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We present an implemented XML data model and a new, simplified query language for multi-level annotated corpora. The new query language involves automatic conversion of queries into the underlying, more complicated MMAXQL query language. It supports queries for sequential and hierarchical, but also associative (e.g. coreferential) relations. The simplified query language has been designed with non-expert users in mind.
We apply a decision tree based approach to pronoun resolution in spoken dialogue. Our system deals with pronouns with NP- and non-NP-antecedents. We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features. We evaluate the system on twenty Switchboard dialogues and show that it compares well to Byron’s (2002) manually tuned system.
This paper presents the current results of an ongoing research project on corpus distribution of prepositions and pronouns within Polish preposition-pronoun contractions. The goal of the project is to provide a quantitative description of Polish preposition-pronoun contractions taking into consideration morphosyntactic properties of their components. It is expected that the results will provide a basis for a revision of the traditionally assumed inflectional paradigms of Polish pronouns and, thus, for a possible remodeling of these paradigms. The results of corpus-based investigations of the distribution of prepositions within preposition-pronoun contractions can be used for grammar-theoretical and lexicographic purposes.
Feedback utterances are among the most frequent in dialogue. Feedback is also a crucial aspect of linguistic theories that take social interaction, involving language, into account. This paper introduces the corpora and datasets of a project scrutinizing this kind of feedback utterances in French. We present the genesis of the corpora (for a total of about 16 hours of transcribed and phone force-aligned speech) involved in the project. We introduce the resulting datasets and discuss how they are being used in on-going work with focus on the form-function relationship of conversational feedback. All the corpora created and the datasets produced in the framework of this project will be made available for research purposes.
This paper presents a survey on hate speech detection. Given the steadily growing body of social media content, the amount of online hate speech is also increasing. Due to the massive scale of the web, methods that automatically detect hate speech are required. Our survey describes key areas that have been explored to automatically recognize these types of utterances using natural language processing. We also discuss limits of those approaches.
This paper presents a survey on the role of negation in sentiment analysis. Negation is a very common linguistic construction that affects polarity and, therefore, needs to be taken into consideration in sentiment analysis.
We will present various computational approaches modeling negation in sentiment analysis. We will, in particular, focus on aspects such as level of representation used for sentiment analysis, negation word detection and scope of negation. We will also discuss limits and challenges of negation modeling on that task.
In this paper, we investigate the practical applicability of Co-Training for the task of building a classifier for reference resolution. We are concerned with the question if Co-Training can significantly reduce the amount of manual labeling work and still produce a classifier with an acceptable performance.
We present an implemented machine learning system for the automatic detection of nonreferential it in spoken dialog. The system builds on shallow features extracted from dialog transcripts. Our experiments indicate a level of performance that makes the system usable as a preprocessing filter for a coreference resolution system. We also report results of an annotation study dealing with the classification of it by naive subjects.
Automatic Food Categorization from Large Unlabeled Corpora and Its Impact on Relation Extraction
(2014)
We present a weakly-supervised induction method to assign semantic information to food items. We consider two tasks of categorizations being food-type classification and the distinction of whether a food item is composite or not. The categorizations are induced by a graph-based algorithm applied on a large unlabeled domain-specific corpus. We show that the usage of a domain-specific corpus is vital. We do not only outperform a manually designed open-domain ontology but also prove the usefulness of these categorizations in relation extraction, outperforming state-of-the-art features that include syntactic information and Brown clustering.
Opinion holder extraction is one of the important subtasks in sentiment analysis. The effective detection of an opinion holder depends on the consideration of various cues on various levels of representation, though they are hard to formulate explicitly as features. In this work, we propose to use convolution kernels for that task which identify meaningful fragments of sequences or trees by themselves. We not only investigate how different levels of information can be effectively combined in different kernels but also examine how the scope of these kernels should be chosen. In general relation extraction, the two candidate entities thought to be involved in a relation are commonly chosen to be the boundaries of sequences and trees. The definition of boundaries in opinion holder extraction, however, is less straightforward since there might be several expressions beside the candidate opinion holder to be eligible for being a boundary.
Active Learning (AL) has been proposed as a technique to reduce the amount of annotated data needed in the context of supervised classification. While various simulation studies for a number of NLP tasks have shown that AL works well on goldstandard data, there is some doubt whether the approach can be successful when applied to noisy, real-world data sets. This paper presents a thorough evaluation of the impact of annotation noise on AL and shows that systematic noise resulting from biased coder decisions can seriously harm the AL process. We present a method to filter out inconsistent annotations during AL and show that this makes AL far more robust when applied to noisy data.
This paper discusses the behaviour of German particle verbs formed by two-way prepositions in combination with pleonastic PPs including the verb particle as a preposition. These particle verbs have a characteristic feature: some of them license directional prepositional phrases in the accusative, some only allow for locative PPs in the dative, and some particle verbs can occur with PPs in the accusative and in the dative. Directional particle verbs together with directional PPs present an additional problem: the particle and the preposition in the PP seem to provide redundant information. The paper gives an overview of the semantic verb classes influencing this phenomenon, based on corpus data, and explains the underlying reasons for the behaviour of the particle verbs. We also show how the restrictions on particle verbs and pleonastic PPs can be expressed in a grammar theory like Lexical Functional Grammar (LFG).
We present the IUCL system, based on supervised learning, for the shared task on stance detection. Our official submission, the random forest model, reaches a score of 63.60, and is ranked 6th out of 19 teams. We also use gradient boosting decision trees and SVM and merge all classifiers into an ensemble method. Our analysis shows that random forest is good at retrieving minority classes and gradient boosting majority classes. The strengths of different classifiers wrt. precision and recall complement each other in the ensemble.
In this paper, we will investigate a cross-linguistic phenomenon referred to as complex prepositions (CPs), which is a frequent type of multiword expressions (MWEs) in many languages. Based on empirical data, we will point out the problems of the traditional treatment of CPs as complex lexical categories, and, thus, propose an analysis using the formal paradigm of the HPSG in the tradition of (Pollard and Sag, 1994). Our objective is to provide an approach to CPs which (1) convincingly explains empirical data, (2) is consistent with the underlying formal framework and does not require any extensions or modification of the existing description apparatus, (3) is computationally tractable.
MULLE is a tool for language learning that focuses on teaching Latin as a foreign language. It is aimed for easy integration into the traditional classroom setting and syllabus, which makes it distinct from other language learning tools that provide standalone learning experience. It uses grammar-based lessons and embraces methods of gamification to improve the learner motivation. The main type of exercise provided by our application is to practice translation, but it is also possible to shift the focus to vocabulary or morphology training.
We present a light-weight tool for the annotation of linguistic data on multiple levels. It is based on the simplification of annotations to sets of markables having attributes and standing in certain relations to each other. We describe the main features of the tool, emphasizing its simplicity, customizability and versatility
We present an approach for opinion role induction for verbal predicates. Our model rests on the assumption that opinion verbs can be divided into three different types where each type is associated with a characteristic mapping between semantic roles and opinion holders and targets. In several experiments, we demonstrate the relevance of those three categories for the task. We show that verbs can easily be categorized with semi-supervised graphbased clustering and some appropriate similarity metric. The seeds are obtained through linguistic diagnostics. We evaluate our approach against a new manually-compiled opinion role lexicon and perform in-context classification.
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 predicative adjectives as an unsupervised criterion to extract subjective adjectives. We do not only compare this criterion with a weakly supervised extraction method but also with gradable adjectives, i.e. another highly subjective subset of adjectives that can be extracted in an unsupervised fashion. In order to prove the robustness of this extraction method, we will evaluate the extraction with the help of two different state-of-the-art sentiment lexicons (as a gold standard).