updated FAQ

app.py

@@ -104,7 +104,7 @@ st.markdown(
 with st.sidebar:
     st.image('images/AGALMA_logo_v2.png')
     # st.markdown('# ἄγαλμα | AGALMA')
-    selected = option_menu('ἄγαλμα | AGALMA', ["App", "About", "FAQ", "License"],
+    selected = option_menu('ἄγαλμα | AGALMA', ["App", "About", "FAQ", "Subcorpora", "License"],
                            menu_icon="menu", default_index=0, orientation="vertical", styles=styles_vertical)
     
 if selected == "App":
@@ -404,22 +404,168 @@ if selected == "FAQ":
     
     
 
-    with st.expander(r"$\textsf{\Large Which models is this interface based on?}$"):
+    with st.expander(r"$\textsf{\Large What is this interface based on?}$"):
         st.write(
-                "This interface is based on five language models. \
-                Language models are statistical models of language, \
-                which store statistical information about word co-occurrence during the training phase. \
-                During training they process a corpus of texts in the target language(s). \
-                Once trained, models can be used to extract information about the language \
-                (in this interface, we focus on the extraction of semantic information) or to perform specific linguistic tasks. \
-                The models on which this interface is based are Word Embedding models."
+                "This interface is based on language models. Language models are probability distributions of \
+                words or word sequences, which store statistical information about word co-occurrences. \
+                This happens during the training phase, in which models process a corpus of texts in the \
+                target language(s). Once trained, linguistic information can be extracted from the models, or \
+                the models can be used to perform specific linguistic tasks. In this interface, we focus on the \
+                extraction of semantic information. To that end, we created five models, corresponding to five \
+                time slices. The models on which this interface is based are so-called Word Embedding \
+                models (the specific architecture is called Word2Vec)."
                 )
         
+    with st.expander(r"$\textsf{\Large What are Word Embeddings?}$"):
+        st.write(
+            "Word Embeddings are representations of words obtained via language modelling. More in \
+            detail, they are strings of numbers (called *vectors*) produced by a language model to \
+            represent each word in the training corpus in a multi-dimensional space. Words that are more \
+            similar in meaning will be closer to one another in this vector space (or semantic space) than \
+            words that are less similar in meaning. The term *word embeddings* is often used as a \
+            synonym of *predict models*, a type of language models introduced by Mikolov *et al.* (2013) \
+            with the Word2Vec architecture. This interface is built upon Word2Vec models."
+        )
+        
     with st.expander(r"$\textsf{\Large Which corpus was used to train the models?}$"):
+        st.markdown('''
+            The five models on which this interface is based were trained on five diachronic slices of the \
+            Diorisis Ancient Greek Corpus, which is ‘a digital collection of ancient Greek texts (from \
+            Homer to the early fifth century AD) compiled for linguistic analyses’ (Vatri & McGillivray \
+            2018: 55). The Diorisis corpus contains a subset of the texts that can be found in the \
+            Thesaurus Linguae Graecae. More information about the works and authors included in each \
+            subcorpus is [here] '''
+        )
+        
+    with st.expander(r"$\textsf{\Large How was the corpus divided into time slices?}$"):
         st.write(
-            "The five models on which this interface is based were trained on five slices of the Diorisis Ancient Greek Corpus (Vatri & McGillivray 2018)."
-        ) 
- 
+            "The texts in the corpus were divided according to chronology. We tried to strike a balance \
+            between respecting the traditional divisions of Ancient Greek literature into periods and \
+            having slices of a more or less comparable size. The division is the following: \
+            \
+            Archaic: beginning-500 BCE; Classical: 499-324 BCE; Hellenistic: 323-0 BCE, Early Roman: \
+            1-250 CE; Late Roman: 251-500 CE."
+        )
+    
+    with st.expander(r"$\textsf{\Large Which are the theoretical assumptions behind distributional semantic models, such as Word Embeddings?}$"):
+        st.write(
+            "Computational semantics is based on the Distributional Hypothesis. According to this \
+            hypothesis, words used in similar lexical contexts (contexts of words surrounding them) will \
+            have a similar meaning. This hypothesis was famously summarized by J.R. Firth as ‘you \
+            shall know a word by the company it keeps’ (1957: xx). Phrased differently, this \
+            means that two words that occur in similar lexical contexts are probably semantically \
+            related. The words that occur in the most similar lexical contexts are referred to as \
+            nearest neighbours. This does not necessarily mean, though, that these words even \
+            occur together. A detailed introduction to distributional semantics can be found in the book \
+            *Distributional Semantics* (Lenci & Sahlgren 2023: 3-25)."
+        )
+        
+    with st.expander(r"$\textsf{\Large What are the nearest neighbours?}$"):
+        st.write(
+            "Word vectors can be used as coordinates to represent words in a geometric space, called \
+            *semantic space*. Words with similar vectors, occurring in similar contexts, are closer in the \
+            space. The nearest neighbours to a word are the closest words to it in the semantic space. \
+            Words close in the space are not necessarily synonyms, they are rather in a relationship of \
+            semantic relatedness, i.e. they belong to the same semantic area. An example of neighbours \
+            in the space could be: *star – moon – sun – cloud – plane – fly – blue*."
+        )
+        
+    with st.expander(r"$\textsf{\Large Are the nearest neighbours the same as concordances?}$"):
+        st.write(
+            "No. The nearest neighbours to a target word do not necessarily occur together with it in the \
+            same context, but each of them will be found in similar lexical contexts. For example, my \
+            colleague Pete and I may often go to the same type of conferences and meet the same \
+            group of people there, but it is quite possible that Pete and I never go to the same \
+            conference at the same time. Pete and I are similar, but not necessarily spending time \
+            together. The extraction of the nearest neighbours with word embeddings is thus different \
+            from finding concordances. The nearest neighbours cannot be extracted manually with close- \
+            reading methods."
+        )
+        
+    with st.expander(r"$\textsf{\Large Which framework and parameters were used to train the models?}$"):
+        st.write(
+            "The Word2vec models were trained by using the CADE framework (Bianchi *et al.* 2020), a \
+            technique which does not require space alignment, i.e. word embeddings trained on different \
+            corpus slices are directly comparable. CADE was used with the following parameters: \
+            size=30, siter=5, diter=5, workers=4, sg=0, ns=20. The chosen architecture was the \
+            Continuous-Bag-of-Words. The context that is taken into account for each word are the 5 \
+            words before, and the 5 words after the target word."
+        )
+        
+    with st.expander(r"$\textsf{\Large What is the cosine similarity value?}$"):
+        st.write(
+            "The cosine similarity is a measure of the distance between two words in the semantic space. \
+            More precisely, the cosine similarity is the cosine of angle between the two vectors in the \
+            multi-dimensional space. The value ranges from -1 to 1. The higher the value of the cosine \
+            similarity (the closer it is to 1), the closer two words are in the semantic space. For example, \
+            according to our model, the cosine similarity value of πατήρ and μήτηρ in the Classical period \
+            is 0.93, relatively high as we might expected for these obviously related words, while the \
+            cosine similarity value of a random pair like πατήρ and τράπεζα in the same time slice is \
+            0.12, considerably lower."
+        )
+   
+    with st.expander(r"$\textsf{\Large What are the 3D representations?}$"):
+        st.write(
+           "The 3D representation is a way to graphically visualize the semantic space, the method used \
+            on this website is called t-SNE. Semantic spaces are multi-dimensional, with as many \
+            dimensions as the digits in the vectors. The embeddings used for this interface only have 30 \
+            dimensions. A 3D representation reduces the dimensions to 3, to allow for graphic \
+            representation. Even if 3D representations are effective means of making a semantic space \
+            visible, **they are not 100% accurate**, since the visualization shows a reduction of the 30 \
+            dimensions. We thus advise not to base any conclusions on the graphic representation only, \
+            but to rely on nearest neighbours extraction and on cosine similarity."
+       )
+        
+    with st.expander(r"$\textsf{\Large Is the information stored by Word Embeddings reliable?}$"):
+        st.write(
+            "The information stored in word embeddings is solely based on the training corpus. This \
+            means that our models have no additional knowledge of the Ancient Greek language and \
+            culture. All information extracted from a model thus reflect word co-occurrences, and word \
+            meaning, in its specific training corpus. \
+            \
+            Please take into account that the results for words occurring very rarely may be inaccurate. \
+            Language modelling works on a statistical basis, so that a word with only few occurrences \
+            may not provide enough evidence to obtain reliable results. But it has been observed that an \
+            extremely high word frequency can also affect the results. It often happens that the nearest \
+            neighbours to words occurring very often are other high-frequency words, such as stop \
+            words (e.g., prepositions, articles, particles). "
+        )
+        
+    with st.expander(r"$\textsf{\Large What if I obtain 'strange' results?}$"):
+        st.write(
+            "For the abovementioned reasons mentioned, word embeddings are not always reliable \
+            methods of semantic investigation. Interpretation of the results is always needed to decide \
+            whether the results at hand are real patterns present in the corpus, and could thus reveal \
+            interesting phenomena, or just noise present in the data."
+        )
+   
+    with st.expander(r"$\textsf{\Large How can word embeddings help us study semantic change?}$"):
+        st.write(
+            "Cosine similarity can be computed between vectors of the same word in different time slices. \
+            The higher the cosine similarity, the more similar the usage of a word is in the two considered \
+            time slices. If the cosine similarity between a word’s vectors in two consecutive time slices is \
+            particularly low, there is a chance that semantic change happened at that point in time. The \
+            analysis of the nearest neighbours to the target word in the two slices can help clarifying if \
+            change actually happened, and which is its direction."
+        )
+    
+    st.markdown("""
+    ## References
+    
+    Bianchi, F., Di Carlo, V., Nicoli, P., & Palmonari, M. (2020). Compass-aligned distributional
+    embeddings for studying semantic differences across corpora. *arXiv preprint
+    arXiv:2004.06519*.
+    
+    Lenci, A., & Sahlgren, M. (2023). *Distributional semantics*. Cambridge University Press.
+    
+    Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word
+    representations in vector space. *arXiv preprint arXiv:1301.3781*.
+
+    Vatri, A., & McGillivray, B. (2018). The Diorisis ancient Greek corpus: Linguistics and
+    literature. *Research Data Journal for the Humanities and Social Sciences*, 3(1), 55-65.
+    """)
+        
+    
    
 if selected == "License":
     st.markdown("""