ymoslem/acl-abstracts huggingface dataset:
Automated WordNet Construction Using Word Embeddings (2017)

We present a fully unsupervised method for automated construction of WordNets based upon recent advances in distributional representations of sentences and word-senses combined with readily available machine translation tools. The approach requires very few linguistic resources and is thus extensible to multiple target languages. To evaluate our method we construct two 600-word testsets for word-to-synset matching in French and Russian using native speakers and evaluate the performance of our method along with several other recent approaches. Our method exceeds the best language-specific and multi-lingual automated WordNets in F-score for both languages. The databases we construct for French and Russian, both languages without large publicly available manually constructed WordNets, will be publicly released along with the testsets.
Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses (2017)

Abstract words refer to things that can not be seen, heard, felt, smelled, or tasted as opposed to concrete words. Among other applications, the degree of abstractness has been shown to be a useful information for metaphor detection. Our contribution to this topic are as follows: i) we compare supervised techniques to learn and extend abstractness ratings for huge vocabularies ii) we learn and investigate norms for larger units by propagating abstractness to verb-noun pairs which lead to better metaphor detection iii) we overcome the limitation of learning a single rating per word and show that multi-sense abstractness ratings are potentially useful for metaphor detection. Finally, with this paper we publish automatically created abstractness norms for 3million English words and multi-words as well as automatically created sense specific abstractness ratings
Improving Clinical Diagnosis Inference through Integration of Structured and Unstructured Knowledge (2017)

This paper presents a novel approach to the task of automatically inferring the most probable diagnosis from a given clinical narrative. Structured Knowledge Bases (KBs) can be useful for such complex tasks but not sufficient. Hence, we leverage a vast amount of unstructured free text to integrate with structured KBs. The key innovative ideas include building a concept graph from both structured and unstructured knowledge sources and ranking the diagnosis concepts using the enhanced word embedding vectors learned from integrated sources. Experiments on the TREC CDS and HumanDx datasets showed that our methods improved the results of clinical diagnosis inference.
Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations (2017)

This paper proposes a method for classifying the type of lexical-semantic relation between a given pair of words. Given an inventory of target relationships, this task can be seen as a multi-class classification problem. We train a supervised classifier by assuming: (1) a specific type of lexical-semantic relation between a pair of words would be indicated by a carefully designed set of relation-specific similarities associated with the words; and (2) the similarities could be effectively computed by {\textquotedblleft}sense representations{\textquotedblright} (sense/concept embeddings). The experimental results show that the proposed method clearly outperforms an existing state-of-the-art method that does not utilize sense/concept embeddings, thereby demonstrating the effectiveness of the sense representations.
Supervised and unsupervised approaches to measuring usage similarity (2017)

Usage similarity (USim) is an approach to determining word meaning in context that does not rely on a sense inventory. Instead, pairs of usages of a target lemma are rated on a scale. In this paper we propose unsupervised approaches to USim based on embeddings for words, contexts, and sentences, and achieve state-of-the-art results over two USim datasets. We further consider supervised approaches to USim, and find that although they outperform unsupervised approaches, they are unable to generalize to lemmas that are unseen in the training data.
Lexical Disambiguation of Igbo using Diacritic Restoration (2017)

Properly written texts in Igbo, a low-resource African language, are rich in both orthographic and tonal diacritics. Diacritics are essential in capturing the distinctions in pronunciation and meaning of words, as well as in lexical disambiguation. Unfortunately, most electronic texts in diacritic languages are written without diacritics. This makes diacritic restoration a necessary step in corpus building and language processing tasks for languages with diacritics. In our previous work, we built some n-gram models with simple smoothing techniques based on a closed-world assumption. However, as a classification task, diacritic restoration is well suited for and will be more generalisable with machine learning. This paper, therefore, presents a more standard approach to dealing with the task which involves the application of machine learning algorithms.
Creating and Validating Multilingual Semantic Representations for Six Languages: Expert versus Non-Expert Crowds (2017)

Creating high-quality wide-coverage multilingual semantic lexicons to support knowledge-based approaches is a challenging time-consuming manual task. This has traditionally been performed by linguistic experts: a slow and expensive process. We present an experiment in which we adapt and evaluate crowdsourcing methods employing native speakers to generate a list of coarse-grained senses under a common multilingual semantic taxonomy for sets of words in six languages. 451 non-experts (including 427 Mechanical Turk workers) and 15 expert participants semantically annotated 250 words manually for Arabic, Chinese, English, Italian, Portuguese and Urdu lexicons. In order to avoid erroneous (spam) crowdsourced results, we used a novel task-specific two-phase filtering process where users were asked to identify synonyms in the target language, and remove erroneous senses.
Using Linked Disambiguated Distributional Networks for Word Sense Disambiguation (2017)

We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on a resource that links two types of sense-aware lexical networks: one is induced from a corpus using distributional semantics, the other is manually constructed. The combination of two networks reduces the sparsity of sense representations used for WSD. We evaluate these enriched representations within two lexical sample sense disambiguation benchmarks. Our results indicate that (1) features extracted from the corpus-based resource help to significantly outperform a model based solely on the lexical resource; (2) our method achieves results comparable or better to four state-of-the-art unsupervised knowledge-based WSD systems including three hybrid systems that also rely on text corpora. In contrast to these hybrid methods, our approach does not require access to web search engines, texts mapped to a sense inventory, or machine translation systems.
One Representation per Word - Does it make Sense for Composition? (2017)

In this paper, we investigate whether an a priori disambiguation of word senses is strictly necessary or whether the meaning of a word in context can be disambiguated through composition alone. We evaluate the performance of off-the-shelf single-vector and multi-sense vector models on a benchmark phrase similarity task and a novel task for word-sense discrimination. We find that single-sense vector models perform as well or better than multi-sense vector models despite arguably less clean elementary representations. Our findings furthermore show that simple composition functions such as pointwise addition are able to recover sense specific information from a single-sense vector model remarkably well.
Elucidating Conceptual Properties from Word Embeddings (2017)

In this paper, we introduce a method of identifying the components (i.e. dimensions) of word embeddings that strongly signifies properties of a word. By elucidating such properties hidden in word embeddings, we could make word embeddings more interpretable, and also could perform property-based meaning comparison. With the capability, we can answer questions like {\textquotedblleft}To what degree a given word has the property cuteness?{\textquotedblright} or {\textquotedblleft}In what perspective two words are similar?{\textquotedblright}. We verify our method by examining how the strength of property-signifying components correlates with the degree of prototypicality of a target word.
TTCS$^\mathcalE$: a Vectorial Resource for Computing Conceptual Similarity (2017)

In this paper we introduce the TTCS$^{\mathcal{E}}$, a linguistic resource that relies on BabelNet, NASARI and ConceptNet, that has now been used to compute the conceptual similarity between concept pairs. The conceptual representation herein provides uniform access to concepts based on BabelNet synset IDs, and consists of a vector-based semantic representation which is compliant with the Conceptual Spaces, a geometric framework for common-sense knowledge representation and reasoning. The TTCS$^{\mathcal{E}}$ has been evaluated in a preliminary experimentation on a conceptual similarity task.
Measuring the Italian-English lexical gap for action verbs and its impact on translation (2017)

This paper describes a method to measure the lexical gap of action verbs in Italian and English by using the IMAGACT ontology of action. The fine-grained categorization of action concepts of the data source allowed to have wide overview of the relation between concepts in the two languages. The calculated lexical gap for both English and Italian is about 30{\%} of the action concepts, much higher than previous results. Beyond this general numbers a deeper analysis has been performed in order to evaluate the impact that lexical gaps can have on translation. In particular a distinction has been made between the cases in which the presence of a lexical gap affects translation correctness and completeness at a semantic level. The results highlight a high percentage of concepts that can be considered hard to translate (about 18{\%} from English to Italian and 20{\%} from Italian to English) and confirms that action verbs are a critical lexical class for translation tasks.
Word Sense Filtering Improves Embedding-Based Lexical Substitution (2017)

The role of word sense disambiguation in lexical substitution has been questioned due to the high performance of vector space models which propose good substitutes without explicitly accounting for sense. We show that a filtering mechanism based on a sense inventory optimized for substitutability can improve the results of these models. Our sense inventory is constructed using a clustering method which generates paraphrase clusters that are congruent with lexical substitution annotations in a development set. The results show that lexical substitution can still benefit from senses which can improve the output of vector space paraphrase ranking models.
Supervised and Unsupervised Word Sense Disambiguation on Word Embedding Vectors of Unambigous Synonyms (2017)

This paper compares two approaches to word sense disambiguation using word embeddings trained on unambiguous synonyms. The first is unsupervised method based on computing log probability from sequences of word embedding vectors, taking into account ambiguous word senses and guessing correct sense from context. The second method is supervised. We use a multilayer neural network model to learn a context-sensitive transformation that maps an input vector of ambiguous word into an output vector representing its sense. We evaluate both methods on corpora with manual annotations of word senses from the Polish wordnet (plWordnet).
The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings (2017)

We motivate and describe a new freely available human-human dialogue data set for interactive learning of visually grounded word meanings through ostensive definition by a tutor to a learner. The data has been collected using a novel, character-by-character variant of the DiET chat tool (Healey et al., 2003; anon.) with a novel task, where a Learner needs to learn invented visual attribute words (such as {\textquotedblleft}burchak{\textquotedblright} for square) from a tutor. As such, the text-based interactions closely resemble face-to-face conversation and thus contain many of the linguistic phenomena encountered in natural, spontaneous dialogue. These include self- and other-correction, mid-sentence continuations, interruptions, turn overlaps, fillers, hedges and many kinds of ellipsis. We also present a generic n-gram framework for building user (i.e. tutor) simulations from this type of incremental dialogue data, which is freely available to researchers. We show that the simulations produce outputs that are similar to the original data (e.g. 78{\%} turn match similarity). Finally, we train and evaluate a Reinforcement Learning dialogue control agent for learning visually grounded word meanings, trained from the BURCHAK corpus. The learned policy shows comparable performance to a rule-based system built previously.
The Use of Object Labels and Spatial Prepositions as Keywords in a Web-Retrieval-Based Image Caption Generation System (2017)

In this paper, a retrieval-based caption generation system that searches the web for suitable image descriptions is studied. Google`s reverse image search is used to find potentially relevant web multimedia content for query images. Sentences are extracted from web pages and the likelihood of the descriptions is computed to select one sentence from the retrieved text documents. The search mechanism is modified to replace the caption generated by Google with a caption composed of labels and spatial prepositions as part of the query`s text alongside the image. The object labels are obtained using an off-the-shelf R-CNN and a machine learning model is developed to predict the prepositions. The effect on the caption generation system performance when using the generated text is investigated. Both human evaluations and automatic metrics are used to evaluate the retrieved descriptions. Results show that the web-retrieval-based approach performed better when describing single-object images with sentences extracted from stock photography websites. On the other hand, images with two image objects were better described with template-generated sentences composed of object labels and prepositions.
Learning to Recognize Animals by Watching Documentaries: Using Subtitles as Weak Supervision (2017)

We investigate animal recognition models learned from wildlife video documentaries by using the weak supervision of the textual subtitles. This is a particularly challenging setting, since i) the animals occur in their natural habitat and are often largely occluded and ii) subtitles are to a large degree complementary to the visual content, providing a very weak supervisory signal. This is in contrast to most work on integrated vision and language in the literature, where textual descriptions are tightly linked to the image content, and often generated in a curated fashion for the task at hand. In particular, we investigate different image representations and models, including a support vector machine on top of activations of a pretrained convolutional neural network, as well as a Naive Bayes framework on a {\textquoteleft}bag-of-activations' image representation, where each element of the bag is considered separately. This representation allows key components in the image to be isolated, in spite of largely varying backgrounds and image clutter, without an object detection or image segmentation step. The methods are evaluated based on how well they transfer to unseen camera-trap images captured across diverse topographical regions under different environmental conditions and illumination settings, involving a large domain shift.
Human Evaluation of Multi-modal Neural Machine Translation: A Case-Study on E-Commerce Listing Titles (2017)

In this paper, we study how humans perceive the use of images as an additional knowledge source to machine-translate user-generated product listings in an e-commerce company. We conduct a human evaluation where we assess how a multi-modal neural machine translation (NMT) model compares to two text-only approaches: a conventional state-of-the-art attention-based NMT and a phrase-based statistical machine translation (PBSMT) model. We evaluate translations obtained with different systems and also discuss the data set of user-generated product listings, which in our case comprises both product listings and associated images. We found that humans preferred translations obtained with a PBSMT system to both text-only and multi-modal NMT over 56{\%} of the time. Nonetheless, human evaluators ranked translations from a multi-modal NMT model as better than those of a text-only NMT over 88{\%} of the time, which suggests that images do help NMT in this use-case.
The BreakingNews Dataset (2017)

We present BreakingNews, a novel dataset with approximately 100K news articles including images, text and captions, and enriched with heterogeneous meta-data (e.g. GPS coordinates and popularity metrics). The tenuous connection between the images and text in news data is appropriate to take work at the intersection of Computer Vision and Natural Language Processing to the next step, hence we hope this dataset will help spur progress in the field.
Automatic identification of head movements in video-recorded conversations: can words help? (2017)

We present an approach where an SVM classifier learns to classify head movements based on measurements of velocity, acceleration, and the third derivative of position with respect to time, jerk. Consequently, annotations of head movements are added to new video data. The results of the automatic annotation are evaluated against manual annotations in the same data and show an accuracy of 68{\%} with respect to these. The results also show that using jerk improves accuracy. We then conduct an investigation of the overlap between temporal sequences classified as either movement or non-movement and the speech stream of the person performing the gesture. The statistics derived from this analysis show that using word features may help increase the accuracy of the model.
Multi-Modal Fashion Product Retrieval (2017)

Finding a product in the fashion world can be a daunting task. Everyday, e-commerce sites are updating with thousands of images and their associated metadata (textual information), deepening the problem. In this paper, we leverage both the images and textual metadata and propose a joint multi-modal embedding that maps both the text and images into a common latent space. Distances in the latent space correspond to similarity between products, allowing us to effectively perform retrieval in this latent space. We compare against existing approaches and show significant improvements in retrieval tasks on a large-scale e-commerce dataset.
Metaphor Detection in a Poetry Corpus (2017)

Metaphor is indispensable in poetry. It showcases the poet`s creativity, and contributes to the overall emotional pertinence of the poem while honing its specific rhetorical impact. Previous work on metaphor detection relies on either rule-based or statistical models, none of them applied to poetry. Our method focuses on metaphor detection in a poetry corpus. It combines rule-based and statistical models (word embeddings) to develop a new classification system. Our system has achieved a precision of 0.759 and a recall of 0.804 in identifying one type of metaphor in poetry.
Machine Translation and Automated Analysis of the Sumerian Language (2017)

This paper presents a newly funded international project for machine translation and automated analysis of ancient cuneiform languages where NLP specialists and Assyriologists collaborate to create an information retrieval system for Sumerian. This research is conceived in response to the need to translate large numbers of administrative texts that are only available in transcription, in order to make them accessible to a wider audience. The methodology includes creation of a specialized NLP pipeline and also the use of linguistic linked open data to increase access to the results.
Investigating the Relationship between Literary Genres and Emotional Plot Development (2017)

Literary genres are commonly viewed as being defined in terms of content and stylistic features. In this paper, we focus on one particular class of lexical features, namely emotion information, and investigate the hypothesis that emotion-related information correlates with particular genres. Using genre classification as a testbed, we compare a model that computes lexicon-based emotion scores globally for complete stories with a model that tracks emotion arcs through stories on a subset of Project Gutenberg with five genres. Our main findings are: (a), the global emotion model is competitive with a large-vocabulary bag-of-words genre classifier (80{\%}F1); (b), the emotion arc model shows a lower performance (59 {\%} F1) but shows complementary behavior to the global model, as indicated by a very good performance of an oracle model (94 {\%} F1) and an improved performance of an ensemble model (84 {\%} F1); (c), genres differ in the extent to which stories follow the same emotional arcs, with particularly uniform behavior for anger (mystery) and fear (adventures, romance, humor, science fiction).
Enjambment Detection in a Large Diachronic Corpus of Spanish Sonnets (2017)

Enjambment takes place when a syntactic unit is broken up across two lines of poetry, giving rise to different stylistic effects. In Spanish literary studies, there are unclear points about the types of stylistic effects that can arise, and under which linguistic conditions. To systematically gather evidence about this, we developed a system to automatically identify enjambment (and its type) in Spanish. For evaluation, we manually annotated a reference corpus covering different periods. As a scholarly corpus to apply the tool, from public HTML sources we created a diachronic corpus covering four centuries of sonnets (3750 poems), and we analyzed the occurrence of enjambment across stanzaic boundaries in different periods. Besides, we found examples that highlight limitations in current definitions of enjambment.
Plotting Markson`s \textquotedblleftMistress\textquotedblright (2017)

The post-modern novel {\textquotedblleft}Wittgenstein`s Mistress{\textquotedblright} by David Markson (1988) presents the reader with a very challenging non-linear narrative, that itself appears to one of the novel`s themes. We present a distant reading of this work designed to complement a close reading of it by David Foster Wallace (1990). Using a combination of text analysis, entity recognition and networks, we plot repetitive structures in the novel`s narrative relating them to its critical analysis.
Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German (2017)

In this paper, we present the annotation challenges we have encountered when working on a historical language that was undergoing elaboration processes. We especially focus on syntactic ambiguity and gradience in Middle Low German, which causes uncertainty to some extent. Since current annotation tools consider construction contexts and the dynamics of the grammaticalization only partially, we plan to extend CorA - a web-based annotation tool for historical and other non-standard language data - to capture elaboration phenomena and annotator unsureness. Moreover, we seek to interactively learn morphological as well as syntactic annotations.