A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Discriminative model improves bilingual lexicon induction.
Simple framework decouples word alignment and multilingual embedding mapping.
Geometric approach learns bilingual mappings from monolingual embeddings.
Paper refines cross-lingual word embeddings using Manhattan norm.
This paper discusses the effect of hubness in zero-shot learning, when ridge regression is used to find a mapping between the example space to the label space. Contrary to the existing approach, which attempts to find a mapping from the example space to the label space, we show that mapping labels into the example spac…
Geometric approach for unsupervised word embedding alignment.
We consider the task of aligning two sets of points in high dimension, which has many applications in natural language processing and computer vision. As an example, it was recently shown that it is possible to infer a bilingual lexicon, without supervised data, by aligning word embeddings trained on monolingual data. …
In lexicon-based classification, documents are assigned labels by comparing the number of words that appear from two opposed lexicons, such as positive and negative sentiment. Creating such words lists is often easier than labeling instances, and they can be debugged by non-experts if classification performance is unsa…
We introduce BilBOWA (Bilingual Bag-of-Words without Alignments), a simple and computationally-efficient model for learning bilingual distributed representations of words which can scale to large monolingual datasets and does not require word-aligned parallel training data. Instead it trains directly on monolingual dat…
We present an approach to learning multi-sense word embeddings relying both on monolingual and bilingual information. Our model consists of an encoder, which uses monolingual and bilingual context (i.e. a parallel sentence) to choose a sense for a given word, and a decoder which predicts context words based on the chos…
This paper covers the two approaches for sentiment analysis: i) lexicon based method; ii) machine learning method. We describe several techniques to implement these approaches and discuss how they can be adopted for sentiment classification of Twitter messages. We present a comparative study of different lexicon combin…
Neural approach for Chinese word segmentation using lexicon and unlabeled data.
Unsupervised machine translation---i.e., not assuming any cross-lingual supervision signal, whether a dictionary, translations, or comparable corpora---seems impossible, but nevertheless, Lample et al. (2018) recently proposed a fully unsupervised machine translation (MT) model. The model relies heavily on an adversari…
Our research extends the Bilingual Evaluation Understudy (BLEU) evaluation technique for statistical machine translation to make it more adjustable and robust. We intend to adapt it to resemble human evaluation more. We perform experiments to evaluate the performance of our technique against the primary existing evalua…
In this paper we consider two sequence tagging tasks for medieval Latin: part-of-speech tagging and lemmatization. These are both basic, yet foundational preprocessing steps in applications such as text re-use detection. Nevertheless, they are generally complicated by the considerable orthographic variation which is ty…
The paper predicts TSE stocks using social media sentiment and volume.
Paper introduces methods to integrate external knowledge into RNNs using attention mechanisms.
The use of robo-readers to analyze news texts is an emerging technology trend in computational finance. In recent research, a substantial effort has been invested to develop sophisticated financial polarity-lexicons that can be used to investigate how financial sentiments relate to future company performance. However, …
This paper describes the resource- and system-building efforts of an eight-week Johns Hopkins University Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically-Informed Machine Translation (SIMT). We describe a new modality/negation (MN) annotation schem…
Cross-language learning allows us to use training data from one language to build models for a different language. Many approaches to bilingual learning require that we have word-level alignment of sentences from parallel corpora. In this work we explore the use of autoencoder-based methods for cross-language learning …
Hybrid engine analyzes news sentiment for markets in real-time.
For decades, context-dependent phonemes have been the dominant sub-word unit for conventional acoustic modeling systems. This status quo has begun to be challenged recently by end-to-end models which seek to combine acoustic, pronunciation, and language model components into a single neural network. Such systems, which…
Study compares BERT with other sentiment analysis models.
This research tackles automatic paraphrasing without translation.
FinLlama uses a fine-tuned Llama 2 model for financial sentiment analysis.
Parallel sentences are a relatively scarce but extremely useful resource for many applications including cross-lingual retrieval and statistical machine translation. This research explores our new methodologies for mining such data from previously obtained comparable corpora. The task is highly practical since non-para…
When digitizing a print bilingual dictionary, whether via optical character recognition or manual entry, it is inevitable that errors are introduced into the electronic version that is created. We investigate automating the process of detecting errors in an XML representation of a digitized print dictionary using a hyb…
DeepSubQE estimates translation quality for subtitles, improving on existing methods.
Improved unsupervised word translation using adversarial autoencoder with cycle consistency and input reconstruction.
Proposes using Wasserstein barycenter for better multilingual alignment.
This work decouples language from math problems to enable cross-language learning.
The multilingual nature of the world makes translation a crucial requirement today. Parallel dictionaries constructed by humans are a widely-available resource, but they are limited and do not provide enough coverage for good quality translation purposes, due to out-of-vocabulary words and neologisms. This motivates th…
Transformers trained with three normalization changes outperform state-of-the-art on low-resource translation tasks.
REALFIN benchmarks financial reasoning by removing implicit assumptions, revealing model weaknesses.
MGLM models all possible language channel factorizations for improved multilingual generation.
Sentiment analysis from news and social media predicts forex market movements.
Neural network detects sex trafficking ads from escort websites.
Automated sentiment analysis and opinion mining is a complex process concerning the extraction of useful subjective information from text. The explosion of user generated content on the Web, especially the fact that millions of users, on a daily basis, express their opinions on products and services to blogs, wikis, so…
Improved ASR for English-isiZulu code-switched speech with semi-supervised training.
The study detects deceptive language in business communication using AI.
ACTRCE uses natural language to improve reinforcement learning performance.
The paper improves ESG taxonomy and classifies sentences as sustainable or unsustainable.
Hybrid and end-to-end models compare in syllable recognition.
In this work, a novel solution to the speaker identification problem is proposed through minimization of statistical divergences between the probability distribution (g). of feature vectors from the test utterance and the probability distributions of the feature vector corresponding to the speaker classes. This approac…
MIOpen optimizes deep learning operators for GPUs, accelerating research.
A new Twitter sentiment model predicts stock market trends with high accuracy.
Paper develops multilingual job classification for ISCO and KZiS.
In an effort to better understand meaning from natural language texts, we explore methods aimed at organizing lexical objects into contexts. A number of these methods for organization fall into a family defined by word ordering. Unlike demographic or spatial partitions of data, these collocation models are of special i…