pwc-archive/papers-with-abstracts huggingface dataset:
Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags
Authors: Yunli Wang, Cyril Goutte
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Change Detection, Change Point Detection, Clustering, Time Series Analysis

Detecting events from social media data has important applications in public security, political issues, and public health. Many studies have focused on detecting specific or unspecific events from Twitter streams. However, not much attention has been paid to detecting changes, and their impact, in online conversations related to an event. We propose methods for detecting such changes, using clustering of temporal profiles of hashtags, and three change point detection algorithms. The methods were tested on two Twitter datasets: one covering the 2014 Ottawa shooting event, and one covering the Sochi winter Olympics. We compare our approach to a baseline consisting of detecting change from raw counts in the conversation. We show that our method produces large gains in change detection accuracy on both datasets.
Event Detection Using Frame-Semantic Parser
Authors: Evangelia Spiliopoulou, Eduard Hovy, Teruko Mitamura
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Deep Learning, Event Detection, General Classification, Word Embeddings

Recent methods for Event Detection focus on Deep Learning for automatic feature generation and feature ranking. However, most of those approaches fail to exploit rich semantic information, which results in relatively poor recall. This paper is a small {\&} focused contribution, where we introduce an Event Detection and classification system, based on deep semantic information retrieved from a frame-semantic parser. Our experiments show that our system achieves higher recall than state-of-the-art systems. Further, we claim that enhancing our system with deep learning techniques like feature ranking can achieve even better results, as it can benefit from both approaches.
Improving Shared Argument Identification in Japanese Event Knowledge Acquisition
Authors: Yin Jou Huang, Sadao Kurohashi
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Coreference Resolution, Text Generation

Event knowledge represents the knowledge of causal and temporal relations between events. Shared arguments of event knowledge encode patterns of role shifting in successive events. A two-stage framework was proposed for the task of Japanese event knowledge acquisition, in which related event pairs are first extracted, and shared arguments are then identified to form the complete event knowledge. This paper focuses on the second stage of this framework, and proposes a method to improve the shared argument identification of related event pairs. We constructed a gold dataset for shared argument learning. By evaluating our system on this gold dataset, we found that our proposed model outperformed the baseline models by a large margin.
Tracing armed conflicts with diachronic word embedding models
Authors: Andrey Kutuzov, Erik Velldal, Lilja {\O}vrelid
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Word Embeddings

Recent studies have shown that word embedding models can be used to trace time-related (diachronic) semantic shifts in particular words. In this paper, we evaluate some of these approaches on the new task of predicting the dynamics of global armed conflicts on a year-to-year basis, using a dataset from the conflict research field as the gold standard and the Gigaword news corpus as the training data. The results show that much work still remains in extracting {`}cultural{'} semantic shifts from diachronic word embedding models. At the same time, we present a new task complete with an evaluation set and introduce the {`}anchor words{'} method which outperforms previous approaches on this set.
The Circumstantial Event Ontology (CEO)
Authors: Roxane Segers, Tommaso Caselli, Piek Vossen
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

In this paper we describe the ongoing work on the Circumstantial Event Ontology (CEO), a newly developed ontology for calamity events that models semantic circumstantial relations between event classes. The circumstantial relations are designed manually, based on the shared properties of each event class. We discuss and contrast two types of event circumstantial relations: semantic circumstantial relations and episodic circumstantial relations. Further, we show the metamodel and the current contents of the ontology and outline the evaluation of the CEO.
Event Detection and Semantic Storytelling: Generating a Travelogue from a large Collection of Personal Letters
Authors: Georg Rehm, Julian Moreno Schneider, Peter Bourgonje, Ankit Srivastava, Jan Nehring, Armin Berger, Luca K{\"o}nig, S{\"o}ren R{\"a}uchle, Jens Gerth
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Entity Linking, Event Detection, Machine Translation, Named Entity Recognition (NER)

We present an approach at identifying a specific class of events, movement action events (MAEs), in a data set that consists of ca. 2,800 personal letters exchanged by the German architect Erich Mendelsohn and his wife, Luise. A backend system uses these and other semantic analysis results as input for an authoring environment that digital curators can use to produce new pieces of digital content. In our example case, the human expert will receive recommendations from the system with the goal of putting together a travelogue, i.e., a description of the trips and journeys undertaken by the couple. We describe the components and architecture and also apply the system to news data.
On the Creation of a Security-Related Event Corpus
Authors: Martin Atkinson, Jakub Piskorski, Hristo Tanev, Vanni Zavarella
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Event Extraction

This paper reports on an effort of creating a corpus of structured information on security-related events automatically extracted from on-line news, part of which has been manually curated. The main motivation behind this effort is to provide material to the NLP community working on event extraction that could be used both for training and evaluation purposes.
Inducing Event Types and Roles in Reverse: Using Function to Discover Theme
Authors: Natalie Ahn
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Event Extraction

With growing interest in automated event extraction, there is an increasing need to overcome the labor costs of hand-written event templates, entity lists, and annotated corpora. In the last few years, more inductive approaches have emerged, seeking to discover unknown event types and roles in raw text. The main recent efforts use probabilistic generative models, as in topic modeling, which are formally concise but do not always yield stable or easily interpretable results. We argue that event schema induction can benefit from greater structure in the process and in linguistic features that distinguish words{'} functions and themes. To maximize our use of limited data, we reverse the typical schema induction steps and introduce new similarity measures, building an intuitive process for inducing the structure of unknown events.
The Event StoryLine Corpus: A New Benchmark for Causal and Temporal Relation Extraction
Authors: Tommaso Caselli, Piek Vossen
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Natural Language Inference, Question Answering, Relation, Relation Extraction, Temporal Relation Extraction

This paper reports on the Event StoryLine Corpus (ESC) v1.0, a new benchmark dataset for the temporal and causal relation detection. By developing this dataset, we also introduce a new task, the StoryLine Extraction from news data, which aims at extracting and classifying events relevant for stories, from across news documents spread in time and clustered around a single seminal event or topic. In addition to describing the dataset, we also report on three baselines systems whose results show the complexity of the task and suggest directions for the development of more robust systems.
The Rich Event Ontology
Authors: Susan Brown, Claire Bonial, Leo Obrst, Martha Palmer
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Question Answering, Semantic Role Labeling

In this paper we describe a new lexical semantic resource, The Rich Event On-tology, which provides an independent conceptual backbone to unify existing semantic role labeling (SRL) schemas and augment them with event-to-event causal and temporal relations. By unifying the FrameNet, VerbNet, Automatic Content Extraction, and Rich Entities, Relations and Events resources, the ontology serves as a shared hub for the disparate annotation schemas and therefore enables the combination of SRL training data into a larger, more diverse corpus. By adding temporal and causal relational information not found in any of the independent resources, the ontology facilitates reasoning on and across documents, revealing relationships between events that come together in temporal and causal chains to build more complex scenarios. We envision the open resource serving as a valuable tool for both moving from the ontology to text to query for event types and scenarios of interest, and for moving from text to the ontology to access interpretations of events using the combined semantic information housed there.
Integrating Decompositional Event Structures into Storylines
Authors: William Croft, Pavl{\'\i}na Pe{\v{s}}kov{\'a}, Michael Regan
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

Storyline research links together events in stories and specifies shared participants in those stories. In these analyses, an atomic event is assumed to be a single clause headed by a single verb. However, many analyses of verbal semantics assume a decompositional analysis of events expressed in single clauses. We present a formalization of a decompositional analysis of events in which each participant in a clausal event has their own temporally extended subevent, and the subevents are related through causal and other interactions. This decomposition allows us to represent storylines as an evolving set of interactions between participants over time.
Grounding Language for Interactive Task Learning
Authors: Peter Lindes, Aaron Mininger, James R. Kirk, John E. Laird
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Navigate

This paper describes how language is grounded by a comprehension system called Lucia within a robotic agent called Rosie that can manipulate objects and navigate indoors. The whole system is built within the Soar cognitive architecture and uses Embodied Construction Grammar (ECG) as a formalism for describing linguistic knowledge. Grounding is performed using knowledge from the grammar itself, from the linguistic context, from the agents perception, and from an ontology of long-term knowledge about object categories and properties and actions the agent can perform. The paper also describes a benchmark corpus of 200 sentences in this domain along with test versions of the world model and ontology and gold-standard meanings for each of the sentences. The benchmark is contained in the supplemental materials.
Guiding Interaction Behaviors for Multi-modal Grounded Language Learning
Authors: Jesse Thomason, Jivko Sinapov, Raymond Mooney
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Grounded language learning, Retrieval

Multi-modal grounded language learning connects language predicates to physical properties of objects in the world. Sensing with multiple modalities, such as audio, haptics, and visual colors and shapes while performing interaction behaviors like lifting, dropping, and looking on objects enables a robot to ground non-visual predicates like {``}empty{''} as well as visual predicates like {``}red{''}. Previous work has established that grounding in multi-modal space improves performance on object retrieval from human descriptions. In this work, we gather behavior annotations from humans and demonstrate that these improve language grounding performance by allowing a system to focus on relevant behaviors for words like {``}white{''} or {``}half-full{''} that can be understood by looking or lifting, respectively. We also explore adding modality annotations (whether to focus on audio or haptics when performing a behavior), which improves performance, and sharing information between linguistically related predicates (if {``}green{''} is a color, {``}white{''} is a color), which improves grounding recall but at the cost of precision.
Structured Learning for Context-aware Spoken Language Understanding of Robotic Commands
Authors: Andrea Vanzo, Danilo Croce, Roberto Basili, Daniele Nardi
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Spoken Language Understanding

Service robots are expected to operate in specific environments, where the presence of humans plays a key role. A major feature of such robotics platforms is thus the ability to react to spoken commands. This requires the understanding of the user utterance with an accuracy able to trigger the robot reaction. Such correct interpretation of linguistic exchanges depends on physical, cognitive and language-dependent aspects related to the environment. In this work, we present the empirical evaluation of an adaptive Spoken Language Understanding chain for robotic commands, that explicitly depends on the operational environment during both the learning and recognition stages. The effectiveness of such a context-sensitive command interpretation is tested against an extension of an already existing corpus of commands, that introduced explicit perceptual knowledge: this enabled deeper measures proving that more accurate disambiguation capabilities can be actually obtained.
Natural Language Grounding and Grammar Induction for Robotic Manipulation Commands
Authors: Muhannad Alomari, Paul Duckworth, Majd Hawasly, David C. Hogg, Anthony G. Cohn
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

We present a cognitively plausible system capable of acquiring knowledge in language and vision from pairs of short video clips and linguistic descriptions. The aim of this work is to teach a robot manipulator how to execute natural language commands by demonstration. This is achieved by first learning a set of visual {`}concepts{'} that abstract the visual feature spaces into concepts that have human-level meaning. Second, learning the mapping/grounding between words and the extracted visual concepts. Third, inducing grammar rules via a semantic representation known as Robot Control Language (RCL). We evaluate our approach against state-of-the-art supervised and unsupervised grounding and grammar induction systems, and show that a robot can learn to execute never seen-before commands from pairs of unlabelled linguistic and visual inputs.
Communication with Robots using Multilayer Recurrent Networks
Authors: Bed{\v{r}}ich Pi{\v{s}}l, David Mare{\v{c}}ek
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

In this paper, we describe an improvement on the task of giving instructions to robots in a simulated block world using unrestricted natural language commands.
Exploring Variation of Natural Human Commands to a Robot in a Collaborative Navigation Task
Authors: Matthew Marge, Claire Bonial, Ashley Foots, Cory Hayes, Cassidy Henry, Kimberly Pollard, Ron artstein, Clare Voss, David Traum
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

Robot-directed communication is variable, and may change based on human perception of robot capabilities. To collect training data for a dialogue system and to investigate possible communication changes over time, we developed a Wizard-of-Oz study that (a) simulates a robot{'}s limited understanding, and (b) collects dialogues where human participants build a progressively better mental model of the robot{'}s understanding. With ten participants, we collected ten hours of human-robot dialogue. We analyzed the structure of instructions that participants gave to a remote robot before it responded. Our findings show a general initial preference for including metric information (e.g., move forward 3 feet) over landmarks (e.g., move to the desk) in motion commands, but this decreased over time, suggesting changes in perception.
Towards Problem Solving Agents that Communicate and Learn
Authors: Anjali Narayan-Chen, Colin Graber, Mayukh Das, Md. Rakibul Islam, Soham Dan, Sriraam Natarajan, Janardhan Rao Doppa, Julia Hockenmaier, Martha Palmer, Dan Roth
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Semantic Parsing

Agents that communicate back and forth with humans to help them execute non-linguistic tasks are a long sought goal of AI. These agents need to translate between utterances and actionable meaning representations that can be interpreted by task-specific problem solvers in a context-dependent manner. They should also be able to learn such actionable interpretations for new predicates on the fly. We define an agent architecture for this scenario and present a series of experiments in the Blocks World domain that illustrate how our architecture supports language learning and problem solving in this domain.
Language-independent Gender Prediction on Twitter
Authors: Nikola Ljube{\v{s}}i{\'c}, Darja Fi{\v{s}}er, Toma{\v{z}} Erjavec
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Gender Prediction, General Classification, Prediction

In this paper we present a set of experiments and analyses on predicting the gender of Twitter users based on language-independent features extracted either from the text or the metadata of users{'} tweets. We perform our experiments on the TwiSty dataset containing manual gender annotations for users speaking six different languages. Our classification results show that, while the prediction model based on language-independent features performs worse than the bag-of-words model when training and testing on the same language, it regularly outperforms the bag-of-words model when applied to different languages, showing very stable results across various languages. Finally we perform a comparative analysis of feature effect sizes across the six languages and show that differences in our features correspond to cultural distances.
When does a compliment become sexist? Analysis and classification of ambivalent sexism using twitter data
Authors: Akshita Jha, Radhika Mamidi
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: General Classification

Sexism is prevalent in today{'}s society, both offline and online, and poses a credible threat to social equality with respect to gender. According to ambivalent sexism theory (Glick and Fiske, 1996), it comes in two forms: Hostile and Benevolent. While hostile sexism is characterized by an explicitly negative attitude, benevolent sexism is more subtle. Previous works on computationally detecting sexism present online are restricted to identifying the hostile form. Our objective is to investigate the less pronounced form of sexism demonstrated online. We achieve this by creating and analyzing a dataset of tweets that exhibit benevolent sexism. By using Support Vector Machines (SVM), sequence-to-sequence models and FastText classifier, we classify tweets into {`}Hostile{'}, {`}Benevolent{'} or {`}Others{'} class depending on the kind of sexism they exhibit. We have been able to achieve an F1-score of 87.22{\%} using FastText classifier. Our work helps analyze and understand the much prevalent ambivalent sexism in social media.
Personality Driven Differences in Paraphrase Preference
Authors: Daniel Preo{\c{t}}iuc-Pietro, Jordan Carpenter, Lyle Ungar
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Machine Translation, Recommendation Systems, Sentiment Analysis, Text Classification, Text Generation

Personality plays a decisive role in how people behave in different scenarios, including online social media. Researchers have used such data to study how personality can be predicted from language use. In this paper, we study phrase choice as a particular stylistic linguistic difference, as opposed to the mostly topical differences identified previously. Building on previous work on demographic preferences, we quantify differences in paraphrase choice from a massive Facebook data set with posts from over 115,000 users. We quantify the predictive power of phrase choice in user profiling and use phrase choice to study psycholinguistic hypotheses. This work is relevant to future applications that aim to personalize text generation to specific personality types.
community2vec: Vector representations of online communities encode semantic relationships
Authors: Trevor Martin
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

Vector embeddings of words have been shown to encode meaningful semantic relationships that enable solving of complex analogies. This vector embedding concept has been extended successfully to many different domains and in this paper we both create and visualize vector representations of an unstructured collection of online communities based on user participation. Further, we quantitatively and qualitatively show that these representations allow solving of semantically meaningful community analogies and also other more general types of relationships. These results could help improve community recommendation engines and also serve as a tool for sociological studies of community relatedness.
Telling Apart Tweets Associated with Controversial versus Non-Controversial Topics
Authors: Aseel Addawood, Rezvaneh Rezapour, Omid Abdar, Jana Diesner
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

In this paper, we evaluate the predictability of tweets associated with controversial versus non-controversial topics. As a first step, we crowd-sourced the scoring of a predefined set of topics on a Likert scale from non-controversial to controversial. Our feature set entails and goes beyond sentiment features, e.g., by leveraging empathic language and other features that have been previously used but are new for this particular study. We find focusing on the structural characteristics of tweets to be beneficial for this task. Using a combination of emphatic, language-specific, and Twitter-specific features for supervised learning resulted in 87{\%} accuracy (F1) for cross-validation of the training set and 63.4{\%} accuracy when using the test set. Our analysis shows that features specific to Twitter or social media, in general, are more prevalent in tweets on controversial topics than in non-controversial ones. To test the premise of the paper, we conducted two additional sets of experiments, which led to mixed results. This finding will inform our future investigations into the relationship between language use on social media and the perceived controversiality of topics.
Cross-Lingual Classification of Topics in Political Texts
Authors: Goran Glava{\v{s}}, Federico Nanni, Simone Paolo Ponzetto
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Classification, General Classification, Text Classification, Topic Classification, Word Embeddings

In this paper, we propose an approach for cross-lingual topical coding of sentences from electoral manifestos of political parties in different languages. To this end, we exploit continuous semantic text representations and induce a joint multilingual semantic vector spaces to enable supervised learning using manually-coded sentences across different languages. Our experimental results show that classifiers trained on multilingual data yield performance boosts over monolingual topic classification.