Articles

Semantic word spaces have been very use- ful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation re- sources and more powerful models of com- position. To remedy this, we …...

Biological processes are complex phenom- ena involving a series of events that are re- lated to one another through various relation- ships. Systems that can understand and rea- son over biological processes would dramat- ically improve the performance of semantic applications involving inference such as ques- tion answering (QA) – …...

We present a new language pair agnostic ap- proach to inducing bilingual vector spaces from non-parallel data without any other re- source in a bootstrapping fashion. The pa- per systematically introduces and describes all key elements of the bootstrapping procedure: (1) starting point or seed lexicon, (2) the confi- dence …...

We present Multi-Relational Latent Seman- tic Analysis (MRLSA) which generalizes La- tent Semantic Analysis (LSA). MRLSA pro- vides an elegant approach to combining mul- tiple relations between words by construct- ing a 3-way tensor. Similar to LSA, a low- rank approximation of the tensor is derived using a tensor decomposition. …...

Pronunciation dictionaries provide a readily available parallel corpus for learning to trans- duce between character strings and phoneme strings or vice versa. Translation models can be used to derive character-level paraphrases on either side of this transduction, allowing for the automatic derivation of alternative pro- nunciations or spellings. We examine …...

Recent work has shown that compositional- distributional models using element-wise op- erations on contextual word vectors benefit from the...

The goal of our research is to distinguish vet- erinary message board posts that describe a case involving a specific patient from posts that ask a general question. We create a text classifier that incorporates automatically gen- erated attribute lists for veterinary patients to tackle this problem. Using a small …...

In this paper, we train a semantic parser that scales up to Freebase. Instead of relying on annotated logical forms, which is especially expensive to obtain at large scale, we learn from question-answer pairs. The main chal- lenge in this setting is narrowing down the huge number of possible logical …...

Social media like forums and microblogs have accumulated a huge amount of user generated content (UGC) containing human knowledge. Currently, most of UGC is listed as a whole or in pre-defined categories. This “list-based” approach is simple, but hinders users from browsing and learning knowledge of certain topics effectively. To …...

The IBM translation models have been hugely influential in statistical machine translation; they are the basis of the alignment models used in modern translation systems. Exclud- ing IBM Model 1, the IBM translation mod- els, and practically all variants proposed in the literature, have relied on the optimization of likelihood …...

Recent studies on extractive text summariza- tion formulate it as a combinatorial optimiza- tion problem such as a Knapsack Problem, a Maximum Coverage Problem or a Budgeted Median Problem. These methods successfully improved summarization quality, but they did not consider the rhetorical relations between the textual units of a source …...

In a language generation system, a content planner selects which elements must be in- cluded in the output text and the ordering be- tween them. Recent empirical approaches per- form content selection without any ordering and have thus no means to ensure that the out- put is coherent. In this …...

Lexical chains provide a representation of the lexical cohesion structure of a text. In this pa- per, we propose two lexical chain based co- hesion models to incorporate lexical cohesion into document-level statistical machine trans- lation: 1) a count cohesion model that rewards a hypothesis whenever a chain word occurs …...

We consider the challenge of learning seman- tic parsers that scale to large, open-domain problems, such as question answering with Freebase. In such settings, the sentences cover a wide variety of topics and include many phrases whose meaning is difficult to rep- resent in a fixed target ontology. For ex- …...

Extractive summarization typically uses sen- tences as summarization units. In contrast, joint compression and summarization can use smaller units such as words and phrases, re- sulting in summaries containing more infor- mation. The goal of compressive summariza- tion is to find a subset of words that max- imize the total …...

A major challenge in supervised sentence compression is making use of rich feature rep- resentations because of very scarce parallel data. We address this problem and present a method to automatically build a compres- sion corpus with hundreds of thousands of instances on which deletion-based algorithms can be trained. In …...

Often the bottleneck in document classifica- tion is finding good representations that zoom in on the most important aspects of the doc- uments. Most research uses n-gram repre- sentations, but relevant features often occur discontinuously, e.g., not...good in sentiment analysis. In this paper we present experi- ments getting experts to …...

Cross-lingual adaptation aims to learn a pre- diction model in a label-scarce target lan- guage by exploiting labeled data from a label- rich source language. An effective cross- lingual adaptation system can substantially re- duce the manual annotation effort required in many natural language processing tasks. In this paper, we …...

Feature computation and exhaustive search have significantly restricted the speed of graph-based dependency parsing. We propose a faster framework of dynamic feature selec- tion, where features are added sequentially as needed, edges are pruned early, and decisions are made online for each sentence. We model this as a sequential decision-making …...

The identification of pseudepigraphic texts – texts not written by the authors to which they are attributed – has important historical, fo- rensic and commercial applications. We in- troduce an unsupervised technique for identi- fying pseudepigrapha. The idea is to identify textual outliers in a corpus based on the pair- …...