Proceedings of the Workshop Semantic Content Acquisition and Representation (SCAR) 2007

This is the proceedings of the Workshop on Semantic Content Acquisition and Representation, held in conjunction with NODALIDA 2007, on May 24 2007 in Tartu, Estonia.</p

Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning

Deep compositional models of meaning acting on distributional representations of words in order to produce vectors of larger text constituents are evolving to a popular area of NLP research. We detail a compositional distributional framework based on a rich form of word embeddings that aims at facilitating the interactions between words in the context of a sentence. Embeddings and composition layers are jointly learned against a generic objective that enhances the vectors with syntactic information from the surrounding context. Furthermore, each word is associated with a number of senses, the most plausible of which is selected dynamically during the composition process. We evaluate the produced vectors qualitatively and quantitatively with positive results. At the sentence level, the effectiveness of the framework is demonstrated on the MSRPar task, for which we report results within the state-of-the-art range.Comment: Accepted for presentation at EMNLP 201

NASARI: a novel approach to a Semantically-Aware Representation of items

The semantic representation of individual word senses and concepts is of fundamental importance to several applications in Natural Language Processing. To date, concept modeling techniques have in the main based their representation either on lexicographic resources, such as WordNet, or on encyclopedic resources, such as Wikipedia. We propose a vector representation technique that combines the complementary knowledge of both these types of resource. Thanks to its use of explicit semantics combined with a novel cluster-based dimensionality reduction and an effective weighting scheme, our representation attains state-of-the-art performance on multiple datasets in two standard benchmarks: word similarity and sense clustering. We are releasing our vector representations at http://lcl.uniroma1.it/nasari/

Embeddings for word sense disambiguation: an evaluation study

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-of-the-art WSD system that incorporates several standard WSD features

Capturing lexical variation in MT evaluation using automatically built sense-cluster inventories

The strict character of most of the existing Machine Translation (MT) evaluation metrics does not permit them to capture lexical variation in translation. However, a central issue in MT evaluation is the high correlation that the metrics should have with human judgments of translation quality. In order to achieve a higher correlation, the identification of sense correspondences between the compared translations becomes really important. Given that most metrics are looking for exact correspondences, the evaluation results are often misleading concerning translation quality. Apart from that, existing metrics do not permit one to make a conclusive estimation of the impact of Word Sense Disambiguation techniques into MT systems. In this paper, we show how information acquired by an unsupervised semantic analysis method can be used to render MT evaluation more sensitive to lexical semantics. The sense inventories built by this data-driven method are incorporated into METEOR: they replace WordNet for evaluation in English and render METEOR’s synonymy module operable in French. The evaluation results demonstrate that the use of these inventories gives rise to an increase in the number of matches and the correlation with human judgments of translation quality, compared to precision-based metrics

Durham - a word sense disambiguation system

Ever since the 1950's when Machine Translation first began to be developed, word sense disambiguation (WSD) has been considered a problem to developers. In more recent times, all NLP tasks which are sensitive to lexical semantics potentially benefit from WSD although to what extent is largely unknown. The thesis presents a novel approach to the task of WSD on a large scale. In particular a novel knowledge source is presented named contextual information. This knowledge source adopts a sub-symbolic training mechanism to learn information from the context of a sentence which is able to aid disambiguation. The system also takes advantage of frequency information and these two knowledge sources are combined. The system is trained and tested on SEMCOR. A novel disambiguation algorithm is also developed. The algorithm must tackle the problem of a large possible number of sense combinations in a sentence. The algorithm presented aims to make an appropriate choice between accuracy and efficiency. This is performed by directing the search at a word level. The performance achieved on SEMCOR is reported and an analysis of the various components of the system is performed. The results achieved on this test data are pleasing, but are difficult to compare with most of the other work carried out in the field. For this reason the system took part in the SENSEVAL evaluation which provided an excellent opportunity to extensively compare WSD systems. SENSEVAL is a small scale WSD evaluation using the HECTOR lexicon. Despite this, few adaptations to the system were required. The performance of the system on the SENSEVAL task are reported and have also been presented in [Hawkins, 2000]

Neural Collective Entity Linking

Entity Linking aims to link entity mentions in texts to knowledge bases, and neural models have achieved recent success in this task. However, most existing methods rely on local contexts to resolve entities independently, which may usually fail due to the data sparsity of local information. To address this issue, we propose a novel neural model for collective entity linking, named as NCEL. NCEL applies Graph Convolutional Network to integrate both local contextual features and global coherence information for entity linking. To improve the computation efficiency, we approximately perform graph convolution on a subgraph of adjacent entity mentions instead of those in the entire text. We further introduce an attention scheme to improve the robustness of NCEL to data noise and train the model on Wikipedia hyperlinks to avoid overfitting and domain bias. In experiments, we evaluate NCEL on five publicly available datasets to verify the linking performance as well as generalization ability. We also conduct an extensive analysis of time complexity, the impact of key modules, and qualitative results, which demonstrate the effectiveness and efficiency of our proposed method.Comment: 12 pages, 3 figures, COLING201

Breaking Sticks and Ambiguities with Adaptive Skip-gram

Recently proposed Skip-gram model is a powerful method for learning high-dimensional word representations that capture rich semantic relationships between words. However, Skip-gram as well as most prior work on learning word representations does not take into account word ambiguity and maintain only single representation per word. Although a number of Skip-gram modifications were proposed to overcome this limitation and learn multi-prototype word representations, they either require a known number of word meanings or learn them using greedy heuristic approaches. In this paper we propose the Adaptive Skip-gram model which is a nonparametric Bayesian extension of Skip-gram capable to automatically learn the required number of representations for all words at desired semantic resolution. We derive efficient online variational learning algorithm for the model and empirically demonstrate its efficiency on word-sense induction task