledge from large knowledge graphs (KGs) efficiently and at high quality. Our work goes beyond context-insensitive knowledge extraction heuristics by computing semantic similarity between KG triplets and textual arguments. Using these triplet similarities as weights, we extract contextualized knowledge paths that connect a conclusion to its premise, while maximizing similarity to the argument. We combine multiple paths into a CCKG that we optionally prune to reduce noise and raise precision. Intrinsic evaluation of the quality of our graphs shows that our method is effective for (re)constructing human explanation graphs. Manual evaluations in a large-scale knowledge selection setup verify high recall and precision of implicit CSK in the CCKGs. Finally, we demonstrate the effectiveness of CCKGs in a knowledge-insensitive argument quality rating task, outperforming strong baselines and rivaling a GPT-3 based system.

Many NLP tasks exhibit human label variation, where different annotators give different labels to the same texts. This variation is known to depend, at least in part, on the sociodemographics of annotators. Recent research aims to model individual annotator behaviour rather than predicting aggregated labels, and we would expect that sociodemographic information is useful for these models. On the other hand, the ecological fallacy states that aggregate group behaviour, such as the behaviour of the average female annotator, does not necessarily explain individual behaviour. To account for sociodemographics in models of individual annotator behaviour, we introduce group-specific layers to multi-annotator models. In a series of experiments for toxic content detection, we find that explicitly accounting for sociodemographic attributes in this way does not significantly improve model performance. This result shows that individual annotation behaviour depends on much more than just sociodemographics.

Framing is an important mechanism in argumentation, as participants in a debate tend to emphasize those aspects or dimensions of the issue under debate that support their standpoint. The task of reframing an argument, that is changing the underlying framing, has received increasing attention recently. We propose a novel unsupervised approach to argument reframing that takes inspiration from counterfactual explanation generation approaches in the field of eXplainable AI (XAI). We formalize the task as a mask-and-replace approach in which an LLM is tasked to replace masked tokens associated with a set of frames to be eliminated by other tokens related to a set of target frames to be added. Our method relies on two key mechanisms: framed decoding and reranking based on a number of metrics similar to those used in XAI to search for a suitable counterfactual. We evaluate our approach on three topics using the dataset by Ruckdeschel and Wiedemann (2022). We show that our two key mechanisms outperform an unguided LLM as a baseline by increasing the ratio of successfully reframed arguments by almost an order of magnitude.

Many annotation tasks in natural language processing are highly subjective in that there can be different valid and justified perspectives on what is a proper label for a given example. This also applies to the judgment of argument quality, where the assignment of a single ground truth is often questionable. At the same time, there are generally accepted concepts behind argumentation that form a common ground. To best represent the interplay of individual and shared perspectives, we consider a continuum of approaches ranging from models that fully aggregate perspectives into a majority label to “share nothing”-architectures in which each annotator is considered in isolation from all other annotators. In between these extremes, inspired by models used in the field of recommender systems, we investigate the extent to which architectures that predict labels for single annotators but include layers that model the relations between different annotators are beneficial. By means of two tasks of argument quality classification (argument concreteness and validity/novelty of conclusions), we show that recommender architectures increase the averaged annotator-individual F1-scores up to 43% over a majority-label model. Our findings indicate that approaches to subjectivity can benefit from relating individual perspectives.

Counterspeech on social media is rare. Consequently, it is difficult to collect naturally occurring examples, in particular for languages without annotated datasets. In this work, we study methods to increase the relevance of social media samples for counterspeech annotation when we lack annotated resources. We use the example of sourcing German data for counterspeech annotations from Twitter. We monitor tweets from German politicians and activists to collect replies. To select relevant replies we a) find replies that match German abusive keywords or b) label replies for counterspeech using a multilingual classifier fine-tuned on English data. For both approaches and a baseline setting, we annotate a random sample and use bootstrap sampling to estimate the amount of counterspeech. We find that neither the multilingual model nor the keyword approach achieve significantly higher counts of true counterspeech than the baseline. Thus, keyword lists or multi-lingual classifiers are likely not worth the added complexity beyond purposive data collection: Already without additional filtering, we gather a meaningful sample with 7,4% true counterspeech.

This paper describes the system and experimental results of an ensemble-based approach tomultilingual framing detection for the submission of the ACCEPT team to the SemEval-2023 Task 3 on Framing Detection (Subtask 2). The approach is based on an ensemble that combines three different methods: a classifier based on large language models, a classifier based on static word embeddings, and an approach that uses external commonsense knowledge graphs, in particular, ConceptNet. The results of the three classification heads are aggregated into an overall prediction for each frame class. Our best submission yielded a micro F1-score of 50.69% (rank 10) and a macro F1-score of 50.20% (rank 3) for English articles. Our experimental results show that static word embeddings and knowledge graphs are useful components for frame detection, while the ensemble of all three methods combines the strengths of our three proposed methods. Through system ablations, we show that the commonsenseguided knowledge graphs are the outperforming method for many languages.

We address the problem of automatically predicting the quality of a conclusion given a set of (textual) premises of an argument, focusing in particular on the task of predicting the validity and novelty of the argumentative conclusion. We propose a multi-task approach that jointly predicts the validity and novelty of the textual conclusion, relying on pre-trained language models fine-tuned on the task. As training data for this task is scarce and costly to obtain, we experimentally investigate the impact of data augmentation approaches for improving the accuracy of prediction compared to a baseline that relies on task-specific data only. We consider the generation of synthetic data as well as the integration of datasets from related argument tasks. We show that especially our synthetic data, combined with class-balancing and instance-specific learning rates, substantially improves classification results (+15.1 points in F₁-score). Using only training data retrieved from related datasets by automatically labeling them for validity and novelty, combined with synthetic data, outperforms the baseline by 11.5 points in F₁-score.

This paper provides an overview of the Argument Validity and Novelty Prediction Shared Task that was organized as part of the 9th Workshop on Argument Mining (ArgMining 2022). The task focused on the prediction of the validity and novelty of a conclusion given a textual premise. Validity is defined as the degree to which the conclusion is justified with respect to the given premise. Novelty defines the degree to which the conclusion contains content that is new in relation to the premise. Six groups participated in the task, submitting overall 13 system runs for the subtask of binary classification and 2 system runs for the subtask of relative classification. The results reveal that the task is challenging, with best results obtained for Validity prediction in the range of 75% F1 score, for Novelty prediction of 70% F1 score and for correctly predicting both Validity and Novelty of 45% F1 score. In this paper we summarize the task definition and dataset. We give an overview of the results obtained by the participating systems, as well as insights to be gained from the diverse contributions.

We present a deep learning based information extraction system that can extract the design and results of a published abstract describing a Randomized Controlled Trial (RCT). In contrast to other approaches, our system does not regard the PICO elements as flat objects or labels but as structured objects. We thus model the task as the one of filling a set of templates and slots; our two-step approach recognizes relevant slot candidates as a first step and assigns them to a corresponding template as second step, relying on a learned pairwise scoring function that models the compatibility of the different slot values. We evaluate the approach on a dataset of 211 manually annotated abstracts for type 2 Diabetes and Glaucoma, showing the positive impact of modelling intra-template entity compatibility. As main benefit, our approach yields a structured object for every RCT abstract that supports the aggregation and summarization of clinical trial results across published studies and can facilitate the task of creating a systematic review or meta-analysis.

Recent question answering and machine reading benchmarks frequently reduce the task to one of pinpointing spans within a certain text passage that answers the given question. Typically, these systems are not required to actually understand the text on a deeper level that allows for more complex reasoning on the information contained. We introduce a new dataset called BiQuAD that requires deeper comprehension in order to answer questions in both extractive and deductive fashion. The dataset consist of 4,190 closed-domain texts and a total of 99,149 question-answer pairs. The texts are synthetically generated soccer match reports that verbalize the main events of each match. All texts are accompanied by a structured Datalog program that represents a (logical) model of its information. We show that state-of-the-art QA models do not perform well on the challenging long form contexts and reasoning requirements posed by the dataset. In particular, transformer based state-of-the-art models achieve F1-scores of only 39.0. We demonstrate how these synthetic datasets align structured knowledge with natural text and aid model introspection when approaching complex text understanding.

When assessing the similarity of arguments, researchers typically use approaches that do not provide interpretable evidence or justifications for their ratings. Hence, the features that determine argument similarity remain elusive. We address this issue by introducing novel argument similarity metrics that aim at high performance and explainability. We show that Abstract Meaning Representation (AMR) graphs can be useful for representing arguments, and that novel AMR graph metrics can offer explanations for argument similarity ratings. We start from the hypothesis that similar premises often lead to similar conclusions—and extend an approach for AMR-based argument similarity rating by estimating, in addition, the similarity of conclusions that we automatically infer from the arguments used as premises. We show that AMR similarity metrics make argument similarity judgements more interpretable and may even support argument quality judgements. Our approach provides significant performance improvements over strong baselines in a fully unsupervised setting. Finally, we make first steps to address the problem of reference-less evaluation of argumentative conclusion generations.

Key point analysis is the task of extracting a set of concise and high-level statements from a given collection of arguments, representing the gist of these arguments. This paper presents our proposed approach to the Key Point Analysis Shared Task, colocated with the 8th Workshop on Argument Mining. The approach integrates two complementary components. One component employs contrastive learning via a siamese neural network for matching arguments to key points; the other is a graph-based extractive summarization model for generating key points. In both automatic and manual evaluation, our approach was ranked best among all submissions to the shared task.

Model-complete text comprehension aims at interpreting a natural language text with respect to a semantic domain model describing the classes and their properties relevant for the domain in question. Solving this task can be approached as a structured prediction problem, consisting in inferring the most probable instance of the semantic model given the text. In this work, we focus on the challenging sub-problem of cardinality prediction that consists in predicting the number of distinct individuals of each class in the semantic model. We show that cardinality prediction can successfully be approached by modeling the overall task as a joint inference problem, predicting the number of individuals of certain classes while at the same time extracting their properties. We approach this task with probabilistic graphical models computing the maximum-a-posteriori instance of the semantic model. Our main contribution lies on the empirical investigation and analysis of different approximative inference strategies based on Gibbs sampling. We present and evaluate our models on the task of extracting key parameters from scientific full text articles describing pre-clinical studies in the domain of spinal cord injury.

In social care environments, the main goal of social workers is to foster independent living by their clients. An important task is thus to monitor progress towards reaching independence in different areas of their patients’ life. To support this task, we present an approach that extracts indications of independence on different life aspects from the day-to-day documentation that social workers create. We describe the process of collecting and annotating a corresponding corpus created from data records of two social work institutions with a focus on disability care. We show that the agreement on the task of annotating the observations of social workers with respect to discrete independent levels yields a high agreement of .74 as measured by Fleiss’ Kappa. We present a classification approach towards automatically classifying an observation into the discrete independence levels and present results for different types of classifiers. Against our original expectation, we show that we reach F-Measures (macro) of 95% averaged across topics, showing that this task can be automatically solved.

In this paper we describe the contributions made by the European H2020 project “Prêt-à-LLOD” (‘Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors’) to the further development of the Linguistic Linked Open Data (LLOD) infrastructure. Prêt-à-LLOD aims to develop a new methodology for building data value chains applicable to a wide range of sectors and applications and based around language resources and language technologies that can be integrated by means of semantic technologies. We describe the methods implemented for increasing the number of language data sets in the LLOD. We also present the approach for ensuring interoperability and for porting LLOD data sets and services to other infrastructures, as well as the contribution of the projects to existing standards.

In recent years, there has been increasing interest in publishing lexicographic and terminological resources as linked data. The benefit of using linked data technologies to publish terminologies is that terminologies can be linked to each other, thus creating a cloud of linked terminologies that cross domains, languages and that support advanced applications that do not work with single terminologies but can exploit multiple terminologies seamlessly. We present Terme-‘a-LLOD (TAL), a new paradigm for transforming and publishing terminologies as linked data which relies on a virtualization approach. The approach rests on a preconfigured virtual image of a server that can be downloaded and installed. We describe our approach to simplifying the transformation and hosting of terminological resources in the remainder of this paper. We provide a proof-of-concept for this paradigm showing how to apply it to the conversion of the well-known IATE terminology as well as to various smaller terminologies. Further, we discuss how the implementation of our paradigm can be integrated into existing NLP service infrastructures that rely on virtualization technology. While we apply this paradigm to the transformation and hosting of terminologies as linked data, the paradigm can be applied to any other resource format as well.

Aspect-based sentiment analysis involves the recognition of so called opinion target expressions (OTEs). To automatically extract OTEs, supervised learning algorithms are usually employed which are trained on manually annotated corpora. The creation of these corpora is labor-intensive and sufficiently large datasets are therefore usually only available for a very narrow selection of languages and domains. In this work, we address the lack of available annotated data for specific languages by proposing a zero-shot cross-lingual approach for the extraction of opinion target expressions. We leverage multilingual word embeddings that share a common vector space across various languages and incorporate these into a convolutional neural network architecture for OTE extraction. Our experiments with 5 languages give promising results: We can successfully train a model on annotated data of a source language and perform accurate prediction on a target language without ever using any annotated samples in that target language. Depending on the source and target language pairs, we reach performances in a zero-shot regime of up to 77% of a model trained on target language data. Furthermore, we can increase this performance up to 87% of a baseline model trained on target language data by performing cross-lingual learning from multiple source languages.

We propose to study the evolution of concepts by learning to complete diachronic analogies between lists of terms which relate to the same concept at different points in time. We present a number of models based on operations on word embedddings that correspond to different assumptions about the characteristics of diachronic analogies and change in concept vocabularies. These are tested in a quantitative evaluation for nine different concepts on a corpus of Dutch newspapers from the 1950s and 1980s. We show that a model which treats the concept terms as analogous and learns weights to compensate for diachronic changes (weighted linear combination) is able to more accurately predict the missing term than a learned transformation and two baselines for most of the evaluated concepts. We also find that all models tend to be coherent in relation to the represented concept, but less discriminative in regard to other concepts. Additionally, we evaluate the effect of aligning the time-specific embedding spaces using orthogonal Procrustes, finding varying effects on performance, depending on the model, concept and evaluation metric. For the weighted linear combination, however, results improve with alignment in a majority of cases. All related code is released publicly.

Supervised machine learning algorithms require training data whose generation for complex relation extraction tasks tends to be difficult. Being optimized for relation extraction at sentence level, many annotation tools lack in facilitating the annotation of relational structures that are widely spread across the text. This leads to non-intuitive and cumbersome visualizations, making the annotation process unnecessarily time-consuming. We propose SANTO, an easy-to-use, domain-adaptive annotation tool specialized for complex slot filling tasks which may involve problems of cardinality and referential grounding. The web-based architecture enables fast and clearly structured annotation for multiple users in parallel. Relational structures are formulated as templates following the conceptualization of an underlying ontology. Further, import and export procedures of standard formats enable interoperability with external sources and tools.

Fine-grained sentiment analysis is receiving increasing attention in recent years. Extracting opinion target expressions (OTE) in reviews is often an important step in fine-grained, aspect-based sentiment analysis. Retrieving this information from user-generated text, however, can be difficult. Customer reviews, for instance, are prone to contain misspelled words and are difficult to process due to their domain-specific language. In this work, we investigate whether character-level models can improve the performance for the identification of opinion target expressions. We integrate information about the character structure of a word into a sequence labeling system using character-level word embeddings and show their positive impact on the system’s performance. Specifically, we obtain an increase by 3.3 points F1-score with respect to our baseline model. In further experiments, we reveal encoded character patterns of the learned embeddings and give a nuanced view of the performance differences of both models.

Word embeddings have been shown to be highly effective in a variety of lexical semantic tasks. They tend to capture meaningful relational similarities between individual words, at the expense of lacking the capabilty of making the underlying semantic relation explicit. In this paper, we investigate the attribute relation that often holds between the constituents of adjective-noun phrases. We use CBOW word embeddings to represent word meaning and learn a compositionality function that combines the individual constituents into a phrase representation, thus capturing the compositional attribute meaning. The resulting embedding model, while being fully interpretable, outperforms count-based distributional vector space models that are tailored to attribute meaning in the two tasks of attribute selection and phrase similarity prediction. Moreover, as the model captures a generalized layer of attribute meaning, it bears the potential to be used for predictions over various attribute inventories without re-training.

The Open Linguistics Working Group (OWLG) brings together researchers from various fields of linguistics, natural language processing, and information technology to present and discuss principles, case studies, and best practices for representing, publishing and linking linguistic data collections. A major outcome of our work is the Linguistic Linked Open Data (LLOD) cloud, an LOD (sub-)cloud of linguistic resources, which covers various linguistic databases, lexicons, corpora, terminologies, and metadata repositories. We present and summarize five years of progress on the development of the cloud and of advancements in open data in linguistics, and we describe recent community activities. The paper aims to serve as a guideline to orient and involve researchers with the community and/or Linguistic Linked Open Data.

In order to explore intuitive verbal and non-verbal interfaces in smart environments we recorded user interactions with an intelligent apartment. Besides offering various interactive capabilities itself, the apartment is also inhabited by a social robot that is available as a humanoid interface. This paper presents a multi-modal corpus that contains goal-directed actions of naive users in attempts to solve a number of predefined tasks. Alongside audio and video recordings, our data-set consists of large amount of temporally aligned sensory data and system behavior provided by the environment and its interactive components. Non-verbal system responses such as changes in light or display contents, as well as robot and apartment utterances and gestures serve as a rich basis for later in-depth analysis. Manual annotations provide further information about meta data like the current course of study and user behavior including the incorporated modality, all literal utterances, language features, emotional expressions, foci of attention, and addressees.

In order to make the growing amount of conceptual knowledge available through ontologies and datasets accessible to humans, NLP applications need access to information on how this knowledge can be verbalized in natural language. One way to provide this kind of information are ontology lexicons, which apart from the actual verbalizations in a given target language can provide further, rich linguistic information about them. Compiling such lexicons manually is a very time-consuming task and requires expertise both in Semantic Web technologies and lexicon engineering, as well as a very good knowledge of the target language at hand. In this paper we present an alternative approach to generating ontology lexicons by means of crowdsourcing: We use CrowdFlower to generate a small Japanese ontology lexicon for ten exemplary ontology elements from the DBpedia ontology according to a two-stage workflow, the main underlying idea of which is to turn the task of generating lexicon entries into a translation task; the starting point of this translation task is a manually created English lexicon for DBpedia. Comparison of the results to a manually created Japanese lexicon shows that the presented workflow is a viable option if an English seed lexicon is already available.

Recent years have witnessed a surge in the amount of semantic information published on the Web. Indeed, the Web of Data, a subset of the Semantic Web, has been increasing steadily in both volume and variety, transforming the Web into a ‘global database’ in which resources are linked across sites. Linguistic fields – in a broad sense – have not been left behind, and we observe a similar trend with the growth of linguistic data collections on the so-called ‘Linguistic Linked Open Data (LLOD) cloud’. While both Semantic Web and Natural Language Processing communities can obviously take advantage of this growing and distributed linguistic knowledge base, they are today faced with a new challenge, i.e., that of facilitating multilingual access to the Web of data. In this paper we present the publication of BabelNet 2.0, a wide-coverage multilingual encyclopedic dictionary and ontology, as Linked Data. The conversion made use of lemon, a lexicon model for ontologies particularly well-suited for this enterprise. The result is an interlinked multilingual (lexical) resource which can not only be accessed on the LOD, but also be used to enrich existing datasets with linguistic information, or to support the process of mapping datasets across languages.

Opinion mining has received wide attention in recent years. Models for this task are typically trained or evaluated with a manually annotated dataset. However, fine-grained annotation of sentiments including information about aspects and their evaluation is very labour-intensive. The data available so far is limited. Contributing to this situation, this paper describes the Bielefeld University Sentiment Analysis Corpus for German and English (USAGE), which we offer freely to the community and which contains the annotation of product reviews from Amazon with both aspects and subjective phrases. It provides information on segments in the text which denote an aspect or a subjective evaluative phrase which refers to the aspect. Relations and coreferences are explicitly annotated. This dataset contains 622 English and 611 German reviews, allowing to investigate how to port sentiment analysis systems across languages and domains. We describe the methodology how the corpus was created and provide statistics including inter-annotator agreement. We further provide figures for a baseline system and results for German and English as well as in a cross-domain setting. The results are encouraging in that they show that aspects and phrases can be extracted robustly without the need of tuning to a particular type of products.

The creation of language resources is a time-consuming process requiring the efforts of many people. The use of resources collaboratively created by non-linguistists can potentially ameliorate this situation. However, such resources often contain more errors compared to resources created by experts. For the particular case of lexica, we analyse the case of Wiktionary, a resource created along wiki principles and argue that through the use of a principled lexicon model, namely Lemon, the resulting data could be better understandable to machines. We then present a platform called Lemon Source that supports the creation of linked lexical data along the Lemon model. This tool builds on the concept of a semantic wiki to enable collaborative editing of the resources by many users concurrently. In this paper, we describe the model, the tool and present an evaluation of its usability based on a small group of users.

In this paper we describe SOBA, a sub-component of the SmartWeb multi-modal dialog system. SOBA is a component for ontologybased information extraction from soccer web pages for automatic population of a knowledge base that can be used for domainspecific question answering. SOBA realizes a tight connection between the ontology, knowledge base and the information extraction component. The originality of SOBA is in the fact that it extracts information from heterogeneous sources such as tabular structures, text and image captions in a semantically integrated way. In particular, it stores extracted information in a knowledge base, and in turn uses the knowledge base to interpret and link newly extracted information with respect to already existing entities.

Recent work has aimed at discovering ontological relations from text corpora. Most approaches are based on the assumption that verbs typically indicate semantic relations between concepts. However, the problem of finding the appropriate generalization level for the verb's arguments with respect to a given taxonomy has not received much attention in the ontology learning community. In this paper, we address the issue of determining the appropriate level of abstraction for binary relations extracted from a corpus with respect to a given concept hierarchy. For this purpose, we reuse techniques from the subcategorization and selectional restrictions acquisition communities. The contribution of our work lies in the systematic analysis of three different measures. We conduct our experiments on the Genia corpus and the Genia ontology and evaluate the different measures by comparing the results of our approach with a gold standard provided by one of the authors, a biologist.