A Short Survey on Sense-Annotated Corpora.
---
paper_title: Knowledge-based Word Sense Disambiguation using Topic Models

paper_content:

Word Sense Disambiguation is an open problem in Natural Language Processing which is particularly challenging and useful in the unsupervised setting where all the words in any given text need to be disambiguated without using any labeled data. Typically WSD systems use the sentence or a small window of words around the target word as the context for disambiguation because their computational complexity scales exponentially with the size of the context. In this paper, we leverage the formalism of topic model to design a WSD system that scales linearly with the number of words in the context. As a result, our system is able to utilize the whole document as the context for a word to be disambiguated. The proposed method is a variant of Latent Dirichlet Allocation in which the topic proportions for a document are replaced by synset proportions. We further utilize the information in the WordNet by assigning a non-uniform prior to synset distribution over words and a logistic-normal prior for document distribution over synsets. We evaluate the proposed method on Senseval-2, Senseval-3, SemEval-2007, SemEval-2013 and SemEval-2015 English All-Word WSD datasets and show that it outperforms the state-of-the-art unsupervised knowledge-based WSD system by a significant margin.
---
paper_title: Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization

paper_content:

Coarse-grained semantic categories such as supersenses have proven useful for a range of downstream tasks such as question answering or machine translation. To date, no effort has been put into integrating the supersenses into distributional word representations. We present a novel joint embedding model of words and supersenses, providing insights into the relationship between words and supersenses in the same vector space. Using these embeddings in a deep neural network model, we demonstrate that the supersense enrichment leads to a significant improvement in a range of downstream classification tasks.
---
paper_title: Random Walks for Knowledge-Based Word Sense Disambiguation

paper_content:

Word Sense Disambiguation WSD systems automatically choose the intended meaning of a word in context. In this article we present a WSD algorithm based on random walks over large Lexical Knowledge Bases LKB. We show that our algorithm performs better than other graph-based methods when run on a graph built from WordNet and eXtended WordNet. Our algorithm and LKB combination compares favorably to other knowledge-based approaches in the literature that use similar knowledge on a variety of English data sets and a data set on Spanish. We include a detailed analysis of the factors that affect the algorithm. The algorithm and the LKBs used are publicly available, and the results easily reproducible.
---
paper_title: Semi-supervised Word Sense Disambiguation with Neural Models

paper_content:

Determining the intended sense of words in text - word sense disambiguation (WSD) - is a long standing problem in natural language processing. Recently, researchers have shown promising results using word vectors extracted from a neural network language model as features in WSD algorithms. However, a simple average or concatenation of word vectors for each word in a text loses the sequential and syntactic information of the text. In this paper, we study WSD with a sequence learning neural net, LSTM, to better capture the sequential and syntactic patterns of the text. To alleviate the lack of training data in all-words WSD, we employ the same LSTM in a semi-supervised label propagation classifier. We demonstrate state-of-the-art results, especially on verbs.
---
paper_title: Semi-Supervised Word Sense Disambiguation Using Word Embeddings in General and Specific Domains

paper_content:

One of the weaknesses of current supervised word sense disambiguation (WSD) systems is that they only treat a word as a discrete entity. However, a continuous-space representation of words (word embeddings) can provide valuable information and thus improve generalization accuracy. Since word embeddings are typically obtained from unlabeled data using unsupervised methods, this method can be seen as a semi-supervised word sense disambiguation approach. This paper investigates two ways of incorporating word embeddings in a word sense disambiguation setting and evaluates these two methods on some SensEval/SemEval lexical sample and all-words tasks and also a domain-specific lexical sample task. The obtained results show that such representations consistently improve the accuracy of the selected supervised WSD system. Moreover, our experiments on a domainspecific dataset show that our supervised baseline system beats the best knowledge-based systems by a large margin.
---
paper_title: Entity Linking meets Word Sense Disambiguation: a Unified Approach

paper_content:

Entity Linking (EL) and Word Sense Disambiguation (WSD) both address the lexical ambiguity of language. But while the two tasks are pretty similar, they differ in a fundamental respect: in EL the textual mention can be linked to a named entity which may or may not contain the exact mention, while in WSD there is a perfect match between the word form (better, its lemma) and a suitable word sense. In this paper we present Babelfy, a unified graph-based approach to EL and WSD based on a loose identification of candidate meanings coupled with a densest subgraph heuristic which selects high-coherence semantic interpretations. Our experiments show state-of- the-art performances on both tasks on 6 different datasets, including a multilingual setting. Babelfy is online at http://babelfy.org
---
paper_title: Embeddings for Word Sense Disambiguation: An Evaluation Study

paper_content:

Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to their ability to capture semantic information from massive amounts of textual content. As a result, many tasks in Natural Language Processing have tried to take advantage of the potential of these distributional models. In this work, we study how word embeddings can be used in Word Sense Disambiguation, one of the oldest tasks in Natural Language Processing and Artificial Intelligence. We propose different methods through which word embeddings can be leveraged in a state-of-the-art supervised WSD system architecture, and perform a deep analysis of how different parameters affect performance. We show how a WSD system that makes use of word embeddings alone, if designed properly, can provide significant performance improvement over a state-ofthe-art WSD system that incorporates several standard WSD features.
---
paper_title: Word Sense Disambiguation using a Bidirectional LSTM

paper_content:

In this paper we present a clean, yet effective, model for word sense disambiguation. Our approach leverage a bidirectional long short-term memory network which is shared between all words. This enables the model to share statistical strength and to scale well with vocabulary size. The model is trained end-to-end, directly from the raw text to sense labels, and makes effective use of word order. We evaluate our approach on two standard datasets, using identical hyperparameter settings, which are in turn tuned on a third set of held out data. We employ no external resources (e.g. knowledge graphs, part-of-speech tagging, etc), language specific features, or hand crafted rules, but still achieve statistically equivalent results to the best state-of-the-art systems, that employ no such limitations.
---
paper_title: Automatic construction and evaluation of a large semantically enriched wikipedia

paper_content:

The hyperlink structure of Wikipedia constitutes a key resource for many Natural Language Processing tasks and applications, as it provides several million semantic annotations of entities in context. Yet only a small fraction of mentions across the entire Wikipedia corpus is linked. In this paper we present the automatic construction and evaluation of a Semantically Enriched Wikipedia (SEW) in which the overall number of linked mentions has been more than tripled solely by exploiting the structure of Wikipedia itself and the wide-coverage sense inventory of BabelNet. As a result we obtain a sense-annotated corpus with more than 200 million annotations of over 4 million different concepts and named entities. We then show that our corpus leads to competitive results on multiple tasks, such as Entity Linking and Word Similarity.
---
paper_title: ShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing

paper_content:

In this paper, we present a novel unsupervised algorithm for word sense disambiguation (WSD) at the document level. Our algorithm is inspired by a widely-used approach in the field of genetics for whole genome sequencing, known as the Shotgun sequencing technique. The proposed WSD algorithm is based on three main steps. First, a brute-force WSD algorithm is applied to short context windows (up to 10 words) selected from the document in order to generate a short list of likely sense configurations for each window. In the second step, these local sense configurations are assembled into longer composite configurations based on suffix and prefix matching. The resulted configurations are ranked by their length, and the sense of each word is chosen based on a voting scheme that considers only the top k configurations in which the word appears. We compare our algorithm with other state-of-the-art unsupervised WSD algorithms and demonstrate better performance, sometimes by a very large margin. We also show that our algorithm can yield better performance than the Most Common Sense (MCS) baseline on one data set. Moreover, our algorithm has a very small number of parameters, is robust to parameter tuning, and, unlike other bio-inspired methods, it gives a deterministic solution (it does not involve random choices).
---
paper_title: EuroSense: Automatic Harvesting of Multilingual Sense Annotations from Parallel Text

paper_content:

Parallel corpora are widely used in a variety of Natural Language Processing tasks, from Machine Translation to cross-lingual Word Sense Disambiguation, where parallel sentences can be exploited to automatically generate high-quality sense annotations on a large scale. In this paper we present EUROSENSE, a multilingual sense-annotated resource based on the joint disambiguation of the Europarl parallel corpus, with almost 123 million sense annotations for over 155 thousand distinct concepts and entities from a languageindependent unified sense inventory. We evaluate the quality of our sense annotations intrinsically and extrinsically, showing their effectiveness as training data for Word Sense Disambiguation.
---
paper_title: A Semantic Concordance

paper_content:

A semantic concordance is a textual corpus and a lexicon so combined that every substantive word in the text is linked to its appropriate sense in the lexicon. Thus it can be viewed either as a corpus in which words have been tagged syntactically and semantically, or as a lexicon in which example sentences can be found for many definitions. A semantic concordance is being constructed to use in studies of sense resolution in context (semantic disambiguation). The Brown Corpus is the text and WordNet is the lexicon. Semantic tags (pointers to WordNet synsets) are inserted in the text manually using an interface, ConText, that was designed to facilitate the task. Another interface supports searches of the tagged text. Some practical uses for semantic concordances am proposed.
---
paper_title: WebCAGe – A Web-Harvested Corpus Annotated with GermaNet Senses

paper_content:

This paper describes an automatic method for creating a domain-independent sense-annotated corpus harvested from the web. As a proof of concept, this method has been applied to German, a language for which sense-annotated corpora are still in short supply. The sense inventory is taken from the German wordnet GermaNet. The web-harvesting relies on an existing mapping of GermaNet to the German version of the web-based dictionary Wiktionary. The data obtained by this method constitute WebCAGe (short for: Web-Harvested Corpus Annotated with GermaNet Senses), a resource which currently represents the largest sense-annotated corpus available for German. While the present paper focuses on one particular language, the method as such is language-independent.
---
paper_title: Turing ’ s Dream and the Knowledge Challenge

paper_content:

There is a set of clear-cut challenges, all centering around knowledge, that have received insufficient attention in AI, and whose solution could bring the realization of Turing's dream - the dream of a machine we can talk with just like a person, and which is therefore (at least) our intellectual equal. These challenges have to do with the representation of linguistically expressible knowledge, the role of knowledge in language understanding, the use of knowledge for several sorts of commonsense reasoning, and knowledge accumulation. Concerning the last topic, I briefly present preliminary results of some of our recent efforts to extract "shallow" general knowledge about the world from large text corpora.
---
paper_title: SensEmbed: Learning Sense Embeddings for Word and Relational Similarity

paper_content:

Word embeddings have recently gained considerable popularity for modeling words in different Natural Language Processing (NLP) tasks including semantic similarity measurement. However, notwithstanding their success, word embeddings are by their very nature unable to capture polysemy, as different meanings of a word are conflated into a single representation. In addition, their learning process usually relies on massive corpora only, preventing them from taking advantage of structured knowledge. We address both issues by proposing a multifaceted approach that transforms word embeddings to the sense level and leverages knowledge from a large semantic network for effective semantic similarity measurement. We evaluate our approach on word similarity and relational similarity frameworks, reporting state-of-the-art performance on multiple datasets.
---
paper_title: It Makes Sense: A Wide-Coverage Word Sense Disambiguation System for Free Text

paper_content:

Word sense disambiguation (WSD) systems based on supervised learning achieved the best performance in SensEval and SemEval workshops. However, there are few publicly available open source WSD systems. This limits the use of WSD in other applications, especially for researchers whose research interests are not in WSD. ::: ::: In this paper, we present IMS, a supervised English all-words WSD system. The flexible framework of IMS allows users to integrate different preprocessing tools, additional features, and different classifiers. By default, we use linear support vector machines as the classifier with multiple knowledge-based features. In our implementation, IMS achieves state-of-the-art results on several SensEval and SemEval tasks.
---
paper_title: Personalizing PageRank for Word Sense Disambiguation

paper_content:

In this paper we propose a new graph-based method that uses the knowledge in a LKB (based on WordNet) in order to perform unsupervised Word Sense Disambiguation. Our algorithm uses the full graph of the LKB efficiently, performing better than previous approaches in English all-words datasets. We also show that the algorithm can be easily ported to other languages with good results, with the only requirement of having a wordnet. In addition, we make an analysis of the performance of the algorithm, showing that it is efficient and that it could be tuned to be faster.
---
paper_title: A Unified Multilingual Semantic Representation of Concepts

paper_content:

Semantic representation lies at the core of several applications in Natural Language Processing. However, most existing semantic representation techniques cannot ::: be used effectively for the representation of individual word senses. We put forward a novel multilingual concept representation, called MUFFIN, which not only enables accurate representation of word senses in different languages, but also provides multiple advantages over existing approaches. MUFFIN represents a given concept in a unified semantic space irrespective of the language of interest, enabling cross-lingual comparison of different ::: concepts. We evaluate our approach in two different evaluation benchmarks, semantic similarity and Word Sense Disambiguation, reporting state-of-the-art performance ::: on several standard datasets.
---
paper_title: One Million Sense-Tagged Instances for Word Sense Disambiguation and Induction

paper_content:

Supervised word sense disambiguation (WSD) systems are usually the best performing systems when evaluated on standard benchmarks. However, these systems need annotated training data to function properly. While there are some publicly available open source WSD systems, very few large annotated datasets are available to the research community. The two main goals of this paper are to extract and annotate a large number of samples and release them for public use, and also to evaluate this dataset against some word sense disambiguation and induction tasks. We show that the open source IMS WSD system trained on our dataset achieves stateof-the-art results in standard disambiguation tasks and a recent word sense induction task, outperforming several task submissions and strong baselines.
---
paper_title: SENSEVAL-2: Overview

paper_content:

SENSEVAL-2: The Second International Workshop on Evaluating Word Sense Disambiguation Systems was held on July 5-6, 2001. This paper gives an overview of SENSEVAL-2, discussing the evaluation exercise, the tasks, the scoring system, and the results. It ends with some recommendations for future evaluation exercises.
---
paper_title: Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity

paper_content:

Semantic similarity is an essential component of many Natural Language Processing applications. However, prior methods for computing semantic similarity often operate at different levels, e.g., single words or entire documents, which requires adapting the method for each data type. We present a unified approach to semantic similarity that operates at multiple levels, all the way from comparing word senses to comparing text documents. Our method leverages a common probabilistic representation over word senses in order to compare different types of linguistic data. This unified representation shows state-ofthe-art performance on three tasks: semantic textual similarity, word similarity, and word sense coarsening.
---
paper_title: Embeddings for Word Sense Disambiguation: An Evaluation Study

paper_content:

Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to their ability to capture semantic information from massive amounts of textual content. As a result, many tasks in Natural Language Processing have tried to take advantage of the potential of these distributional models. In this work, we study how word embeddings can be used in Word Sense Disambiguation, one of the oldest tasks in Natural Language Processing and Artificial Intelligence. We propose different methods through which word embeddings can be leveraged in a state-of-the-art supervised WSD system architecture, and perform a deep analysis of how different parameters affect performance. We show how a WSD system that makes use of word embeddings alone, if designed properly, can provide significant performance improvement over a state-ofthe-art WSD system that incorporates several standard WSD features.
---
paper_title: A Semantic Concordance

paper_content:

A semantic concordance is a textual corpus and a lexicon so combined that every substantive word in the text is linked to its appropriate sense in the lexicon. Thus it can be viewed either as a corpus in which words have been tagged syntactically and semantically, or as a lexicon in which example sentences can be found for many definitions. A semantic concordance is being constructed to use in studies of sense resolution in context (semantic disambiguation). The Brown Corpus is the text and WordNet is the lexicon. Semantic tags (pointers to WordNet synsets) are inserted in the text manually using an interface, ConText, that was designed to facilitate the task. Another interface supports searches of the tagged text. Some practical uses for semantic concordances am proposed.
---
paper_title: Incorporating Glosses into Neural Word Sense Disambiguation

paper_content:

Word Sense Disambiguation (WSD) aims to identify the correct meaning of polysemous words in the particular context. Lexical resources like WordNet which are proved to be of great help for WSD in the knowledge-based methods. However, previous neural networks for WSD always rely on massive labeled data (context), ignoring lexical resources like glosses (sense definitions). In this paper, we integrate the context and glosses of the target word into a unified framework in order to make full use of both labeled data and lexical knowledge. Therefore, we propose GAS: a gloss-augmented WSD neural network which jointly encodes the context and glosses of the target word. GAS models the semantic relationship between the context and the gloss in an improved memory network framework, which breaks the barriers of the previous supervised methods and knowledge-based methods. We further extend the original gloss of word sense via its semantic relations in WordNet to enrich the gloss information. The experimental results show that our model outperforms the state-of-theart systems on several English all-words WSD datasets.
---
paper_title: MRD-based Word Sense Disambiguation: Further Extending Lesk

paper_content:

This paper reconsiders the task of MRDbased word sense disambiguation, in extending the basic Lesk algorithm to investigate the impact onWSD performance of different tokenisation schemes, scoring mechanisms, methods of gloss extension and filtering methods. In experimentation over the Lexeed Sensebank and the Japanese Senseval2 dictionary task, we demonstrate that character bigrams with sense-sensitive gloss extension over hyponyms and hypernyms enhances WSD performance.
---
paper_title: A graph-based method to improve wordnet domains

paper_content:

WordNet Domains (WND) is a lexical resource where synsets have been semi-automatically annotated with one or more domain labels from a set of 170 hierarchically organized domains. The uses of WND include the power to reduce the polysemy degree of the words, grouping those senses that belong to the same domain. This paper presents a novel automatic method to propagate domain information through WordNet. We compare both labellings (the original and the new one) allowing us to detect anomalies in the original WND labels. We also compare the quality of both resources (the original labelling and the new one) in a common Word Sense Disambiguation task. The results show that the new labelling clearly outperform the original one by a large margin.
---
paper_title: It Makes Sense: A Wide-Coverage Word Sense Disambiguation System for Free Text

paper_content:

Word sense disambiguation (WSD) systems based on supervised learning achieved the best performance in SensEval and SemEval workshops. However, there are few publicly available open source WSD systems. This limits the use of WSD in other applications, especially for researchers whose research interests are not in WSD. ::: ::: In this paper, we present IMS, a supervised English all-words WSD system. The flexible framework of IMS allows users to integrate different preprocessing tools, additional features, and different classifiers. By default, we use linear support vector machines as the classifier with multiple knowledge-based features. In our implementation, IMS achieves state-of-the-art results on several SensEval and SemEval tasks.
---
paper_title: SemEval-2015 Task 13: Multilingual All-Words Sense Disambiguation and Entity Linking

paper_content:

In this paper we present the Multilingual AllWords Sense Disambiguation and Entity Linking task. Word Sense Disambiguation (WSD) and Entity Linking (EL) are well-known problems in the Natural Language Processing field and both address the lexical ambiguity of language. Their main difference lies in the kind of meaning inventories that are used: EL uses encyclopedic knowledge, while WSD uses lexicographic information. Our aim with this task is to analyze whether, and if so, how, using a resource that integrates both kinds of inventories (i.e., BabelNet 2.5.1) might enable WSD and EL to be solved by means of similar (even, the same) methods. Moreover, we investigate this task in a multilingual setting and for some specific domains.
---
paper_title: Automatic construction and evaluation of a large semantically enriched wikipedia

paper_content:

The hyperlink structure of Wikipedia constitutes a key resource for many Natural Language Processing tasks and applications, as it provides several million semantic annotations of entities in context. Yet only a small fraction of mentions across the entire Wikipedia corpus is linked. In this paper we present the automatic construction and evaluation of a Semantically Enriched Wikipedia (SEW) in which the overall number of linked mentions has been more than tripled solely by exploiting the structure of Wikipedia itself and the wide-coverage sense inventory of BabelNet. As a result we obtain a sense-annotated corpus with more than 200 million annotations of over 4 million different concepts and named entities. We then show that our corpus leads to competitive results on multiple tasks, such as Entity Linking and Word Similarity.
---
paper_title: Entity Linking meets Word Sense Disambiguation: a Unified Approach

paper_content:

Entity Linking (EL) and Word Sense Disambiguation (WSD) both address the lexical ambiguity of language. But while the two tasks are pretty similar, they differ in a fundamental respect: in EL the textual mention can be linked to a named entity which may or may not contain the exact mention, while in WSD there is a perfect match between the word form (better, its lemma) and a suitable word sense. In this paper we present Babelfy, a unified graph-based approach to EL and WSD based on a loose identification of candidate meanings coupled with a densest subgraph heuristic which selects high-coherence semantic interpretations. Our experiments show state-of- the-art performances on both tasks on 6 different datasets, including a multilingual setting. Babelfy is online at http://babelfy.org
---
paper_title: EuroSense: Automatic Harvesting of Multilingual Sense Annotations from Parallel Text

paper_content:

Parallel corpora are widely used in a variety of Natural Language Processing tasks, from Machine Translation to cross-lingual Word Sense Disambiguation, where parallel sentences can be exploited to automatically generate high-quality sense annotations on a large scale. In this paper we present EUROSENSE, a multilingual sense-annotated resource based on the joint disambiguation of the Europarl parallel corpus, with almost 123 million sense annotations for over 155 thousand distinct concepts and entities from a languageindependent unified sense inventory. We evaluate the quality of our sense annotations intrinsically and extrinsically, showing their effectiveness as training data for Word Sense Disambiguation.
---

Title: A Short Survey on Sense-Annotated Corpora

Section 1: Introduction
Description 1: Introduce the importance of Word Sense Disambiguation (WSD) and the challenge of obtaining sense-annotated corpora.

Section 2: Sense-Annotated Corpora
Description 2: Describe the main efforts and methodologies used to compile sense-annotated corpora and provide an overview of the currently available corpora.

Section 3: WordNet
Description 3: Highlight the major sense-annotated corpora using WordNet as the sense inventory, including descriptions of SemCor, SemEval, Princeton WordNet Gloss, OntoNotes, and OMSTI.

Section 4: Wikipedia
Description 4: Discuss sense-annotated corpora derived from Wikipedia, including the methodology and coverage.

Section 5: BabelNet
Description 5: Examine the sense-annotated corpora utilizing BabelNet, emphasizing the multilingual aspect and how it integrates various dictionary and encyclopedia resources.

Section 6: Statistics
Description 6: Present a comprehensive set of global and local statistics for the sense-annotated corpora, covering aspects like number of sense annotations, ambiguity, and entropy.

Section 7: Conclusion
Description 7: Summarize the survey, listing the main findings, motivating the use of sense-annotated corpora, and suggesting future work to unify these resources.