Articles

We provide a generalization of discrimina- tive lexicalized shift reduce parsing tech- niques for phrase structure grammar to a wide range of morphologically rich lan- guages. The model is efficient and outper- forms recent strong baselines on almost all languages considered. It takes advantage of a dependency based modelling of …...

While agreement-based joint training has proven to deliver state-of-the-art align- ment accuracy, the produced word align- ments are usually restricted to one-to- one mappings because of the hard cons- traint on agreement. We propose a ge- neral framework to allow for arbitrary loss functions that measure the disagreement between asymmetric …...

In this paper, a turn-taking phenomenon taxonomy is introduced, organised accord- ing to the level of information conveyed. It is aimed to provide a better grasp of the behaviours used by humans while talking to each other, so that they can be method- ically replicated in spoken dialogue sys- tems. …...

Expectation-maximization algorithms, such as those implemented in GIZA++ pervade the field of unsupervised word alignment. However, these algorithms have a problem of over-fitting, leading to “garbage collector effects,” where rare towords tend to be erroneously aligned untranslated words. This paper proposes a leave-one-out expectation- maximization algorithm for unsupervised word alignment …...

A key challenge in vocabulary acquisition is learning which of the many possible meanings is appropriate for a word. The word generalization problem refers to how children associate a word such as dog with a meaning at the appropriate category level in a taxonomy of objects, such as Dalma- tians, …...

This study focuses on personality predic- tion of protagonists in novels based on the Five-Factor Model of personality. We present and publish a novel collaboratively built dataset of fictional character person- ality and design our task as a text classifi- cation problem. We incorporate a range of semantic features, including …...

Relation classification is an important re- search arena in the field of natural lan- guage processing (NLP). In this paper, we present SDP-LSTM, a novel neural net- work to classify the relation of two enti- ties in a sentence. Our neural architecture leverages the shortest dependency path (SDP) between two …...

Compositional embedding models build a representation (or embedding) for a linguistic structure based on its compo- nent word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement. The key idea is to combine both (unlexicalized) hand- crafted …...

We propose a transition-based model for joint word segmentation, POS tagging and text normalization. Different from pre- vious methods, the model can be trained on standard text corpora, overcoming the lack of annotated microblog corpora. To evaluate our model, we develop an anno- tated corpus based on microblogs. Exper- imental …...

We propose CORE, a novel matrix fac- torization model that leverages contextual information for open relation extraction. Our model is based on factorization ma- chines and integrates facts from various sources, such as knowledge bases or open information extractors, as well as the con- text in which these facts have …...

Two problems arise when using distant su- pervision for relation extraction. First, in this method, an already existing knowl- edge base is heuristically aligned to texts, and the alignment results are treated as la- beled data. However, the heuristic align- ment can fail, resulting in wrong label problem. In addition, …...

This paper presents a novel approach to au- tomatically solving arithmetic word problems. This is the first algorithmic approach that can handle arithmetic problems with multi- ple steps and operations, without depending on additional annotations or predefined tem- plates. We develop a theory for expression trees that can be used …...

Non-compositionality of multiword ex- pressions is an intriguing problem that can be the source of error in a variety of NLP tasks such as language genera- tion, machine translation and word sense disambiguation. We present methods of non-compositionality detection for En- glish noun compounds using the unsu- pervised learning of …...

Integrating vision and language has long been a dream in work on artificial intel- ligence (AI). In the past two years, we have witnessed an explosion of work that brings together vision and language from images to videos and beyond. The avail- able corpora have played a crucial role in …...

A simile is a comparison between two essentially unlike things, such as “Jane swims like a dolphin”. Similes often ex- press a positive or negative sentiment to- ward something, but recognizing the po- larity of a simile can depend heavily on world knowledge. For example, “memory like an elephant” is …...

In this paper we focus on a new problem of event coreference resolution across televi- sion news videos. Based on the observa- tion that the contents from multiple data modalities are complementary, we develop a novel approach to jointly encode effec- tive features from both closed captions and video key …...

In this work, we build an entity/event-level sentiment analysis system, which is able to recognize and infer both explicit and im- plicit sentiments toward entities and events in the text. We design Probabilistic Soft Logic models that integrate explicit senti- ments, inference rules, and +/-effect event information (events that positively …...

Opinion summarization is the task of pro- ducing the summary of a text, such that the summary also preserves the sentiment of the text. Opinion Summarization is thus a trade-off between summarization and sen- timent analysis. The demand of com- pression may drop sentiment bearing sen- tences, and the demand …...

Cross-Lingual Learning provides a mech- anism to adapt NLP tools available for la- bel rich languages to achieve similar tasks for label-scarce languages. An efficient cross-lingual tool significantly reduces the cost and effort required to manually an- notate data. In this paper, we use the Recursive Autoencoder architecture to de- …...

This paper is concerned with the task of bilingual lexicon induction using image- based features. By applying features from a convolutional neural network (CNN), we obtain state-of-the-art performance on a standard dataset, obtaining a 79% relative improvement over previous work which uses bags of visual words based on SIFT features. …...