13 citations · 63 across the 28 of their papers we have counts for
26 papers · 1 filter
Analyzing Pokémon and Mario Streamers' Twitch Chat with LLM-based User Embeddings
Mika Hämäläinen, Jack Rueter, Khalid Alnajjar
We present a novel digital humanities method for representing our Twitch chatters as user embeddings created by a large language model (LLM). We cluster these embeddings automatica…
Leveraging Transformer-Based Models for Predicting Inflection Classes of Words in an Endangered Sami Language
Khalid Alnajjar, Mika Hämäläinen, Jack Rueter
This paper presents a methodology for training a transformer-based model to classify lexical and morphosyntactic features of Skolt Sami, an endangered Uralic language characterized…
Sentiment Analysis Using Aligned Word Embeddings for Uralic Languages
Khalid Alnajjar, Mika Hämäläinen, Jack Rueter
In this paper, we present an approach for translating word embeddings from a majority language into 4 minority languages: Erzya, Moksha, Udmurt and Komi-Zyrian. Furthermore, we ali…
Modern French Poetry Generation with RoBERTa and GPT-2
Mika Hämäläinen, Khalid Alnajjar, Thierry Poibeau
We present a novel neural model for modern poetry generation in French. The model consists of two pretrained neural models that are fine-tuned for the poem generation task. The enc…
Emotion Conditioned Creative Dialog Generation
Khalid Alnajjar, Mika Hämäläinen
We present a DialGPT based model for generating creative dialog responses that are conditioned based on one of the following emotions: anger, disgust, fear, happiness, pain, sadnes…
Automatic Generation of Factual News Headlines in Finnish
Maximilian Koppatz, Khalid Alnajjar, Mika Hämäläinen +1
We present a novel approach to generating news headlines in Finnish for a given news story. We model this as a summarization task where a model is given a news article, and its tas…
Video Games as a Corpus: Sentiment Analysis using Fallout New Vegas Dialog
Mika Hämäläinen, Khalid Alnajjar, Thierry Poibeau
We present a method for extracting a multilingual sentiment annotated dialog data set from Fallout New Vegas. The game developers have preannotated every line of dialog in the game…
When to Laugh and How Hard? A Multimodal Approach to Detecting Humor and its Intensity
Khalid Alnajjar, Mika Hämäläinen, Jörg Tiedemann +2
Prerecorded laughter accompanying dialog in comedy TV shows encourages the audience to laugh by clearly marking humorous moments in the show. We present an approach for automatical…
Multilingual Persuasion Detection: Video Games as an Invaluable Data Source for NLP
Teemu Pöyhönen, Mika Hämäläinen, Khalid Alnajjar
Role-playing games (RPGs) have a considerable amount of text in video game dialogues. Quite often this text is semi-annotated by the game developers. In this paper, we extract a mu…
Harnessing Multilingual Resources to Question Answering in Arabic
Khalid Alnajjar, Mika Hämäläinen
The goal of the paper is to predict answers to questions given a passage of Qur'an. The answers are always found in the passage, so the task of the model is to predict where an ans…
Processing M.A. Castrén's Materials: Multilingual Typed and Handwritten Manuscripts
Niko Partanen, Jack Rueter, Mika Hämäläinen +1
The study forms a technical report of various tasks that have been performed on the materials collected and published by Finnish ethnographer and linguist, Matthias Alexander Castr…
TFW2V: An Enhanced Document Similarity Method for the Morphologically Rich Finnish Language
Quan Duong, Mika Hämäläinen, Khalid Alnajjar
Measuring the semantic similarity of different texts has many important applications in Digital Humanities research such as information retrieval, document clustering and text summ…
Detecting Depression in Thai Blog Posts: a Dataset and a Baseline
Mika Hämäläinen, Pattama Patpong, Khalid Alnajjar +2
We present the first openly available corpus for detecting depression in Thai. Our corpus is compiled by expert verified cases of depression in several online blogs. We experiment…
Finnish Dialect Identification: The Effect of Audio and Text
Mika Hämäläinen, Khalid Alnajjar, Niko Partanen +1
Finnish is a language with multiple dialects that not only differ from each other in terms of accent (pronunciation) but also in terms of morphological forms and lexical choice. We…
The Current State of Finnish NLP
Mika Hämäläinen, Khalid Alnajjar
There are a lot of tools and resources available for processing Finnish. In this paper, we survey recent papers focusing on Finnish NLP related to many different subcategories of N…
When a Computer Cracks a Joke: Automated Generation of Humorous Headlines
Khalid Alnajjar, Mika Hämäläinen
Automated news generation has become a major interest for new agencies in the past. Oftentimes headlines for such automatically generated news articles are unimaginative as they ha…
How Cute is Pikachu? Gathering and Ranking Pokémon Properties from Data with Pokémon Word Embeddings
Mika Hämäläinen, Khalid Alnajjar, Niko Partanen
We present different methods for obtaining descriptive properties automatically for the 151 original Pokémon. We train several different word embeddings models on a crawled Pokémon…
Human Evaluation of Creative NLG Systems: An Interdisciplinary Survey on Recent Papers
Mika Hämäläinen, Khalid Alnajjar
We survey human evaluation in papers presenting work on creative natural language generation that have been published in INLG 2020 and ICCC 2020. The most typical human evaluation…
Lemmatization of Historical Old Literary Finnish Texts in Modern Orthography
Mika Hämäläinen, Niko Partanen, Khalid Alnajjar
Texts written in Old Literary Finnish represent the first literary work ever written in Finnish starting from the 16th century. There have been several projects in Finland that hav…
Never guess what I heard... Rumor Detection in Finnish News: a Dataset and a Baseline
Mika Hämäläinen, Khalid Alnajjar, Niko Partanen +1
This study presents a new dataset on rumor detection in Finnish language news headlines. We have evaluated two different LSTM based models and two different BERT models, and have f…
Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered
Mika Hämäläinen, Niko Partanen, Jack Rueter +1
We train neural models for morphological analysis, generation and lemmatization for morphologically rich languages. We present a method for automatically extracting substantially l…
!Qué maravilla! Multimodal Sarcasm Detection in Spanish: a Dataset and a Baseline
Khalid Alnajjar, Mika Hämäläinen
We construct the first ever multimodal sarcasm dataset for Spanish. The audiovisual dataset consists of sarcasm annotated text that is aligned with video and audio. The dataset rep…
The Great Misalignment Problem in Human Evaluation of NLP Methods
Mika Hämäläinen, Khalid Alnajjar
We outline the Great Misalignment Problem in natural language processing research, this means simply that the problem definition is not in line with the method proposed and the hum…
When Word Embeddings Become Endangered
Khalid Alnajjar
Big languages such as English and Finnish have many natural language processing (NLP) resources and models, but this is not the case for low-resourced and endangered languages as s…