CS 224 N : Language Dynamics analysis through Word 2 Vec Embeddings

Recent breakthroughs in word representation methods have generated a new spark of enthusiasm amidst the computational linguistic community, with methods such as Word2Vec have indeed shown huge potential to compress insightful information on words’ contextual meaning in lowdimensional vectors. While the success of these representations has mainly been harvested for traditional NLP tasks such as word prediction or sentiment analysis, recent studies have begun using these representations to track the dynamics of language and meaning over time. However, recent works have also shown these embeddings to be extremely noisy and training-set dependent, thus considerably restricting the scope and significance of this potential application. In this project, building upon the work presented by  in 2015, we thus propose to investigate ways of defining interpretable embeddings, and as well as alternative ways of assessing the dynamics of semantic changes so as to endow more statistical power to the analysis.  The recent success of Neural-Network-generated word embeddings (word2vec, Glove, etc.) for traditional NLP tasks such as word prediction or text sentiment analysis has motivated the scientific community to use these representations as a way to analyze language itself. Indeed, if these low-dimensional word representations have proven to successfully carry both semantic and syntactic information, such a successful information compression could thus potentially be harvested to tackle more complex linguistic problems, such as monitoring language dynamics over time or space. In particular, in , , and [7], word embeddings are used to capture drifts of word meanings over time through the analysis of the temporal evolution of any given word’ closest neighbors. Other studies  use them to relate semantic shifts to geographical considerations. However, as highlighted by Hahn and Hellrich in , the inherent randomness of the methods used to encode these representations results in the high variability of any given word’s closest neighbors, thus considerably narrowing the statistical power of the study: how can we detect real semantic changes from the ambient jittering inherent to the embeddings’ representations? Can we try to provide a perhaps more interpretable and sounder basis of comparison than the neighborhoods to detect these changes? Building upon the methodology developed by Hamilton and al  to study language dynamics and the observations made by Hahn and Hellrich , we propose to tackle this problem from a mesoscopic scale: the intuition would be that if local neighborhoods are too unstable, we should thus look at information contained in the overall embedding matrix to build our statistical framework. In particular, a first idea is that we should try to evaluate the existence of a potentially ”backbone” structure of the embeddings. Indeed, it would seem intuitive that if certain words –such as “gay” or “asylum” (as observed by Hamilton et al) have exhibited important drifts in meaning throughout the 20th century, another large set of words – such as “food”,“house” or “people” – have undergone very little semantic change over time. As such, we should expect the relative distance between atoms in this latter set (as defined by the acute angle between their respective embeddings) to remain relatively constant from decade to decade. Hence, one could try to use this stable backbone graph as a way to triangulate the movement of the other word vectors over time, thus hopefully inducing more interpretable changes over time. Such an approach could also be used to answer the question of assessing the validity of our embeddings for linguistic purposes: how well do these embeddings capture similarity and nuances between words? A generally