Мақалалар

Engaging in a debate with oneself or others to take decisions is an integral part of our day-to- day life. A debate on a topic (say, use of per- formance enhancing drugs) typically proceeds by one party making an assertion/claim (say, PEDs are bad for health) and then providing an …...

Learning to determine when the time- varying facts of a Knowledge Base (KB) have to be updated is a challenging task. We propose to learn state changing verbs from Wikipedia edit history. When a state-changing event, such as a marriage or death, happens to an entity, the in- fobox on …...

Most of the current automated essay scor- ing (AES) systems are trained using manu- ally graded essays from a specific prompt. These systems experience a drop in accu- racy when used to grade an essay from a different prompt. Obtaining a large num- ber of manually graded essays each time …...

Social media represents a rich source of up- to-date information about events such as in- cidents. The sheer amount of available infor- mation makes machine learning approaches a necessity for further processing. This learn- ing problem is often concerned with region- ally restricted datasets such as data from only one …...

The paper presents the results of the first large-scale human evaluation of automatic grammatical error correction (GEC) sys- tems. Twelve participating systems and the unchanged input of the CoNLL-2014 shared task have been reassessed in a WMT-inspired human evaluation proce- dure. Methods introduced for the Work- shop of Machine Translation …...

In recent years, the task of recommending hashtags for microblogs has been given increasing attention. Various methods have been proposed to study the problem from different aspects. However, most of the recent studies have not considered the differences in the types or uses of hashtags. In this paper, we introduce …...

Summarization based on text extraction is inherently limited, but generation-style ab- stractive methods have proven challeng- ing to build. In this work, we propose a fully data-driven approach to abstrac- tive sentence summarization. Our method utilizes a local attention-based model that generates each word of the summary con- ditioned on …...

This paper proposes a graph-based read- ability assessment method using word coupling. Compared to the state-of-the- art methods such as the readability for- mulae, the word-based and feature-based methods, our method develops a coupled bag-of-words model which combines the merits of word frequencies and text fea- tures. Unlike the general …...

We propose a summarization approach for scientific articles which takes advantage of citation-context and the document discourse model. While citations have been previously used in generating scientific summaries, they lack the related context from the referenced article and therefore do not accurately reflect the article’s content. Our method overcomes the …...

Syntactic language models and N-gram language models have both been used in word ordering. In this paper, we give an empirical comparison between N-gram and syntactic language models on word or- der task. Our results show that the quality of automatically-parsed training data has a relatively small impact on syntactic …...

We present an LSTM approach to deletion-based sentence compression where the task is to translate a sentence into a sequence of zeros and ones, cor- responding to token deletion decisions. We demonstrate that even the most basic version of the system, which is given no syntactic information (no PoS or …...

Accurate dependency parsing requires large treebanks, which are only available for a few languages. We propose a method that takes advantage of shared structure across languages to build a mature parser using less training data. We propose a model for learning a shared “univer- sal” parser that operates over an …...

We present a novel method for the cross- lingual transfer of dependency parsers. Our goal is to induce a dependency parser in a target language of interest without any direct supervision: instead we as- sume access to parallel translations be- tween the target and one or more source languages, and …...

Path queries on a knowledge graph can be used to answer compositional ques- tions such as “What languages are spoken by people living in Lisbon?”. However, knowledge graphs often have missing facts (edges) which disrupts path queries. Re- cent models for knowledge base comple- tion impute missing facts by embedding …...

We present a comprehensive study of eval- uation methods for unsupervised embed- ding techniques that obtain meaningful representations of words from text. Differ- ent evaluations result in different orderings of embedding methods, calling into ques- tion the common assumption that there is one single optimal vector representation. We present new …...

In order to reduce noise in training data, most natural language crowdsourcing an- notation tasks gather redundant labels and aggregate them into an integrated label, which is provided to the classifier. How- ever, aggregation discards potentially use- ful information from linguistically am- biguous instances. For five natural language tasks, we …...

We present extensions to a continuous- state dependency parsing method that makes it applicable to morphologically rich languages. Starting with a high- performance transition-based parser that uses long short-term memory (LSTM) re- current neural networks to learn repre- sentations of the parser state, we replace lookup-based word representations with representations …...

There are two main types of word repre- sentations: low-dimensional embeddings and high-dimensional distributional vec- tors, in which each dimension corresponds to a context word. In this paper, we ini- tialize an embedding-learning model with distributional vectors. Evaluation on word similarity shows that this initialization sig- nificantly increases the quality …...

Performing link prediction in Knowledge Bases (KBs) with embedding-based mod- els, like with the model TransE (Bordes et al., 2013) which represents relationships as translations in the embedding space, have shown promising results in recent years. Most of these works are focused on modeling single relationships and hence do not …...

We study the problem of jointly em- bedding a knowledge base and a text corpus. The key issue is the alignment model making sure the vectors of entities, relations and words are in the same space. Wang et al. (2014a) rely on Wikipedia an- chors, making the applicable scope quite …...