This paper presents several strategies to automatically obtain additional examples for incontext learning, effectively transforming relation extraction from a 1-shot to a few-shot setting.Specifically, we introduce a novel strategy for example selection, in which new examples are selected based on the similarity of their underlying syntactic-semantic structure to the provided 1-shot example.We show that our strategy results in complementary word choices and sentence structures compared to LLM-generated examples.When both strategies are combined, the resulting hybrid system achieves a more holistic picture of the relations of interest than either method alone.Our framework transfers well across datasets (FS-TACRED and FS-FewRel) and LLM families (Qwen and Gemma).Overall, our hybrid system consistently outperforms alternative strategies achieving state-of-the-art performance on FS-TACRED and strong gains on a customized FewRel subset.

Mayank Singh, Vikas Yadav, Shiva Krishna Reddy Malay, Shravan Nayak, Sai Rajeswar, Sathwik Tejaswi Madhusudhan, Eduardo Blanco. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Md Nayem Uddin, Amir Saeidi, Divij Handa, Agastya Seth, Tran Cao Son, Eduardo Blanco, Steven Corman, Chitta Baral. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Counterspeech is a targeted response to counteract and challenge abusive or hateful content. It effectively curbs the spread of hatred and fosters constructive online communication. Previous studies have proposed different strategies for automatically generated counterspeech. Evaluations, however, focus on relevance, surface form, and other shallow linguistic characteristics. This paper investigates the human likeness of AI-generated counterspeech, a critical factor influencing effectiveness. We implement and evaluate several LLM-based generation strategies, and discover that AI-generated and human-written counterspeech can be easily distinguished by both simple classifiers and humans. Further, we reveal differences in linguistic characteristics, politeness, and specificity. The dataset used in this study is publicly available for further research.

Large Language Models (LLMs) have revolutionized natural language processing, yet remain vulnerable to jailbreak attacks-particularly multi-turn jailbreaks that distribute malicious intent across benign exchanges, thereby bypassing alignment mechanisms.Existing approaches often suffer from limited exploration of the adversarial space, rely on hand-crafted heuristics, or lack systematic query refinement.We propose NEXUS (Network Exploration for eXploiting Unsafe Sequences), a modular framework for constructing, refining, and executing optimized multi-turn attacks.NEXUS comprises: (1) ThoughtNet, which hierarchically expands a harmful intent into a structured semantic network of topics, entities, and query chains;(2) a feedback-driven Simulator that iteratively refines and prunes these chains through attacker-victim-judge LLM collaboration using harmfulness and semantic-similarity benchmarks; and (3) a Network Traverser that adaptively navigates the refined query space for real-time attacks.This pipeline systematically uncovers stealthy, high-success adversarial paths across LLMs.Our experimental results on several closed-source and open-source LLMs show that NEXUS can achieve a higher attack success rate, between 2.1% and 19.4%, compared to state-of-the-art approaches.Our source code is available at github.com/inspire-lab/NEXUS.

Distinguishing between rhetorical questions and informational questions is a challenging task, as many rhetorical questions have similar surface forms to informational questions.Existing datasets, however, do not contain many questions that can be rhetorical or informational in different contexts.We introduce Studying Rhetorically Ambiguous Questions (SRAQ), a new dataset explicitly constructed to support the study of such rhetorical ambiguity.The questions in SRAQ can be interpreted as either rhetorical or informational depending on the context.We evaluate the performance of state-of-the-art language models on this dataset and find that they struggle to recognize many rhetorical questions.

People often ask questions with false assumptions, a type of question that does not have regular answers.Answering such questions requires first identifying the false assumptions.Large Language Models (LLMs) often generate misleading answers to these questions because of hallucinations.In this paper, we focus on identifying and answering questions with false assumptions in several domains.We first investigate whether the problem reduces to fact verification.Then, we present an approach leveraging external evidence to mitigate hallucinations.Experiments with five LLMs demonstrate that (1) incorporating retrieved evidence is beneficial and (2) generating and validating atomic assumptions yields more improvements and provides an interpretable answer by pinpointing the false assumptions.Evidence: The 2020 Summer Olympics, […],

This paper focuses on the task of measuring and forecasting incivility in conversations following replies to hate speech. Identifying replies that steer conversations away from hatred and elicit civil follow-up conversations sheds light into effective strategies to engage with hate speech and proactively avoid further escalation. We propose new metrics that take into account various dimensions of antisocial and prosocial behaviors to measure the conversation incivility following replies to hate speech. Our best metric aligns with human perceptions better than prior work. Additionally, we present analyses on a) the language of antisocial and prosocial posts, b) the relationship between antisocial or prosocial posts and user interactions, and c) the language of replies to hate speech that elicit follow-up conversations with different incivility levels. We show that forecasting the incivility level of conversations following a reply to hate speech is a challenging task. We also present qualitative analyses to identify the most common errors made by our best model.

Enfa Fane, Md Nayem Uddin, Oghenevovwe Ikumariegbe, Daniyal Kashif, Eduardo Blanco, Steven Corman. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

MohammadHossein Rezaei, Eduardo Blanco. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

This paper presents a question-answering approach to extract document-level event-argument structures. We automatically ask and answer questions for each argument type an event may have. Questions are generated using manually defined templates and generative transformers. Template-based questions are generated using predefined role-specific wh-words and event triggers from the context document. Transformer-based questions are generated using large language models trained to formulate questions based on a passage and the expected answer. Additionally, we develop novel data augmentation strategies specialized in inter-sentential event-argument relations. We use a simple span-swapping technique, coreference resolution, and large language models to augment the training instances. Our approach enables transfer learning without any corpora-specific modifications and yields competitive results with the RAMS dataset. It outperforms previous work, and it is especially beneficial to extract arguments that appear in different sentences than the event trigger. We also present detailed quantitative and qualitative analyses shedding light on the most common errors made by our best model.

In this paper, we investigate and evaluate large language models’ capability in location prediction. We present experimental results with four models—FLAN-T5, FLAN-UL2, FLAN-Alpaca, and ChatGPT—in various instruction finetuning and exemplar settings. We analyze whether taking into account the context—tweets published before and after the tweet mentioning a location—is beneficial. Additionally, we conduct an ablation study to explore whether instruction modification is beneficial. Lastly, our qualitative analysis sheds light on the errors made by the best-performing model.

Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Peng Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Ranran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Sanjay Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip S. Yu, Wenpeng Yin. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Automatic counterspeech generation methods have been developed to assist efforts in combating hate speech.Existing research focuses on generating counterspeech with linguistic attributes such as being polite, informative, and intent-driven.However, the real impact of counterspeech in online environments is seldom considered.This study aims to develop methods for generating counterspeech constrained by conversation outcomes and evaluate their effectiveness.We experiment with large language models (LLMs) to incorporate into the text generation process two desired conversation outcomes: low conversation incivility and nonhateful hater reentry.Specifically, we experiment with instruction prompts, LLM finetuning, and LLM reinforcement learning (RL).Evaluation results show that our methods effectively steer the generation of counterspeech towards the desired outcomes.Our analyses, however, show that there are differences in the quality and style depending on the model.

User-generated counter hate speech is a promising means to combat hate speech, but questions about whether it can stop incivility in follow-up conversations linger. We argue that effective counter hate speech stops incivility from emerging in follow-up conversations—counter hate that elicits more incivility is counterproductive. This study introduces the task of predicting the incivility of conversations following replies to hate speech. We first propose a metric to measure conversation incivility based on the number of civil and uncivil comments as well as the unique authors involved in the discourse. Our metric approximates human judgments more accurately than previous metrics. We then use the metric to evaluate the outcomes of replies to hate speech. A linguistic analysis uncovers the differences in the language of replies that elicit follow-up conversations with high and low incivility. Experimental results show that forecasting incivility is challenging. We close with a qualitative analysis shedding light into the most common errors made by the best model.

Md Nayem Uddin, Enfa Rose George, Eduardo Blanco, Steven Corman. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

MOTIVATION: O-linked glycosylation, an essential post-translational modification process in Homo sapiens, involves attaching sugar moieties to the oxygen atoms of serine and/or threonine residues. It influences various biological and cellular functions. While threonine or serine residues within protein sequences are potential sites for O-linked glycosylation, not all serine and/or threonine residues undergo this modification, underscoring the importance of characterizing its occurrence. This study presents a novel approach for predicting intracellular and extracellular O-linked glycosylation events on proteins, which are crucial for comprehending cellular processes. Two base multi-layer perceptron models were trained by leveraging a stacked generalization framework. These base models respectively use ProtT5 and Ankh O-linked glycosylation site-specific embeddings whose combined predictions are used to train the meta-multi-layer perceptron model. Trained on extensive O-linked glycosylation datasets, the stacked-generalization model demonstrated high predictive performance on independent test datasets. Furthermore, the study emphasizes the distinction between nucleocytoplasmic and extracellular O-linked glycosylation, offering insights into their functional implications that were overlooked in previous studies. By integrating the protein language model's embedding with stacked generalization techniques, this approach enhances predictive accuracy of O-linked glycosylation events and illuminates the intricate roles of O-linked glycosylation in proteomics, potentially accelerating the discovery of novel glycosylation sites. RESULTS: Stack-OglyPred-PLM produces Sensitivity, Specificity, Matthews Correlation Coefficient, and Accuracy of 90.50%, 89.60%, 0.464, and 89.70%, respectively on a benchmark NetOGlyc-4.0 independent test dataset. These results demonstrate that Stack-OglyPred-PLM is a robust computational tool to predict O-linked glycosylation sites in proteins. AVAILABILITY AND IMPLEMENTATION: The developed tool, programs, training, and test dataset are available at https://github.com/PakhrinLab/Stack-OglyPred-PLM.

We explore authentic counterhate arguments for online hateful content toward individuals.Previous efforts are limited to counterhate to fight against hateful content toward groups.Thus, we present a corpus of 54,816 hateful tweet-paragraph pairs, where the paragraphs are candidate counterhate arguments.The counterhate arguments are retrieved from 2,500 online articles from multiple sources.We propose a methodology that assures the authenticity of the counter argument and its specificity to the individual of interest.We show that finding arguments in online articles is an efficient alternative to counterhate generation approaches that may hallucinate unsupported arguments.We also present linguistic insights on the language used in counterhate arguments.Experimental results show promising results.It is more challenging, however, to identify counterhate arguments for hateful content toward individuals not included in the training set.

Countering rather than censoring hate speech has emerged as a promising strategy to address hatred.There are many types of counterspeech in user-generated content: addressing the hateful content or its author, generic requests, well-reasoned counter arguments, insults, etc.The effectiveness of counterspeech, which we define as subsequent incivility, depends on these types.In this paper, we present a theoretically grounded taxonomy of replies to hate speech and a new corpus.We work with real, user-generated hate speech and all the replies it elicits rather than replies generated by a third party.Our analyses provide insights into the content real users reply with as well as which replies are empirically most effective.We also experiment with models to characterize the replies to hate speech, thereby opening the door to estimating whether a reply to hate speech will result in further incivility. Error Type % ExampleGround Truth Predicted Rhetorical 26 Hate: F**k worthless inbreds who've contributed nothing to society.

Recent studies in the hate and counter hate domain have provided the grounds for investigating how to detect this pervasive content in social media. These studies mostly work with synthetic replies to hateful content written by annotators on demand rather than replies written by real users. We argue that working with naturally occurring replies to hateful content is key to study the problem. Building on this motivation, we create a corpus of 5,652 hateful tweets and replies. We analyze their fine-grained relationships by indicating whether the reply (a) is hate or counter hate speech, (b) provides a justification, (c) attacks the author of the tweet, and (d) adds additional hate. We also present linguistic insights into the language people use depending on these fine-grained relationships. Experimental results show improvements (a) taking into account the hateful tweet in addition to the reply and (b) pretraining with related tasks.

This paper introduces the problem of determining whether people are located in the places they mention in their tweets. In particular, we investigate the role of text and images to solve this challenging problem. We present a new corpus of tweets that contain both text and images. Our analyses show that this problem is multimodal at its core: human judgments depend on whether annotators have access to the text, the image, or both. Experimental results show that a neural architecture that combines both modalities yields better results. We also conduct an error analysis to provide insights into why and when each modality is beneficial.

Negation poses a challenge in many natural language understanding tasks.Inspired by the fact that understanding a negated statement often requires humans to infer affirmative interpretations, in this paper we show that doing so benefits models for three natural language understanding tasks.We present an automated procedure to collect pairs of sentences with negation and their affirmative interpretations, resulting in over 150,000 pairs.Experimental results show that leveraging these pairs helps (a) T5 generate affirmative interpretations from negations in a previous benchmark, and (b) a RoBERTa-based classifier solve the task of natural language inference.We also leverage our pairs to build a plug-and-play neural generator that given a negated statement generates an affirmative interpretation.Then, we incorporate the pretrained generator into a RoBERTa-based classifier for sentiment analysis and show that doing so improves the results.Crucially, our proposal does not require any manual effort.* Work was done prior to joining Amazon. English-Norwegian (en-no) parallel sentences: (en)There is no more than one Truth.(no) Og det finnes kun en Sannhet.Backtranslation: And there is only one truth.English-Spanish (en-es) parallel sentences: (en) The term gained traction only after 1999.(es) El término no se popularizó hasta después del 1999.

Krishna Sanagavarapu, Jathin Singaraju, Anusha Kakileti, Anirudh Kaza, Aaron Mathews, Helen Li, Nathan Brito, Eduardo Blanco. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Hate speech is plaguing the cyberspace along with user-generated content.This paper investigates the role of conversational context in the annotation and detection of online hate and counter speech, where context is defined as the preceding comment in a conversation thread.We created a context-aware dataset for a 3-way classification task on Reddit comments: hate speech, counter speech, or neutral.Our analyses indicate that context is critical to identify hate and counter speech: human judgments change for most comments depending on whether we show annotators the context.A linguistic analysis draws insights into the language people use to express hate and counter speech.Experimental results show that neural networks obtain significantly better results if context is taken into account.We also present qualitative error analyses shedding light into (a) when and why context is beneficial and (b) the remaining errors made by our best model when context is taken into account.

Abstract This paper presents a corpus and experiments to mine possession relations from text. Specifically, we target alienable and control possessions and assign temporal anchors indicating when a possession relation holds between the possessor and possessee. We work with intra-sentential possessor and possessees that satisfy lexical and syntactic constraints. We experiment with traditional classifiers and neural networks to automate the task. In addition, we analyze the factors that help to determine possession existence and possession type and common errors made by the best performing classifiers. Experimental results show that determining possession existence relies on the entire sentence, whereas determining possession type primarily relies on the verb, possessor and possessee.

Abstract Negation is a complex linguistic phenomenon present in all human languages. It can be seen as an operator that transforms an expression into another expression whose meaning is in some way opposed to the original expression. In this article, we survey previous work on negation with an emphasis on computational approaches. We start defining negation and two important concepts: scope and focus of negation. Then, we survey work in natural language processing that considers negation primarily as a means to improve the results in some task. We also provide information about corpora containing negation annotations in English and other languages, which usually include a combination of annotations of negation cues, scopes, foci, and negated events. We continue the survey with a description of automated approaches to process negation, ranging from early rule-based systems to systems built with traditional machine learning and neural networks. Finally, we conclude with some reflections on current progress and future directions.

Abstract This article presents a two-step methodology to annotate temporally anchored spatial knowledge on top of OntoNotes. We first generate potential knowledge using semantic roles or syntactic dependencies and then crowdsource annotations to validate the potential knowledge. The resulting annotations indicate how long entities are or are not located somewhere and temporally anchor this spatial information. We present an in-depth corpus analysis comparing the spatial knowledge generated by manipulating roles or dependencies. Experiments show that working with syntactic dependencies instead of semantic roles allows us to generate more potential entity-related spatial knowledge and obtain better results in a realistic scenario, that is, with predicted linguistic information.

Abstract On top of a neural network-based dependency parser and a graph-based natural language processing module, we design a Prolog-based dialog engine that explores interactively a ranked fact database extracted from a text document. We reorganize dependency graphs to focus on the most relevant content elements of a sentence and integrate sentence identifiers as graph nodes. Additionally, after ranking the graph, we take advantage of the implicit semantic information that dependency links and WordNet bring in the form of subject–verb–object, “is-a” and “part-of” relations. Working on the Prolog facts and their inferred consequences, the dialog engine specializes the text graph with respect to a query and reveals interactively the document’s most relevant content elements. The open-source code of the integrated system is available at https://github.com/ptarau/DeepRank .

This paper introduces two tasks: determining (a) the duration of possession relations and (b) co-possessions, i.e., whether multiple possessors possess a possessee at the same time.We present new annotations on top of corpora annotating possession existence, and experimental results.Regarding possession duration, we derive the time spans we work with empirically from annotations indicating lower and upper bounds.Regarding co-possessions, we use a binary label.Cohen's kappa coefficients indicate substantial agreement, and experimental results show that text is more useful than the image for solving these tasks.* Work done at the University of North Texas Figure 1: Sample tweet with text and an image.The author of the tweet possesses the cup for a few weeks or months.The tweet does not indicate a co-possession.

The focus of a negation is the set of tokens intended to be negated, and a key component for revealing affirmative alternatives to negated utterances.In this paper, we experiment with neural networks to predict the focus of negation.Our main novelty is leveraging a scope detector to introduce the scope of negation as an additional input to the network.Experimental results show that doing so obtains the best results to date.Additionally, we perform a detailed error analysis providing insights into the main error categories, and analyze errors depending on whether the model takes into account scope and context information.

Jordan Sanders, Meghana Gudala, Kathleen Hamilton, Nishtha Prasad, Jordan Stovall, Eduardo Blanco, Jane E Hamilton, Kirk Roberts. Proceedings of the 28th International Conference on Computational Linguistics. 2020.

Negation is underrepresented in existing natural language inference benchmarks.Additionally, one can often ignore the few negations in existing benchmarks and still make the right inference judgments.In this paper, we present a new benchmark for natural language inference in which negation plays an important role.We also show that state-of-the-art transformers struggle making inference judgments with the new pairs. Original pairNew pair w/ negation RTE T: Tropical Storm Debby is blamed for several deaths across the Caribbean.T neg : Tropical Storm Debby is not blamed for several deaths across the Caribbean.H: A tropical storm has caused loss of life.H neg : A tropical storm has not caused loss of life.

This paper presents WikiPossessions, a new benchmark corpus for the task of temporally-oriented possession (TOP), or tracking objects as they change hands over time. We annotate Wikipedia articles for 90 different well-known artifacts paintings, diamonds, and archaeological artifacts), producing 799 artifact-possessor relations with associated attributes. For each article, we also produce a full possession timeline. The full version of the task combines straightforward entity-relation extraction with complex temporal reasoning, as well as verification of textual support for the relevant types of knowledge. Specifically, to complete the full TOP task for a given article, a system must do the following: a) identify possessors; b) anchor possessors to times/events; c) identify temporal relations between each temporal anchor and the possession relation it corresponds to; d) assign certainty scores to each possessor and each temporal relation; and e) assemble individual possession events into a global possession timeline. In addition to the corpus, we release evaluation scripts and a baseline model for the task.

In this work we address the processing of negation in Spanish. We first present a machine learning system that processes negation in Spanish. Specifically, we focus on two tasks: i) negation cue detection and ii) scope identification. The corpus used in the experimental framework is the SFU Corpus. The results for cue detection outperform state-of-the-art results, whereas for scope detection this is the first system that performs the task for Spanish. Moreover, we provide a qualitative error analysis aimed at understanding the limitations of the system and showing which negation cues and scopes are straightforward to predict automatically, and which ones are challenging.

This article presents a corpus and experiments to extract spatial timelines from biographies. Spatial timelines capture where someone is and is not located, and specify when this spatial information is true. We work with 100 Wikipedia biographies, and consider intersentential (location, year) pairs as well as years that are not explicitly stated in the biographies. Experimental results show that a combination of LSTMs outperforms SVM with linguistically motivated features.

Dhivya Chinnappa, Srikala Murugan, Eduardo Blanco. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

The National Institute of Standards and Technology (NIST) has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy (NLACP) to a machine-readable form. To study the automation process, we consider the hierarchical ABAC model as our reference model since it better reflects the requirements of real-world organizations. Therefore, this paper focuses on the questions of: how can we automatically infer the hierarchical structure of an ABAC model given NLACPs; and, how can we extract and define the set of authorization attributes based on the resulting structure. To address these questions, we propose an approach built upon recent advancements in natural language processing and machine learning techniques. For such a solution, the lack of appropriate data often poses a bottleneck. Therefore, we decouple the primary contributions of this work into: (1) developing a practical framework to extract authorization attributes of hierarchical ABAC system from natural language artifacts, and (2) generating a set of realistic synthetic natural language access control policies (NLACPs) to evaluate the proposed framework. Our experimental results are promising as we achieved - in average - an F1-score of 0.96 when extracting attributes values of subjects, and 0.91 when extracting the values of objects’ attributes from natural language access control policies.

This paper presents a corpus and experimental results to extract possession relations over time.We work with Wikipedia articles about artworks, and extract possession relations along with temporal information indicating when these relations are true.The annotation scheme yields many possessors over time for a given artwork, and experimental results show that an LSTM ensemble can automate the task.

This paper presents a set of dimensions to characterize the association between two people.We distinguish between interactions (when somebody refers to somebody in a conversation) and relationships (a sequence of interactions).We work with dialogue scripts from the TV show Friends, and do not impose any restrictions on the interactions and relationships.We introduce and analyze a new corpus, and present experimental results showing that the task can be automated.

Dhivya Chinnappa, Eduardo Blanco. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

The National Institute of Standards and Technology (NIST) has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy (NLACP) to a machine-readable form. An essential step towards this automation is to automate the extraction of ABAC attributes from NLACPs, which is the focus of this paper. We, therefore, raise the question of: how can we automate the task of attributes extraction from natural language documents? Our proposed solution to this question is built upon the recent advancements in natural language processing and machine learning techniques. For such a solution, the lack of appropriate data often poses a bottleneck. Therefore, we decouple the primary contributions of this work into: (1) developing a practical framework to extract ABAC attributes from natural language artifacts, and (2) generating a set of realistic synthetic natural language access control policies (NLACPs) to evaluate the proposed framework. The experimental results are promising with regard to the potential automation of the task of interest. Using a convolutional neural network (CNN), we achieved - in average - an F1-score of 0.96 when extracting the attributes of subjects, and 0.91 when extracting the objects' attributes from natural language access control policies.

This paper presents a framework to understand negation in positive terms.Specifically, we extract positive meaning from negation when the negation cue syntactically modifies a noun or adjective.Our approach is grounded on generating potential positive interpretations automatically, and then scoring them.Experimental results show that interpretations scored high can be reliably identified.

This paper presents a corpus and experiments to determine dimensions of interpersonal relationships.We define a set of dimensions heavily inspired by work in social science.We create a corpus by retrieving pairs of people, and then annotating dimensions for their relationships.A corpus analysis shows that dimensions can be annotated reliably.Experimental results show that given a pair of people, values to dimensions can be assigned automatically.

This paper presents a framework to infer spatial knowledge from semantic role representations. We infer whether entities are or are not located somewhere, and temporally anchor this spatial information. A large crowdsourcing effort on top of OntoNotes shows that these temporally-anchored spatial inferences are ubiquitous and intuitive to humans. Experimental results show that inferences can be performed automatically and semantic features bring significant improvement.

This paper complements semantic role representations with spatial knowledge beyond indicating plain locations.Namely, we extract where entities are (and are not) located, and for how long (seconds, hours, days, etc.).Crowdsourced annotations show that this additional knowledge is intuitive to humans and can be annotated by non-experts.Experimental results show that the task can be automated.

This paper presents an approach to extract implicit interpretations from modal constructions.Importantly, our approach uses a deterministic procedure to normalize eventualities and generate potential interpretations.An annotation effort demonstrates that these interpretations are intuitive to humans and most modal constructions convey at least one interpretation.Experimental results show that the task is challenging but can be automated.

This paper presents a two-step procedure to extract positive meaning from verbal negation.We first generate potential positive interpretations manipulating syntactic dependencies.Then, we score them according to their likelihood.Manual annotations show that positive interpretations are ubiquitous and intuitive to humans.Experimental results show that dependencies are better suited than semantic roles for this task, and automation is possible.

This paper presents a methodology to extract positive interpretations from negated statements.First, we automatically generate plausible interpretations using well-known grammar rules and manipulating semantic roles.Second, we score plausible alternatives according to their likelihood.Manual annotations show that the positive interpretations are intuitive to humans, and experimental results show that the scoring task can be automated.

This paper presents a framework to infer spatial knowledge from verbal semantic role representations.First, we generate potential spatial knowledge deterministically.Second, we determine whether it can be inferred and a degree of certainty.Inferences capture that something is located or is not located somewhere, and temporally anchor this information.An annotation effort shows that inferences are ubiquitous and intuitive to humans.

This paper presents a semantic logic-based approach to determine textual similarity. Three logic form transformations taking into account semantic structure of sentences are proposed. Logic proofs are obtained using an adapted resolution step that drops predicates when a proof cannot be found with standard resolution. Features are extracted from proofs and combined using supervised machine learning to obtain the final similarity scores. Experimental results show that taking into account semantic relations to determine textual similarity yields performance improvements with respect to both baselines and third-party state-of-the-art systems. Specific sentence pairs that benefit from considering semantic relations are discussed. Detailed results provide empirical evidence that either proof direction offers a strong baseline although considering both is beneficial, and that ignoring concepts that are not an argument of a semantic relation is not sound.

This paper presents a methodology to infer implicit semantic relations from verbargument structures.An annotation effort shows implicit relations boost the amount of meaning explicitly encoded for verbs.Experimental results with automatically obtained parse trees and verb-argument structures demonstrate that inferring implicit relations is a doable task.

Abstract This paper introduces a model for capturing the meaning of negated statements by identifying the negated concepts and revealing the implicit positive meanings. A negated sentence may be represented logically in different ways depending on what is the scope and focus of negation. The novel approach introduced here identifies the focus of negation and thus eliminates erroneous interpretations. Furthermore, negation is incorporated into a framework for composing semantic relations, proposed previously, yielding a richer semantic representation of text, including hidden inferences. Annotations of negation focus were performed over PropBank, and learning features were identified. The experimental results show that the models introduced here obtain a weighted f-measure of 0.641 for predicting the focus of negation and 78 percent accuracy for incorporating negation into composition of semantic relations.

This paper presents a novel approach to determine textual similarity.A layered methodology to transform text into logic forms is proposed, and semantic features are derived from a logic prover.Experimental results show that incorporating the semantic structure of sentences is beneficial.When training data is unavailable, scores obtained from the logic prover in an unsupervised manner outperform supervised methods.

Abstract This paper presents a method for the composition of at-location with other semantic relations. The method is based on inference axioms that combine two semantic relations yielding another relation that otherwise is not expressed. An experimental study conducted on PropBank, WordNet, and eXtended WordNet shows that inferences have high accuracy. The method is applicable to combining other semantic relations and it is beneficial to many semantically intense applications.